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Extending with support for arbitrary IFC schemas is possible at compile-time when using C++ and at run-time when using Python.  In addition to a C++ and Python API, IfcOpenShell comes with an ecosystem of tools, notably including IfcConvert (an application to convert IFC models to other formats), the BlenderBIM Add-on (an add-on to Blender providing a graphical IFC authoring platform), and many other libraries, CLI apps, and more. Support is also provided for auxiliary standards such as BCF and IDS.  For more information, see:  * [IfcOpenShell Website](http://ifcopenshell.org) * [IfcOpenShell Documentation](http://bonsaibim.org/docs-python) * [IfcOpenShell C++ Installation](https://bonsaibim.org/docs-python/ifcopenshell/installation.html) * [IfcOpenShell Python Installation](https://bonsaibim.org/docs-python/ifcopenshell-python/installation.html) * [IfcOpenShell Python Hello World Tutorial](https://bonsaibim.org/docs-python/ifcopenshell-python/hello_world.html) * [Bonsai Website](https://bonsaibim.org) * [Bonsai Documentation](http://bonsaibim.org/docs) * [Add-on Installation](https://bonsaibim.org/docs/users/installation.html) * [Exploring an IFC model](https://bonsaibim.org/docs/users/exploring_an_ifc_model.html)  <table> <thead> <tr> <th>Service</th> <th>Status</th> </tr> </thead> <tbody> <tr> <td>Anaconda Daily Build</td> <td><a href=\"https://anaconda.org/ifcopenshell/ifcopenshell\"><img src=\"https://img.shields.io/conda/vn/ifcopenshell/ifcopenshell\" alt=\"Anaconda-Server Badge\"></a></td> </tr> <tr> <td>Anaconda v0.8.0 Stable</td> <td><a href=\"https://anaconda.org/conda-forge/ifcopenshell\"><img src=\"https://img.shields.io/conda/vn/conda-forge/ifcopenshell\" alt=\"Anaconda-Server Badge\"></a></td> </tr> <tr> <td>PyPi Daily Build</td> <td><a href=\"https://pypi.org/project/ifcopenshell/\"><img src=\"https://img.shields.io/pypi/v/ifcopenshell\" alt=\"PyPi Badge\"></a></td> </tr> <tr> <td>ArchLinux AUR Package Stable</td> <td><a href=\"https://aur.archlinux.org/packages/ifcopenshell\"><img src=\"https://img.shields.io/aur/version/ifcopenshell\" alt=\"AUR Badge\"></a></td> </tr> <tr> <td>ArchLinux AUR Package git</td> <td><a href=\"https://aur.archlinux.org/packages/ifcopenshell-git\"><img src=\"https://img.shields.io/aur/version/ifcopenshell-git\" alt=\"AUR Badge\"></a></td> </tr> <tr> <td>BlenderBIM Add-on Chocolatey (under moderation)</td> <td><a href=\"https://community.chocolatey.org/packages/blenderbim-nightly/\"><img src=\"https://img.shields.io/chocolatey/v/blenderbim-nightly\" alt=\"Chocolatey Badge\"></a></td> </tr> <tr> <td>Sponsor development on OpenCollective</td> <td><a href=\"https://opencollective.com/opensourcebim/\"><img src=\"https://opencollective.com/opensourcebim/tiers/badge.svg\" alt=\"Financial Contributors\"></a></td> </tr> <tr> <td>Docker hub</td> <td><a href=\"https://hub.docker.com/r/aecgeeks/ifcopenshell\"><img src=\"https://img.shields.io/docker/pulls/aecgeeks/ifcopenshell\" alt=\"Docker Pulls\"></a></td> </tr>  </tbody> </table> Contents --------  Those marked with an asterisk are part of IfcOpenShell. <table> <thead> <tr> <th>Name</th> <th>Description</th> <th>License</th> </tr> </thead> <tbody> <tr> <td>bcf</td> <td>Library to read and write BCF-XML and query OpenCDE BCF-API modules</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>blenderbim</td> <td>Add-on to Blender providing a graphical native IFC authoring platform</td> <td>GPL-3.0-or-later</td> </tr> <tr> <td>bsdd</td> <td>Library to query the bSDD API</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifc2ca</td> <td>Utility to convert IFC structural analysis models to Code_Aster</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifc4d</td> <td>Convert to and from IFC and project management software</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifc5d</td> <td>Report and optimise cost information from IFC</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcbimtester</td> <td>Wrapper for Gherkin based unit testing for IFC models</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcblender</td> <td>Historic Blender IFC import add-on</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifccityjson</td> <td>Convert CityJSON to IFC</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcclash</td> <td>Clash detection library and CLI app</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifccobie</td> <td>Extract IFC data for COBie handover requirements</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcconvert</td> <td>CLI app to convert IFC to many other formats</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifccsv</td> <td>Library and CLI app to export and import schedules from IFC</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcdiff</td> <td>Compare changes between IFC models</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcfm</td> <td>Extract IFC data for FM handover requirements</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcgeom</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcgeom_schema_agnostic</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcgeomserver</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcjni</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcmax</td> <td>Historic extension for IFC support in 3DS Max</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcopenshell-python</td> <td>Python library for IFC 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One of the three components of the DOCKSTRING bundle (package, dataset, and benchmark tasks).","text_prefix":false,"timestamp":1778704681,"version":"0.3.4"},"doclang":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"<p align=\"center\">   <a href=\"https://github.com/doclang-project/doclang\">     <img loading=\"lazy\" alt=\"DocLang\" src=\"https://github.com/doclang-project/doclang/raw/main/resources/logo.png\" width=\"30%\"/>   </a> </p>  # DocLang  [![PyPI version](https://img.shields.io/pypi/v/doclang)](https://pypi.org/project/doclang/) ![Python](https://img.shields.io/badge/python-3.10%20%7C%20%203.11%20%7C%203.12%20%7C%203.13%20%7C%203.14-blue) [![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![Checked with mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](https://mypy-lang.org/) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit) [![License Apache 2.0](https://img.shields.io/github/license/doclang-project/doclang)](https://opensource.org/licenses/Apache-2.0)  **[DocLang](https://www.doclang.ai/) is the AI-native markup format for unstructured content** \u2014 including documents, images, and more. It maps cleanly to LLM tokens while preserving structure, semantics, layout, and geometry in a single, unambiguous representation.  This repository is the home of the normative specification and the reference toolkit for DocLang. If you build with LLMs and VLMs on real-world content, this is where the standard lives.  ## Specification  The source of the specification is available in [spec.md](https://github.com/doclang-project/doclang/blob/main/spec.md) and exports to different formats can be found in the [exports/](https://github.com/doclang-project/doclang/tree/main/exports) directory.  ## Reference Toolkit  The commands below illustrate basic scenarios. For advanced installation and usage options (minimal install, platform notes, custom Schematron backends, Python API), see the [toolkit README](https://github.com/doclang-project/doclang/blob/main/doclang/README.md).  ### Installation  ```bash pip install \"doclang[schematron-saxon]\" ```  ### Validation  ```bash doclang validate -n my_document.dclg ```  ### Packaging  ```bash doclang pack my_document.dclg ```  ## Citation  If you use DocLang in academic or technical work, please cite the specification:  ```bibtex @misc{doclang_2026,   title        = {DocLang: Universal AI Document Format},   author       = {{DocLang Project}},   year         = {2026},   version      = {main},   howpublished = {\\url{https://github.com/doclang-project/doclang}}, } ```  ## Development  To work on this repository \u2014 setup, tests, reference generation, releases \u2014 see [CONTRIBUTING.md](https://github.com/doclang-project/doclang/blob/main/CONTRIBUTING.md).  ## We \u2764\ufe0f Open Source AI  DocLang is developed in the open and supported by the [LF AI & Data Foundation](https://lfaidata.foundation/projects/). Learn more about the project at [doclang-project](https://github.com/doclang-project).  ## License  DocLang is licensed under the Apache License 2.0. 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Supported Python Versions ------------------------- Python >= 3.5  Deprecated Python Versions -------------------------- Python == 2.7 - Python 2.7 support will be removed on January 1, 2020. - protobuf does not support Visual C++ 2008, windows py27 package not available","dev_url":"https://github.com/googleapis/google-cloud-python/tree/master/api_core","doc_url":"https://googleapis.dev/python/google-api-core/latest/index.html","home":"https://github.com/googleapis/python-api-core","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-api-core/google-api-core-2.8.1.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Core Library for Google Client Libraries with grpcio-gcp","text_prefix":false,"timestamp":1608137690,"version":"2.8.1"},"google-api-python-client":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/google/google-api-python-client/","doc_url":"https://developers.google.com/api-client-library/python/start/get_started","home":"https://github.com/google/google-api-python-client/","license":"Apache-2.0 and MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://files.pythonhosted.org/packages/source/g/google-api-python-client/google_api_python_client-2.199.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Google API Client Library for Python","text_prefix":false,"timestamp":0,"version":"2.201.0"},"google-apitools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pypi.org/project/google-apitools/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-apitools/google-apitools-0.5.35.tar.gz","subdirs":["noarch"],"summary":"client libraries for humans","text_prefix":false,"timestamp":1588242669,"version":"0.5.35"},"google-apputils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"license":"Apache 2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.python.org/packages/source/g/google-apputils/google-apputils-0.4.2.tar.gz","subdirs":["linux-64","osx-64","win-32","win-64"],"text_prefix":false,"timestamp":0,"version":"0.4.2"},"google-auth":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"This library provides the ability to authenticate to Google APIs using various methods. It also provides integration with several HTTP libraries.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-auth","doc_url":"https://googleapis.dev/python/google-auth/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-auth","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-auth/google_auth-2.46.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Google authentication library for Python","text_prefix":false,"timestamp":0,"version":"2.62.0"},"google-auth-httplib2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"This library provides an httplib2 transport for google-auth. Note that httplib has lots of problems such as lack of threadsafety and insecure usage of TLS. Using it is highly discouraged. This library is intended to help existing users of oauth2client migrate to google-auth.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-auth-httplib2","doc_url":"https://google-auth.readthedocs.io/en/latest/","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-auth-httplib2","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-auth-httplib2/google_auth_httplib2-0.3.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Google Authentication Library httplib2 transport","text_prefix":false,"timestamp":0,"version":"0.4.4"},"google-auth-oauthlib":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"This library provides oauthlib integration with google-auth.","dev_url":"https://github.com/googleapis/google-auth-library-python-oauthlib","doc_url":"http://google-auth-oauthlib.readthedocs.io/en/latest/","home":"https://github.com/googleapis/google-auth-library-python-oauthlib","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://files.pythonhosted.org/packages/source/g/google-auth-oauthlib/google_auth_oauthlib-1.4.1.tar.gz","subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Google Authentication Library, oauthlib integration with google-auth","text_prefix":true,"timestamp":0,"version":"1.5.0"},"google-cloud-aiplatform":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/python-aiplatform","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-aiplatform/google_cloud_aiplatform-1.132.0.tar.gz","subdirs":["linux-64","noarch","osx-64","osx-arm64","win-64"],"summary":"Vertex AI API client library","text_prefix":true,"timestamp":1662693888,"version":"2.5.0"},"google-cloud-alloydb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-alloydb","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-alloydb","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-alloydb/google_cloud_alloydb-0.6.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Alloydb API client library","text_prefix":false,"timestamp":1774962174,"version":"0.11.4"},"google-cloud-appengine-logging":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-appengine-logging","doc_url":"https://github.com/googleapis/google-cloud-python/blob/main/packages/google-cloud-appengine-logging/README.rst","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-appengine-logging","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-appengine-logging/google_cloud_appengine_logging-1.7.0.tar.gz","subdirs":["noarch"],"summary":"Python Client for Google Cloud Appengine Logging","text_prefix":false,"timestamp":1660632537,"version":"1.11.0"},"google-cloud-artifact-registry":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Artifact Registry provides a single place for your organization to manage container images and language packages (such as Maven and npm). It is fully integrated with Google Cloud\u2019s tooling and runtimes and comes with support for native artifact protocols. 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Supported Python Versions ------------------------- Python >= 3.5  Deprecated Python Versions -------------------------- Python == 2.7  - Python 2.7 support will be removed on January 1, 2020. - protobuf does not support Visual C++ 2008, windows py27 package not available","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigquery","doc_url":"https://googleapis.dev/python/bigquery/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigquery","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-bigquery/google_cloud_bigquery-3.40.1.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"BigQuery API client library","text_prefix":false,"timestamp":1576179244,"version":"3.46.1"},"google-cloud-bigquery-datatransfer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"BigQuery Data Transfer API is a Google Cloud service that lets client applications transfer data from partner SaaS applications to Google BigQuery on a scheduled, managed basis. See the [quick start guide](https://googleapis.dev/python/bigquerydatatransfer/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigquery-datatransfer","doc_url":"https://googleapis.dev/python/bigquerydatatransfer/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigquery-datatransfer","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-bigquery-datatransfer/google_cloud_bigquery_datatransfer-3.20.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Bigquery Datatransfer API client library","text_prefix":true,"timestamp":1644943879,"version":"3.24.0"},"google-cloud-bigquery-storage":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"google-cloud-bigquery-storage installs google-cloud-bigquery-storage-core and the extra requirements for parsing table data. Avro and Arrow data formats are supported.  Supported Python Versions ------------------------- Python >= 3.8  Deprecated Python Versions -------------------------- Python == 2.7, Python <= 3.7.  The last version of this library compatible with Python 2.7 and 3.5 is google-cloud-bigquery-storage==1.1.0.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigquery-storage","doc_url":"https://cloud.google.com/python/docs/reference/bigquerystorage/latest","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigquery-storage","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://files.pythonhosted.org/packages/source/g/google-cloud-bigquery-storage/google_cloud_bigquery_storage-2.41.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Batteries-included library for BigQuery Storage API","text_prefix":false,"timestamp":1551943712,"version":"2.41.0"},"google-cloud-bigquery-storage-core":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"google-cloud-bigquery-storage-core the core client library for connecting to the BigQuery Storage API.  Supported Python Versions ------------------------- Python >= 3.8  Deprecated Python Versions -------------------------- Python == 2.7, Python <= 3.6.  The last version of this library compatible with Python 2.7 and 3.5 is google-cloud-bigquery-storage==1.1.0.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigquery-storage","doc_url":"https://cloud.google.com/python/docs/reference/bigquerystorage/latest","home":"https://github.com/googleapis/python-bigquery-storage","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://files.pythonhosted.org/packages/source/g/google-cloud-bigquery-storage/google_cloud_bigquery_storage-2.41.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Core library for BigQuery Storage API","text_prefix":true,"timestamp":1601485574,"version":"2.41.0"},"google-cloud-bigtable":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Python Client for Google Cloud Bigtable -------------------------  [Google Cloud Bigtable](https://cloud.google.com/bigtable) is Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.  - [Client Library Documentation](https://googleapis.github.io/google-cloud-python/latest/bigtable/usage.html) - [Product Documentation](https://cloud.google.com/bigtable/docs)  Quick Start -----------  In order to use this library, you first need to go through the following steps:  1. [Select or create a Cloud Platform project.](https://console.cloud.google.com/project) 2. [Enable billing for your project.](https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project) 3. [Enable the Cloud Bigtable API.](https://cloud.google.com/bigtable) 4. [Setup Authentication.](https://googleapis.github.io/google-cloud-python/latest/core/auth.html)  Supported Python Versions ------------------------- Python >= 3.7  Deprecated Python Versions ------------------------- * Python 2.7: the last released version which supported Python 2.7 was version 1.7.0, released 2021-02-09. * Python 3.5: the last released version which supported Python 3.5 was version 1.7.0, released 2021-02-09. * Python 3.6: the last released version which supported Python 3.6 was version v2.10.1, released 2022-06-03.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigtable","doc_url":"https://github.com/googleapis/google-cloud-python/blob/main/packages/google-cloud-bigtable/README.rst","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-bigtable","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-bigtable/google_cloud_bigtable-2.35.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Python Client for Google Cloud Bigtable","text_prefix":true,"timestamp":1556536347,"version":"2.50.1"},"google-cloud-billing":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-billing","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"Google Cloud Billing API client library","text_prefix":false,"timestamp":1790945835,"version":"1.21.0"},"google-cloud-billing-budgets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-billing-budgets","doc_url":"https://googleapis.dev/python/billingbudgets/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-billing-budgets","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-billing-budgets/google_cloud_billing_budgets-1.17.2.tar.gz","subdirs":["noarch"],"summary":"Python Client for Google Cloud Billing Budget","text_prefix":true,"timestamp":1664582471,"version":"1.17.2"},"google-cloud-build":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Build API is a Google Cloud service that lets client applications create and manage builds on Google Cloud Platform. Cloud Build lets you build software quickly across all languages. Get complete control over defining custom workflows for building, testing, and deploying across multiple environments:: - such as VMs, serverless, Kubernetes, or Firebase. See the [quick start guide](https://googleapis.dev/python/cloudbuild/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-build","doc_url":"https://googleapis.dev/python/cloudbuild/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-build","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-build/google_cloud_build-3.34.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Build API client library","text_prefix":true,"timestamp":1634147832,"version":"3.41.0"},"google-cloud-compute":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-compute","doc_url":"https://googleapis.dev/python/compute/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-compute","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-compute/google_cloud_compute-1.48.0.tar.gz","subdirs":["noarch"],"summary":"Python Client for Google Cloud Compute Engine","text_prefix":false,"timestamp":1639456992,"version":"1.56.0"},"google-cloud-container":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"**Python Client for Kubernetes Engine**  Kubernetes Engine: The Google Kubernetes Engine API is used for building and managing container based applications, powered by the open source Kubernetes technology.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-container","doc_url":"https://googleapis.dev/python/container/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-container","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-container/google_cloud_container-2.61.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Google Cloud Container API client library","text_prefix":true,"timestamp":1556429552,"version":"2.67.0"},"google-cloud-containeranalysis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Container Analysis API client library is an implementation of the Grafeas API, which stores, and enables querying and retrieval of critical metadata about all of your software artifacts. See the [quick start guide](https://googleapis.dev/python/containeranalysis/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/python-containeranalysis","doc_url":"https://googleapis.dev/python/containeranalysis/latest/index.html","home":"https://github.com/googleapis/python-containeranalysis","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-cloud-containeranalysis/google_cloud_containeranalysis-2.14.5.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Container Analysis API client library","text_prefix":true,"timestamp":1651786243,"version":"2.14.5"},"google-cloud-core":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"This library is not meant to stand-alone. 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You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.  - [Client Library Documentation](https://googleapis.dev/python/language/latest/index.html) - [Product Documentation](https://cloud.google.com/language/docs)  **note**      This library currently does not run on Google App Engine Standard.     We are actively working on adding this support.  Quick Start -----------  In order to use this library, you first need to go through the following steps:  1. [Select or create a Cloud Platform project.](https://console.cloud.google.com/project) 2. [Enable billing for your project.](https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project) 3. [Enable the Google Cloud Language API.](https://cloud.google.com/natural-language) 4. [Setup Authentication.](https://googleapis.github.io/google-cloud-python/latest/core/auth.html)  Supported Python Versions ----------- Python >= 3.6","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-language","doc_url":"https://cloud.google.com/python/docs/reference/language/latest","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-language","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-language/google_cloud_language-2.18.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Google Cloud Language API client library","text_prefix":true,"timestamp":1556535682,"version":"2.22.0"},"google-cloud-life-sciences":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-life-sciences","doc_url":"https://googleapis.dev/python/lifesciences/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-life-sciences","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-life-sciences/google_cloud_life_sciences-0.9.15.tar.gz","subdirs":["noarch"],"summary":"Python Client for Google Cloud Life Sciences","text_prefix":false,"timestamp":1654656288,"version":"0.9.15"},"google-cloud-logging":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The Stackdriver Logging API writes log entries and manages your Stackdriver Logging configuration. See the [usage guide](https://googleapis.dev/python/logging/latest/usage.html).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-logging","doc_url":"https://docs.cloud.google.com/logging/docs","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-logging","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-logging/google_cloud_logging-3.13.0.tar.gz","subdirs":["noarch"],"summary":"Stackdriver Logging API client library","text_prefix":false,"timestamp":1665449146,"version":"3.17.0"},"google-cloud-managedkafka":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-managedkafka","doc_url":"https://github.com/googleapis/google-cloud-python/blob/main/packages/google-cloud-managedkafka/README.rst","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-managedkafka","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-managedkafka/google_cloud_managedkafka-0.2.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Managedkafka API client library","text_prefix":false,"timestamp":1790941143,"version":"0.4.2"},"google-cloud-memcache":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Cloud Memorystore for Memached API: Ship performant and highly available key value store, compatible with OSS Memcached protocol.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-memcache","doc_url":"https://googleapis.dev/python/memcache/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-memcache","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-memcache/google_cloud_memcache-1.13.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Memcache API client library","text_prefix":false,"timestamp":1665459829,"version":"1.17.0"},"google-cloud-modelarmor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Python Client for Model Armor.  Model Armor is a Google Cloud service that screens prompts and responses for safety, security, and prompt-injection risks. This package is the generated GAPIC client library for the Model Armor API.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-modelarmor","doc_url":"https://cloud.google.com/security-command-center/docs/model-armor-overview","home":"https://pypi.org/project/google-cloud-modelarmor/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"Google Cloud Modelarmor API client library","text_prefix":false,"timestamp":1790915268,"version":"0.7.2"},"google-cloud-monitoring":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Python idiomatic client for Google Stackdriver Monitoring","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-monitoring","doc_url":"https://googleapis.dev/python/monitoring/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-monitoring","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-monitoring/google_cloud_monitoring-2.28.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Google Cloud Monitoring API client library","text_prefix":true,"timestamp":0,"version":"2.31.0"},"google-cloud-monitoring-dashboards":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Monitoring Dashboards API is a Google Cloud service that lets client applications manage dashboard configurations used in the Stackdriver UI. See the [quick start guide](https://googleapis.dev/python/monitoring-dashboards/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/python-monitoring-dashboards","doc_url":"https://googleapis.dev/python/monitoring-dashboards/latest/index.html","home":"https://github.com/googleapis/python-monitoring-dashboards","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-cloud-monitoring-dashboards/google_cloud_monitoring_dashboards-2.15.3.tar.gz","subdirs":["noarch"],"summary":"Monitoring Dashboards API client library","text_prefix":true,"timestamp":1623895695,"version":"2.15.3"},"google-cloud-ndb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"This is an updated version of the ndb client library for use with Google Cloud Datastore. Client Library Documentation - https://googleapis.dev/python/python-ndb/latest Product Documentation - https://cloud.google.com/datastore/docs The original Python 2 version of ndb was designed specifically for the Google App Engine python27 runtime and can be found at https://github.com/GoogleCloudPlatform/datastore-ndb-python. This version of ndb is designed for the Google App Engine Python 3 runtime, and will run on other Python platforms as well.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-ndb","doc_url":"https://googleapis.dev/python/python-ndb/latest/","home":"https://pypi.org/project/google-cloud-ndb","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://files.pythonhosted.org/packages/source/g/google-cloud-ndb/google_cloud_ndb-2.7.1.tar.gz","subdirs":["noarch"],"summary":"NDB library for Google Cloud Datastore","text_prefix":false,"timestamp":1790897689,"version":"2.7.1"},"google-cloud-orchestration-airflow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-orchestration-airflow","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-orchestration-airflow/google_cloud_orchestration_airflow-1.18.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Orchestration Airflow API client library","text_prefix":false,"timestamp":1645422819,"version":"1.23.0"},"google-cloud-org-policy":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python","doc_url":"https://cloud.google.com/python/docs/reference/orgpolicy/latest","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-org-policy","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"Google Cloud Org Policy API client library","text_prefix":false,"timestamp":1792061935,"version":"1.19.0"},"google-cloud-os-config":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Python client library for the Google Cloud OS Config API, which lets you manage OS patch management, inventory, and configuration of VM instances on Google Compute Engine.","dev_url":"https://github.com/googleapis/google-cloud-python","doc_url":"https://cloud.google.com/python/docs/reference/osconfig/latest","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-os-config","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"Google Cloud Os Config API client library","text_prefix":false,"timestamp":1791994419,"version":"1.26.0"},"google-cloud-os-login":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud OS Login API is a Google Cloud service that lets you manage SSH access to your instances using IAM without having to create and manage individual SSH keys. OS Login maintains a consistent Linux user identity across VM instances and is the recommended way to manage many users across multiple instances:: - or projects. See the [quick start guide](https://googleapis.dev/python/oslogin/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-os-login","doc_url":"https://googleapis.dev/python/oslogin/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-os-login","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-os-login/google_cloud_os_login-2.19.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Os Login API client library","text_prefix":true,"timestamp":1634088612,"version":"2.23.0"},"google-cloud-phishing-protection":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud\u2019s Phishing Protection countermeasure platform helps to detect phishing attacks against your users. Phishing Protection API enables you to submit URLs suspected to be unsafe. Any URLs that are confirmed to match the Safe Browsing Policies will be added to the Safe Browsing list, which is used by over three billion devices to show warnings when a user visits a known unsafe web resource. Common sources of these URLs are customer reports or internal phishing detection results. See the [quick start guide](https://googleapis.dev/python/phishingprotection/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/python-phishingprotection","doc_url":"https://googleapis.dev/python/phishingprotection/latest/index.html","home":"https://github.com/googleapis/python-phishingprotection","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-cloud-phishing-protection/google_cloud_phishing_protection-1.11.5.tar.gz","subdirs":["noarch"],"summary":"Phishing Protection API client library","text_prefix":true,"timestamp":1664854799,"version":"1.11.5"},"google-cloud-pubsub":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Pub / Sub is a fully-managed real-time messaging service that allows you to send and receive messages between independent applications. You can leverage Cloud Pub/Sub\u2019s flexibility to decouple systems and components hosted on Google Cloud Platform or elsewhere on the Internet. By building on the same technology Google uses, Cloud Pub / Sub is designed to provide \u201cat least once\u201d delivery at low latency with on-demand scalability to 1 million messages per second (and beyond).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-pubsub","doc_url":"https://docs.cloud.google.com/python/docs/reference/pubsub/latest","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-pubsub","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-pubsub/google_cloud_pubsub-2.34.0.tar.gz","subdirs":["noarch"],"summary":"Python Client for Google Cloud Pub / Sub","text_prefix":true,"timestamp":1658020496,"version":"2.43.0"},"google-cloud-pubsublite":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/python-pubsublite","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-pubsublite/google_cloud_pubsublite-1.13.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Pubsublite API client library","text_prefix":false,"timestamp":1663803848,"version":"1.13.0"},"google-cloud-recommendations-ai":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Cloud Recommendations AI API: Deliver highly personalized product recommendations at scale.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-recommendations-ai","doc_url":"https://googleapis.dev/python/recommendationengine/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-recommendations-ai","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-recommendations-ai/google_cloud_recommendations_ai-0.11.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Recommendations Ai API client library","text_prefix":false,"timestamp":1635216534,"version":"0.13.1"},"google-cloud-recommender":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Recommender API is a Google Cloud service that provides recommendations for cloud customers for various categories like performance optimization, cost savings, reliability, feature discovery, etc. These recommendations are generated automatically based on analysis of user resources, configuration and monitoring metrics. See the [quick start guide](https://googleapis.dev/python/recommender/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/python-recommender","doc_url":"https://googleapis.dev/python/recommender/latest/index.html","home":"https://github.com/googleapis/python-recommender","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-cloud-recommender/google_cloud_recommender-2.15.5.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Recommender API client library","text_prefix":true,"timestamp":1635185111,"version":"2.15.5"},"google-cloud-redis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Memorystore for Redis API is a Google Cloud service that lets client applications communicate with a fully managed Redis service for Google Cloud. Applications running on Google Cloud can achieve extreme performance by leveraging the highly scalable, available, secure Redis service without the:: - burden of managing complex Redis deployments. See the [quick start guide](https://googleapis.dev/python/redis/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-redis","doc_url":"https://googleapis.dev/python/redis/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-redis","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-redis/google_cloud_redis-2.19.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Redis API client library","text_prefix":true,"timestamp":1646106737,"version":"2.22.0"},"google-cloud-resource-manager":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Resource Manager API provides methods that you can use to programmatically manage your projects in the Google Cloud Platform. You can create, list, update or delete projects within Google Cloud Platform. See the [quick start guide](https://googleapis.dev/python/cloudresourcemanager/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-resource-manager","doc_url":"https://googleapis.dev/python/cloudresourcemanager/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-resource-manager","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-resource-manager/google_cloud_resource_manager-1.16.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Resource Manager API client library","text_prefix":false,"timestamp":1657814609,"version":"1.18.0"},"google-cloud-run":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Cloud Run API: is a managed compute platform that enables you to run containers that are invocable via requests or events.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-run","doc_url":"https://googleapis.dev/python/run/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-run","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-run/google_cloud_run-0.14.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Run API client library","text_prefix":false,"timestamp":1693307266,"version":"0.16.1"},"google-cloud-runtimeconfig":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The Google Cloud RuntimeConfig API enables developers to dynamically configure and expose variables through Google Cloud Platform. In addition, you can also set Watchers and Waiters that will watch for changes to your data and return based on certain conditions. See the [quick start guide](https://googleapis.dev/python/runtimeconfig/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-runtimeconfig","doc_url":"https://googleapis.dev/python/runtimeconfig/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-runtimeconfig","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-runtimeconfig/google_cloud_runtimeconfig-0.36.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud RuntimeConfig API client library","text_prefix":false,"timestamp":1588339837,"version":"0.37.2"},"google-cloud-scheduler":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-scheduler","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://files.pythonhosted.org/packages/source/g/google-cloud-scheduler/google_cloud_scheduler-2.21.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Scheduler API client library","text_prefix":true,"timestamp":1688130985,"version":"2.21.0"},"google-cloud-sdk":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cloud.google.com/sdk/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-sdk-589.0.0-darwin-arm.tar.gz","subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"Command-line interface for Google Cloud Platform products and services","text_prefix":true,"timestamp":1657690350,"version":"589.0.0"},"google-cloud-secret-manager":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-secret-manager","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-secret-manager/google_cloud_secret_manager-2.26.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Secret Manager API client library","text_prefix":true,"timestamp":1661375530,"version":"2.31.0"},"google-cloud-securitycenter":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Cloud Security Command Center API is a Google Cloud service that provides access to temporal views of assets and findings within an organization. See the [quick start guide](https://googleapis.dev/python/securitycenter/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/python-securitycenter","doc_url":"https://googleapis.dev/python/securitycenter/latest/index.html","home":"https://github.com/googleapis/python-securitycenter","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-cloud-securitycenter/google_cloud_securitycenter-1.34.0.tar.gz","subdirs":["noarch"],"summary":"Cloud Security Command Center API client library","text_prefix":true,"timestamp":1623893105,"version":"1.34.0"},"google-cloud-spanner":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Python Client for Cloud Spanner -------------------------  [Cloud Spanner]() is the world's first fully managed relational database service to offer both strong consistency and horizontal scalability for mission-critical online transaction processing (OLTP) applications. With Cloud Spanner you enjoy all the traditional benefits of a relational database; but unlike any other relational database service, Cloud Spanner scales horizontally to hundreds or thousands of servers to handle the biggest transactional workloads.  - [Client Library Documentation](https://googleapis.github.io/google-cloud-python/latest/spanner/index.html) - [Product Documentation](https://cloud.google.com/spanner/docs)  Quick Start -----------  In order to use this library, you first need to go through the following steps:  1. [Select or create a Cloud Platform project.](https://console.cloud.google.com/project) 2. [Enable billing for your project.](https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project) 3. [Enable the Google Cloud Language API.](https://cloud.google.com/natural-language) 4. [Setup Authentication.](https://googleapis.github.io/google-cloud-python/latest/core/auth.html)  Supported Python Versions ----------- Python >= 3.5  Deprecated Python Versions ----------- Python == 2.7. Python 2.7 support will be removed on January 1, 2020.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-spanner","doc_url":"https://googleapis.dev/python/spanner/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-spanner","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-spanner/google_cloud_spanner-3.62.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Google Cloud Spanner API client library","text_prefix":false,"timestamp":1558199024,"version":"3.71.0"},"google-cloud-speech":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-speech","doc_url":"https://googleapis.dev/python/speech/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-speech","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-speech/google_cloud_speech-2.35.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Speech API client library","text_prefix":true,"timestamp":1628625856,"version":"2.41.0"},"google-cloud-storage":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/blob/main/packages/google-cloud-storage","doc_url":"https://docs.cloud.google.com/python/docs/reference/storage/latest","home":"https://github.com/googleapis/google-cloud-python/blob/main/packages/google-cloud-storage","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-storage/google_cloud_storage-3.4.1.tar.gz","subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Python Client for Google Cloud Storage","text_prefix":false,"timestamp":1573747241,"version":"3.17.0"},"google-cloud-storage-control":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-storage-control","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-storage-control/google_cloud_storage_control-1.8.0.tar.gz","subdirs":["linux-64","noarch","osx-64","osx-arm64","win-64"],"summary":"Google Cloud Storage Control API client library","text_prefix":false,"timestamp":1766178407,"version":"1.16.0"},"google-cloud-storage-transfer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-storage-transfer","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-storage-transfer/google_cloud_storage_transfer-1.18.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Storage Transfer API client library","text_prefix":false,"timestamp":1694762002,"version":"1.22.0"},"google-cloud-talent":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Cloud Talent Solution API provides the capability to create, read, update, and delete job postings, as well as search jobs based on keywords and filters. See the [quick start guide](https://googleapis.dev/python/talent/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/python-talent","doc_url":"https://googleapis.dev/python/talent/latest/index.html","home":"https://github.com/googleapis/python-talent","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-cloud-talent/google_cloud_talent-2.13.5.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Talent Solution API client library","text_prefix":true,"timestamp":1636485476,"version":"2.13.5"},"google-cloud-tasks":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-tasks","doc_url":"https://googleapis.dev/python/cloudtasks/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-tasks","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-tasks/google_cloud_tasks-2.20.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Tasks API client library","text_prefix":true,"timestamp":1614115395,"version":"2.26.0"},"google-cloud-texttospeech":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Text-to-Speech API synthesizes natural-sounding speech by applying powerful neural network models. See the [quick start guide](https://googleapis.dev/python/texttospeech/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-texttospeech","doc_url":"https://cloud.google.com/python/docs/reference/texttospeech/latest","home":"https://github.com/googleapis/google-cloud-python","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-texttospeech/google_cloud_texttospeech-2.34.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Texttospeech API client library","text_prefix":false,"timestamp":1643905384,"version":"2.38.0"},"google-cloud-trace":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The Stackdriver Trace API sends application trace data to Stackdriver Trace for viewing. Trace data is collected for all App Engine applications by default. Trace data from other applications can be provided using this API. See the [quick start guide](https://googleapis.dev/python/cloudtrace/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-trace","doc_url":"https://cloud.google.com/python/docs/reference/cloudtrace/latest","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-trace","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-trace/google_cloud_trace-1.17.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Trace API client library","text_prefix":true,"timestamp":1632790447,"version":"1.21.0"},"google-cloud-translate":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Python Client for Google Cloud Translation -------------------------  With [Google Cloud Translation]( https://cloud.google.com/translate/), you can dynamically translate text between thousands of language pairs. The Google Cloud Translation API lets websites and programs integrate with Google Cloud Translation programmatically. Google Cloud Translation is available as a paid service. See the [Pricing](https://cloud.google.com/translate/pricing) and [FAQ](https://cloud.google.com/translate/faq) pages for details.  - [Client Library Documentation](https://googleapis.dev/python/translation/latest/index.html) - [Product Documentation](https://cloud.google.com/translate/docs)  Quick Start -----------  In order to use this library, you first need to go through the following steps:  1. [Select or create a Cloud Platform project.](https://console.cloud.google.com/project) 2. [Enable billing for your project.](https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project) 3. [Enable the Google Cloud Language API.](https://cloud.google.com/natural-language) 4. [Setup Authentication.](https://googleapis.github.io/google-cloud-python/latest/core/auth.html)  Supported Python Versions ----------- Python >= 3.7","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-translate","doc_url":"https://googleapis.dev/python/translation/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-translate","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-translate/google_cloud_translate-3.23.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Google Cloud Translate API client library","text_prefix":true,"timestamp":1558197233,"version":"3.28.0"},"google-cloud-vectorsearch":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Python Client for Vector Search.  google-cloud-vectorsearch provides Python client access to Google Cloud's Vector Search service, a managed vector database for search, recommendation, and AI applications.","dev_url":"https://github.com/googleapis/google-cloud-python","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-vectorsearch","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"Google Cloud Vectorsearch API client library","text_prefix":false,"timestamp":1790915117,"version":"0.11.5"},"google-cloud-videointelligence":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Video Intelligence API is a Google Cloud service that makes videos searchable, and discoverable, by extracting metadata with an easy to use API. You can now search every moment of every video file in your catalog and find every occurrence as well as its significance. It quickly annotates videos:: - stored in Google Cloud Storage, and helps you identify key nouns entities of your video, and when they occur within the video. Separate signal from noise, by retrieving relevant information at the video, shot or per frame. See the [quick start guide](https://googleapis.dev/python/videointelligence/latest/index.html#quick-start).","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-videointelligence","doc_url":"https://googleapis.dev/python/videointelligence/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-videointelligence","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-videointelligence/google_cloud_videointelligence-2.17.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Videointelligence API client library","text_prefix":true,"timestamp":1654610138,"version":"2.21.0"},"google-cloud-vision":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Python Client for Google Cloud Vision -------------------------  The [Google Cloud Vision](https://cloud.google.com/vision/) API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API. It quickly classifies images into thousands of categories (e.g., \"sailboat\", \"lion\", \"Eiffel Tower\"), detects individual objects and faces within images, and finds and reads printed words contained within images. You can build metadata on your image catalog, moderate offensive content, or enable new marketing scenarios through image sentiment analysis. Analyze images uploaded in the request or integrate with your image storage on Google Cloud Storage.  - [Client Library Documentation](https://googleapis.dev/python/vision/latest/index.html) - [Product Documentation](https://cloud.google.com/vision/reference/rest/)  Quick Start -----------  In order to use this library, you first need to go through the following steps:  1. [Select or create a Cloud Platform project.](https://console.cloud.google.com/project) 2. [Enable billing for your project.](https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project) 3. [Enable the Google Cloud Language API.](https://cloud.google.com/natural-language) 4. [Setup Authentication.](https://googleapis.github.io/google-cloud-python/latest/core/auth.html)  Supported Python Versions ----------- Python >= 3.7","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-vision","doc_url":"https://cloud.google.com/python/docs/reference/vision/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-vision","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-vision/google_cloud_vision-3.11.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Google Cloud Vision API client library","text_prefix":false,"timestamp":1573226532,"version":"3.16.0"},"google-cloud-webrisk":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Web Risk API is a Google Cloud service that lets client applications check URLs against Google's constantly updated lists of unsafe web resources. Unsafe web resources include social engineering sites such as phishing and deceptive sites and sites that host malware or unwanted software. With the Web Risk API, you can quickly identify known bad sites, warn users before they click infected links, and prevent users from posting links to known infected pages from your site. The Web Risk API includes data on more than a million unsafe URLs and stays up to date by examining billions of URLs each day. See the [quick start guide](https://cloud.google.com/web-risk/docs/quickstart).","dev_url":"https://github.com/googleapis/python-webrisk","doc_url":"https://googleapis.dev/python/webrisk/latest/index.html","home":"https://github.com/googleapis/python-webrisk","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-cloud-webrisk/google-cloud-webrisk-1.14.4.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Web Risk API client library","text_prefix":true,"timestamp":1623894050,"version":"1.14.4"},"google-cloud-websecurityscanner":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Google Cloud Web Security Scanner API scans your Compute and App Engine apps for common web vulnerabilities. [Quick start guide](https://googleapis.dev/python/websecurityscanner/latest/index.html#quick-start)","dev_url":"https://github.com/googleapis/python-websecurityscanner","doc_url":"https://googleapis.dev/python/websecurityscanner/latest/index.html","home":"https://github.com/googleapis/python-websecurityscanner","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-cloud-websecurityscanner/google-cloud-websecurityscanner-1.14.4.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Web Security Scanner API client library","text_prefix":true,"timestamp":1623866472,"version":"1.14.4"},"google-cloud-workflows":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Cloud Workflows API: Orchestrate and automateGoogle Cloud and HTTP-based API services with serverless workflows.","dev_url":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-workflows","doc_url":"https://googleapis.dev/python/workflows/latest/index.html","home":"https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-workflows","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/g/google-cloud-workflows/google_cloud_workflows-1.19.0.tar.gz","subdirs":["noarch"],"summary":"Google Cloud Workflows API client library","text_prefix":false,"timestamp":1633035525,"version":"1.24.0"},"google-colab":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Colaboratory is a research project created to help disseminate machine learning education and research. It\u2019s a Jupyter notebook environment that requires no setup to use. For more information, see our FAQ.","dev_url":"https://github.com/googlecolab/colabtools","doc_url":"https://colab.research.google.com/","home":"https://colab.research.google.com/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.io/packages/source/g/google-colab/google-colab-1.0.0.tar.gz","subdirs":["noarch"],"summary":"Python libraries for Google 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interact with your Google Drive","text_prefix":false,"timestamp":1648577603,"version":"0.5.1"},"google-genai":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/googleapis/python-genai","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"GenAI Python SDK","text_prefix":false,"timestamp":1781872981,"version":"2.31.0"},"google-generativeai":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/google/generative-ai-python","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/google-gemini/generative-ai-python/archive/v0.8.4.tar.gz","subdirs":["noarch"],"summary":"The Gemini API Python SDK enables developers to use Google's state-of-the-art generative AI models to build AI-powered features and 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[![PyPi](https://img.shields.io/pypi/v/hklpy2_solvers.svg)](https://pypi.python.org/pypi/hklpy2_solvers) [![Conda Package](https://img.shields.io/badge/package-hklpy2_solvers-green.svg)](https://anaconda.org/channels/conda-forge/packages/hklpy2_solvers/overview) [![Conda Version](https://anaconda.org/conda-forge/hklpy2_solvers/badges/version.svg)](https://anaconda.org/channels/conda-forge/packages/hklpy2_solvers/overview) [![Conda Downloads](https://anaconda.org/conda-forge/hklpy2_solvers/badges/downloads.svg)](https://anaconda.org/channels/conda-forge/packages/hklpy2_solvers/overview) [![Conda Platforms](https://anaconda.org/conda-forge/hklpy2_solvers/badges/platforms.svg)](https://anaconda.org/channels/conda-forge/packages/hklpy2_solvers/overview) [![Coverage Status](https://coveralls.io/repos/github/prjemian/hklpy2_solvers/badge.svg?branch=main)](https://coveralls.io/github/prjemian/hklpy2_solvers?branch=main) [![Documentation](https://img.shields.io/badge/docs-blue)](https://prjemian.github.io/hklpy2_solvers/latest/) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/prjemian/hklpy2_solvers) [![License: ANL](https://img.shields.io/badge/license-ANL-brightgreen)](LICENSE)  Solvers for the [hklpy2](https://github.com/prjemian/hklpy2) package.  <table>     <tr>         <td>             <b>Solvers</b>             <p>                 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/geometries.html\">Geometries</a>                 \u00b7 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/guide_ad_hoc.html\">ad_hoc</a>                 \u00b7 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/guide_diffcalc.html\">diffcalc</a>             </p>         </td>         <td>             <b>User Guide</b>             <p>                 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/guides.html\">Guides</a>                 \u00b7 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/install.html\">Installation</a>                 \u00b7 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/api/hklpy2_solvers/\">API</a>                 \u00b7 <a href=\"https://prjemian.github.io/hklpy2_solvers\">documentation</a>                 \u00b7 <a href=\"https://github.com/prjemian/hklpy2_solvers\">source</a>                 \u00b7 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/release_notes.html\">Releases</a>                 \u00b7 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/license.html\">License</a>                 \u00b7 <a href=\"https://prjemian.github.io/hklpy2_solvers/latest/genindex.html\">Index</a>             </p>         </td>     </tr> 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Complete parsing support is provided for [IFC2x3 TC1], [IFC4 Add2 TC1], IFC4x1, IFC4x3, and IFC4x3. Extensive geometric support is implemented for the IFC releases [IFC2x3 TC1] and [IFC4 Add2 TC1]. Extending with support for arbitrary IFC schemas is possible at compile-time when using C++ and at run-time when using Python.  In addition to a C++ and Python API, IfcOpenShell comes with an ecosystem of tools, notably including IfcConvert (an application to convert IFC models to other formats), the BlenderBIM Add-on (an add-on to Blender providing a graphical IFC authoring platform), and many other libraries, CLI apps, and more. Support is also provided for auxiliary standards such as BCF and IDS.  For more information, see:  * [IfcOpenShell Website](http://ifcopenshell.org) * [IfcOpenShell Documentation](http://bonsaibim.org/docs-python) * [IfcOpenShell C++ Installation](https://bonsaibim.org/docs-python/ifcopenshell/installation.html) * [IfcOpenShell Python Installation](https://bonsaibim.org/docs-python/ifcopenshell-python/installation.html) * [IfcOpenShell Python Hello World Tutorial](https://bonsaibim.org/docs-python/ifcopenshell-python/hello_world.html) * [Bonsai Website](https://bonsaibim.org) * [Bonsai Documentation](http://bonsaibim.org/docs) * [Add-on Installation](https://bonsaibim.org/docs/users/installation.html) * [Exploring an IFC model](https://bonsaibim.org/docs/users/exploring_an_ifc_model.html)  <table> <thead> <tr> <th>Service</th> <th>Status</th> </tr> </thead> <tbody> <tr> <td>Anaconda Daily Build</td> <td><a href=\"https://anaconda.org/ifcopenshell/ifcopenshell\"><img src=\"https://img.shields.io/conda/vn/ifcopenshell/ifcopenshell\" alt=\"Anaconda-Server Badge\"></a></td> </tr> <tr> <td>Anaconda v0.8.0 Stable</td> <td><a href=\"https://anaconda.org/conda-forge/ifcopenshell\"><img src=\"https://img.shields.io/conda/vn/conda-forge/ifcopenshell\" alt=\"Anaconda-Server Badge\"></a></td> </tr> <tr> <td>PyPi Daily Build</td> <td><a href=\"https://pypi.org/project/ifcopenshell/\"><img src=\"https://img.shields.io/pypi/v/ifcopenshell\" alt=\"PyPi Badge\"></a></td> </tr> <tr> <td>ArchLinux AUR Package Stable</td> <td><a href=\"https://aur.archlinux.org/packages/ifcopenshell\"><img src=\"https://img.shields.io/aur/version/ifcopenshell\" alt=\"AUR Badge\"></a></td> </tr> <tr> <td>ArchLinux AUR Package git</td> <td><a href=\"https://aur.archlinux.org/packages/ifcopenshell-git\"><img src=\"https://img.shields.io/aur/version/ifcopenshell-git\" alt=\"AUR Badge\"></a></td> </tr> <tr> <td>BlenderBIM Add-on Chocolatey (under moderation)</td> <td><a href=\"https://community.chocolatey.org/packages/blenderbim-nightly/\"><img src=\"https://img.shields.io/chocolatey/v/blenderbim-nightly\" alt=\"Chocolatey Badge\"></a></td> </tr> <tr> <td>Sponsor development on OpenCollective</td> <td><a href=\"https://opencollective.com/opensourcebim/\"><img src=\"https://opencollective.com/opensourcebim/tiers/badge.svg\" alt=\"Financial Contributors\"></a></td> </tr> <tr> <td>Docker hub</td> <td><a href=\"https://hub.docker.com/r/aecgeeks/ifcopenshell\"><img src=\"https://img.shields.io/docker/pulls/aecgeeks/ifcopenshell\" alt=\"Docker Pulls\"></a></td> </tr>  </tbody> </table> Contents --------  Those marked with an asterisk are part of IfcOpenShell. <table> <thead> <tr> <th>Name</th> <th>Description</th> <th>License</th> </tr> </thead> <tbody> <tr> <td>bcf</td> <td>Library to read and write BCF-XML and query OpenCDE BCF-API modules</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>blenderbim</td> <td>Add-on to Blender providing a graphical native IFC authoring platform</td> <td>GPL-3.0-or-later</td> </tr> <tr> <td>bsdd</td> <td>Library to query the bSDD API</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifc2ca</td> <td>Utility to convert IFC structural analysis models to Code_Aster</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifc4d</td> <td>Convert to and from IFC and project management software</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifc5d</td> <td>Report and optimise cost information from IFC</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcbimtester</td> <td>Wrapper for Gherkin based unit testing for IFC models</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcblender</td> <td>Historic Blender IFC import add-on</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifccityjson</td> <td>Convert CityJSON to IFC</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcclash</td> <td>Clash detection library and CLI app</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifccobie</td> <td>Extract IFC data for COBie handover requirements</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcconvert</td> <td>CLI app to convert IFC to many other formats</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifccsv</td> <td>Library and CLI app to export and import schedules from IFC</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcdiff</td> <td>Compare changes between IFC models</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcfm</td> <td>Extract IFC data for FM handover requirements</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcgeom</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcgeom_schema_agnostic</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcgeomserver</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcjni</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcmax</td> <td>Historic extension for IFC support in 3DS Max</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcopenshell-python</td> <td>Python library for IFC manipulation</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcparse</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>ifcpatch</td> <td>Utility to run pre-packaged scripts to manipulate IFCs</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcsverchok</td> <td>Blender Add-on for visual node programming with IFC</td> <td>GPL-3.0-or-later</td> </tr> <tr> <td>ifctester</td> <td>Library, CLI and webapp for IDS model auditing</td> <td>LGPL-3.0-or-later</td> </tr> <tr> <td>ifcwrap</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>qtviewer</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> <tr> <td>serializers</td> <td>Internal library for IfcOpenShell</td> <td>LGPL-3.0-or-later*</td> </tr> </tbody> </table>  [LGPL]: https://github.com/IfcOpenShell/IfcOpenShell/tree/master/COPYING.LESSER \"LGPL-3.0-or-later\" [IFC]: https://technical.buildingsmart.org/standards/ifc/ \"IFC\" [IFC2x3 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For Linux users of `hackrf`, you will likely want to link the provided udev rule into your system installation in order for the hardware to have the correct permissions:      sudo ln -s $CONDA_PREFIX/lib/udev/rules.d/53-hackrf.rules /etc/udev/rules.d/     sudo udevadm control --reload     sudo udevadm trigger  Then, make sure your user account belongs to the plugdev group in order to be able to access your device:      sudo usermod -a -G plugdev <user>  You may have to restart for this change to take effect.","dev_url":"https://github.com/greatscottgadgets/hackrf","doc_url":"https://hackrf.readthedocs.io/en/latest/","home":"https://greatscottgadgets.com/hackrf","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/greatscottgadgets/hackrf/releases/download/v2026.01.3/hackrf-2026.01.3.tar.xz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Host software for HackRF, a low cost, open source software radio platform.","text_prefix":false,"timestamp":1643222183,"version":"2026.01.3"},"libhamlib4":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The Ham Radio Control Library (Hamlib, for short) is a project to provide programs with a consistent Application Programming Interface (API) for controlling the myriad of radios and rotators available to amateur radio and communications users. Many Amateur Radio Transceivers come with serial interfaces that allows software to control the radio. This project will endeavour to provide shared libraries that greatly simplify the application programmer's interaction with radio equipment and other controllable devices such as rotators, switches, etc. The Hamlib Wiki Supported Radios page, https://github.com/Hamlib/Hamlib/wiki/Supported-Radios, contains a snapshot of the supported radios at the time of the last Hamlib release.","dev_url":"https://github.com/Hamlib/Hamlib","doc_url":"https://github.com/Hamlib/Hamlib/wiki/Documentation","home":"http://www.hamlib.org/","license":"LGPL-2.1-or-later AND GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/Hamlib/Hamlib/releases/download/4.7.2/hamlib-4.7.2.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Ham radio control library for rigs, rotators, tuners, and amplifiers","text_prefix":false,"timestamp":1790802820,"version":"4.7.2"},"libharfbuzz":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"HarfBuzz is a text shaping library. 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HEIF and AVIF are new image file formats employing HEVC (h.265) or AV1 image coding, respectively, for the best compression ratios currently possible. libheif makes use of libde265 for HEIF image decoding and x265 for encoding. For AVIF, libaom, dav1d, svt-av1, or rav1e are used as codecs.","dev_url":"https://github.com/strukturag/libheif","home":"https://github.com/strukturag/libheif","license":"LGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"1.14.2":{"weak":["libheif >=1.14.2,<1.15.0a0"]},"1.15.1":{"weak":["libheif >=1.15.1,<1.16.0a0"]},"1.15.2":{"weak":["libheif >=1.15.2,<1.16.0a0"]},"1.16.1":{"weak":["libheif >=1.16.1,<1.17.0a0"]},"1.16.2":{"weak":["libheif >=1.16.2,<1.17.0a0"]},"1.17.0":{"weak":["libheif >=1.17.0,<1.18.0a0"]},"1.17.2":{"weak":["libheif >=1.17.2,<1.18.0a0"]},"1.17.3":{"weak":["libheif >=1.17.3,<1.18.0a0"]},"1.17.5":{"weak":["libheif >=1.17.5,<1.18.0a0"]},"1.17.6":{"weak":["libheif >=1.17.6,<1.18.0a0"]},"1.18.2":{"weak":["libheif >=1.18.2,<1.19.0a0"]},"1.19.5":{"weak":["libheif >=1.19.5,<1.20.0a0"]},"1.19.6":{"weak":["libheif >=1.19.6,<1.20.0a0"]},"1.19.7":{"weak":["libheif >=1.19.7,<1.20.0a0"]},"1.23.0":{"weak":["libheif >=1.23.0,<1.24.0a0"]},"1.23.1":{"weak":["libheif >=1.23.1,<1.24.0a0"]},"1.23.2":{"weak":["libheif >=1.23.2,<1.24.0a0"]},"1.23.3":{"weak":["libheif >=1.23.3,<1.24.0a0"]},"1.23.4":{"weak":["libheif >=1.23.4,<1.24.0a0"]},"1.23.5":{"weak":["libheif >=1.23.5,<1.24.0a0"]},"1.23.6":{"weak":["libheif >=1.23.6,<1.24.0a0"]}},"source_url":"https://github.com/strukturag/libheif/releases/download/v1.23.2/libheif-1.23.2.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"libheif is an HEIF and AVIF file format decoder and encoder","text_prefix":true,"timestamp":1791980788,"version":"1.23.6"},"libhidapi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"HIDAPI is a multi-platform library which allows an application to interface with USB and Bluetooth HID-Class devices on Windows, Linux, FreeBSD, and macOS.        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Keyboards, mice, and some other devices which are blacklisted from having hidraw nodes will not work. Fortunately, for nearly all the uses of hidraw, this is not a problem.   This back-end uses libusb-1.0 to communicate directly to a USB device. 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Mamba is part of a bigger ecosystem to make scientific packaging more sustainable. You can read our [announcement blog post](https://medium.com/@QuantStack/open-software-packaging-for-science-61cecee7fc23). The ecosystem also consists of `quetz`, an open source `conda` package server and `boa`, a fast `conda` package builder.  ### Installation  Please refer to the [`mamba` and `micromamba` installation guide](https://mamba.readthedocs.io/en/latest/installation.html) in the documentation.  ### Additional features in Mamba and Micromamba  `mamba` and `micromamba` come with features on top of stock `conda`.  ### `repoquery`  To efficiently query repositories and query package dependencies you can use `mamba repoquery` or `micromamba repoquery`. See the [repoquery documentation](https://mamba.readthedocs.io/en/latest/user_guide/mamba.html#repoquery) for details.  ### Installing lock files  `micromamba` can be used to install lock files generated by [conda-lock](https://conda-incubator.github.io/conda-lock/) without having to install `conda-lock`. Simply invoke e.g. `micromamba create -n my-env -f conda-lock.yml` with an environment lockfile named `*-lock.yml` or `*-lock.yaml`.","dev_url":"https://github.com/mamba-org/mamba","doc_url":"https://mamba.readthedocs.io/","home":"https://github.com/mamba-org/mamba","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.10.0":{"weak":["libmamba >=0.10.0,<0.11.0a0"]},"0.11.0":{"weak":["libmamba >=0.11.0,<0.12.0a0"]},"0.11.1":{"weak":["libmamba >=0.11.1,<0.12.0a0"]},"0.11.2":{"weak":["libmamba >=0.11.2,<0.12.0a0"]},"0.11.3":{"weak":["libmamba >=0.11.3,<0.12.0a0"]},"0.12.0":{"weak":["libmamba >=0.12.0,<0.13.0a0"]},"0.12.1":{"weak":["libmamba >=0.12.1,<0.13.0a0"]},"0.12.2":{"weak":["libmamba >=0.12.2,<0.13.0a0"]},"0.12.3":{"weak":["libmamba >=0.12.3,<0.13.0a0"]},"0.13.0":{"weak":["libmamba >=0.13.0,<0.14.0a0"]},"0.13.1":{"weak":["libmamba >=0.13.1,<0.14.0a0"]},"0.14.0":{"weak":["libmamba >=0.14.0,<0.15.0a0"]},"0.14.1":{"weak":["libmamba 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>=2.8.1,<2.9.0a0"]},"2.9.0":{"weak":["libmamba >=2.9.0,<2.10.0a0"]}},"source_url":"https://github.com/mamba-org/mamba/archive/refs/tags/2.2.0.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","linux-riscv64","osx-64","osx-arm64","win-64","win-arm64"],"summary":"A fast drop-in alternative to conda, using libsolv for dependency resolution","text_prefix":true,"timestamp":1792057402,"version":"2.9.0"},"libmamba-spdlog":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# Mamba, the Fast Cross-Platform Package Manager  [![Build Status](https://github.com/mamba-org/mamba/workflows/CI/badge.svg)](https://github.com/mamba-org/mamba/actions) [![Join the Gitter Chat](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/mamba-org/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![docs](https://readthedocs.org/projects/mamba/badge/?version=latest&style=flat)](https://mamba.readthedocs.io/en/latest)  `mamba` is a reimplementation of the conda package manager in C++.  - parallel downloading of repository data and package files using multi-threading - libsolv for much faster dependency solving, a state of the art library used in the RPM package manager of Red Hat, Fedora and OpenSUSE - core parts of `mamba` are implemented in C++ for maximum efficiency  At the same time, `mamba` utilizes the same command line parser, package installation and deinstallation code and transaction verification routines as `conda` to stay as compatible as possible.  Mamba is part of a bigger ecosystem to make scientific packaging more sustainable. You can read our [announcement blog post](https://medium.com/@QuantStack/open-software-packaging-for-science-61cecee7fc23). The ecosystem also consists of `quetz`, an open source `conda` package server and `boa`, a fast `conda` package builder.  ### Installation  Please refer to the [`mamba` and `micromamba` installation guide](https://mamba.readthedocs.io/en/latest/installation.html) in the documentation.  ### Additional features in Mamba and Micromamba  `mamba` and `micromamba` come with features on top of stock `conda`.  ### `repoquery`  To efficiently query repositories and query package dependencies you can use `mamba repoquery` or `micromamba repoquery`. See the [repoquery documentation](https://mamba.readthedocs.io/en/latest/user_guide/mamba.html#repoquery) for details.  ### Installing lock files  `micromamba` can be used to install lock files generated by [conda-lock](https://conda-incubator.github.io/conda-lock/) without having to install `conda-lock`. Simply invoke e.g. `micromamba create -n my-env -f conda-lock.yml` with an environment lockfile named `*-lock.yml` or `*-lock.yaml`.","dev_url":"https://github.com/mamba-org/mamba","doc_url":"https://mamba.readthedocs.io/","home":"https://github.com/mamba-org/mamba","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["linux-64","linux-aarch64","linux-ppc64le","linux-riscv64","osx-64","osx-arm64","win-64","win-arm64"],"summary":"A fast drop-in alternative to conda, using libsolv for dependency resolution","text_prefix":false,"timestamp":1792057402,"version":"2.9.0"},"libmambapy":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"# Mamba, the Fast Cross-Platform Package Manager  [![Build Status](https://github.com/mamba-org/mamba/workflows/CI/badge.svg)](https://github.com/mamba-org/mamba/actions) [![Join the Gitter Chat](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/mamba-org/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![docs](https://readthedocs.org/projects/mamba/badge/?version=latest&style=flat)](https://mamba.readthedocs.io/en/latest)  `mamba` is a reimplementation of the conda package manager in C++.  - parallel downloading of repository data and package files using multi-threading - libsolv for much faster dependency solving, a state of the art library used in the RPM package manager of Red Hat, Fedora and OpenSUSE - core parts of `mamba` are implemented in C++ for maximum efficiency  At the same time, `mamba` utilizes the same command line parser, package installation and deinstallation code and transaction verification routines as `conda` to stay as compatible as possible.  Mamba is part of a bigger ecosystem to make scientific packaging more sustainable. You can read our [announcement blog post](https://medium.com/@QuantStack/open-software-packaging-for-science-61cecee7fc23). The ecosystem also consists of `quetz`, an open source `conda` package server and `boa`, a fast `conda` package builder.  ### Installation  Please refer to the [`mamba` and `micromamba` installation guide](https://mamba.readthedocs.io/en/latest/installation.html) in the documentation.  ### Additional features in Mamba and Micromamba  `mamba` and `micromamba` come with features on top of stock `conda`.  ### `repoquery`  To efficiently query repositories and query package dependencies you can use `mamba repoquery` or `micromamba repoquery`. See the [repoquery documentation](https://mamba.readthedocs.io/en/latest/user_guide/mamba.html#repoquery) for details.  ### Installing lock files  `micromamba` can be used to install lock files generated by [conda-lock](https://conda-incubator.github.io/conda-lock/) without having to install `conda-lock`. Simply invoke e.g. `micromamba create -n my-env -f conda-lock.yml` with an environment lockfile named `*-lock.yml` or `*-lock.yaml`.","dev_url":"https://github.com/mamba-org/mamba","doc_url":"https://mamba.readthedocs.io/","home":"https://github.com/mamba-org/mamba","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.18.0":{"weak":["libmambapy >=0.18.0,<0.19.0a0"]},"0.18.1":{"weak":["libmambapy >=0.18.1,<0.19.0a0"]},"0.18.2":{"weak":["libmambapy >=0.18.2,<0.19.0a0"]},"0.19.0":{"weak":["libmambapy >=0.19.0,<0.20.0a0"]},"0.19.1":{"weak":["libmambapy >=0.19.1,<0.20.0a0"]},"0.20.0":{"weak":["libmambapy >=0.20.0,<0.21.0a0"]},"0.21.0":{"weak":["libmambapy >=0.21.0,<0.22.0a0"]},"0.21.1":{"weak":["libmambapy >=0.21.1,<0.22.0a0"]},"0.21.2":{"weak":["libmambapy >=0.21.2,<0.22.0a0"]},"0.22.0":{"weak":["libmambapy >=0.22.0,<0.23.0a0"]},"0.22.1":{"weak":["libmambapy >=0.22.1,<0.23.0a0"]},"0.23.0":{"weak":["libmambapy >=0.23.0,<0.24.0a0"]},"0.23.1":{"weak":["libmambapy >=0.23.1,<0.24.0a0"]},"0.23.3":{"weak":["libmambapy >=0.23.3,<0.24.0a0"]},"0.24.0":{"weak":["libmambapy >=0.24.0,<0.25.0a0"]},"0.25.0":{"weak":["libmambapy >=0.25.0,<0.26.0a0"]},"0.26.0":{"weak":["libmambapy >=0.26.0,<0.27.0a0"]},"0.27.0":{"weak":["libmambapy >=0.27.0,<0.28.0a0"]},"1.0.0":{"weak":["libmambapy >=1.0.0,<1.1.0a0"]},"1.1.0":{"weak":["libmambapy >=1.1.0,<1.2.0a0"]},"1.2.0":{"weak":["libmambapy >=1.2.0,<1.3.0a0"]},"1.3.0":{"weak":["libmambapy >=1.3.0,<1.4.0a0"]},"1.3.1":{"weak":["libmambapy >=1.3.1,<1.4.0a0"]},"1.4.0":{"weak":["libmambapy >=1.4.0,<1.5.0a0"]},"1.4.1":{"weak":["libmambapy >=1.4.1,<1.5.0a0"]},"1.4.2":{"weak":["libmambapy >=1.4.2,<1.5.0a0"]},"1.4.4":{"weak":["libmambapy >=1.4.4,<1.5.0a0"]},"1.4.5":{"weak":["libmambapy >=1.4.5,<1.5.0a0"]},"1.4.6":{"weak":["libmambapy >=1.4.6,<1.5.0a0"]},"1.4.7":{"weak":["libmambapy >=1.4.7,<1.5.0a0"]},"1.4.8":{"weak":["libmambapy >=1.4.8,<1.5.0a0"]},"1.4.9":{"weak":["libmambapy >=1.4.9,<1.5.0a0"]},"1.5.0":{"weak":["libmambapy >=1.5.0,<1.6.0a0"]},"1.5.1":{"weak":["libmambapy >=1.5.1,<1.6.0a0"]},"1.5.10":{"weak":["libmambapy >=1.5.10,<1.6.0a0"]},"1.5.11":{"weak":["libmambapy >=1.5.11,<1.6.0a0"]},"1.5.12":{"weak":["libmambapy >=1.5.12,<1.6.0a0"]},"1.5.2":{"weak":["libmambapy >=1.5.2,<1.6.0a0"]},"1.5.3":{"weak":["libmambapy >=1.5.3,<1.6.0a0"]},"1.5.4":{"weak":["libmambapy >=1.5.4,<1.6.0a0"]},"1.5.5":{"weak":["libmambapy >=1.5.5,<1.6.0a0"]},"1.5.6":{"weak":["libmambapy >=1.5.6,<1.6.0a0"]},"1.5.7":{"weak":["libmambapy >=1.5.7,<1.6.0a0"]},"1.5.8":{"weak":["libmambapy >=1.5.8,<1.6.0a0"]},"1.5.9":{"weak":["libmambapy >=1.5.9,<1.6.0a0"]},"2.0.0":{"weak":["libmambapy >=2.0.0,<2.1.0a0"]},"2.0.1":{"weak":["libmambapy >=2.0.1,<2.1.0a0"]},"2.0.2":{"weak":["libmambapy >=2.0.2,<2.1.0a0"]},"2.0.3":{"weak":["libmambapy >=2.0.3,<2.1.0a0"]},"2.0.4":{"weak":["libmambapy >=2.0.4,<2.1.0a0"]},"2.0.5":{"weak":["libmambapy >=2.0.5,<2.1.0a0"]},"2.0.6":{"weak":["libmambapy >=2.0.6,<2.1.0a0"]},"2.0.7":{"weak":["libmambapy >=2.0.7,<2.1.0a0"]},"2.0.8":{"weak":["libmambapy >=2.0.8,<2.1.0a0"]},"2.1.0":{"weak":["libmambapy >=2.1.0,<2.2.0a0"]},"2.1.1":{"weak":["libmambapy >=2.1.1,<2.2.0a0"]},"2.2.0":{"weak":["libmambapy >=2.2.0,<2.3.0a0"]},"2.2.0.post0dev0":{"weak":["libmambapy >=2.2.0.post0dev0,<2.3.0a0"]},"2.3.0":{"weak":["libmambapy >=2.3.0,<2.4.0a0"]},"2.3.1":{"weak":["libmambapy >=2.3.1,<2.4.0a0"]},"2.3.2":{"weak":["libmambapy >=2.3.2,<2.4.0a0"]},"2.3.3":{"weak":["libmambapy >=2.3.3,<2.4.0a0"]},"2.4.0":{"weak":["libmambapy >=2.4.0,<2.5.0a0"]},"2.5.0":{"weak":["libmambapy >=2.5.0,<2.6.0a0"]},"2.6.0":{"weak":["libmambapy >=2.6.0,<2.7.0a0"]},"2.6.1":{"weak":["libmambapy >=2.6.1,<2.7.0a0"]},"2.6.2":{"weak":["libmambapy >=2.6.2,<2.7.0a0"]},"2.7.0":{"weak":["libmambapy >=2.7.0,<2.8.0a0"]},"2.8.0":{"weak":["libmambapy >=2.8.0,<2.9.0a0"]},"2.8.1":{"weak":["libmambapy >=2.8.1,<2.9.0a0"]},"2.9.0":{"weak":["libmambapy >=2.9.0,<2.10.0a0"]}},"source_url":"https://github.com/mamba-org/mamba/archive/refs/tags/2.2.0.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","linux-riscv64","osx-64","osx-arm64","win-64","win-arm64"],"summary":"A fast drop-in alternative to conda, using libsolv for dependency resolution","text_prefix":false,"timestamp":1792057402,"version":"2.9.0"},"libmambapy-stubs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"# Mamba, the Fast Cross-Platform Package Manager  [![Build Status](https://github.com/mamba-org/mamba/workflows/CI/badge.svg)](https://github.com/mamba-org/mamba/actions) [![Join the Gitter Chat](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/mamba-org/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![docs](https://readthedocs.org/projects/mamba/badge/?version=latest&style=flat)](https://mamba.readthedocs.io/en/latest)  `mamba` is a reimplementation of the conda package manager in C++.  - parallel downloading of repository data and package files using multi-threading - libsolv for much faster dependency solving, a state of the art library used in the RPM package manager of Red Hat, Fedora and OpenSUSE - core parts of `mamba` are implemented in C++ for maximum efficiency  At the same time, `mamba` utilizes the same command line parser, package installation and deinstallation code and transaction verification routines as `conda` to stay as compatible as possible.  Mamba is part of a bigger ecosystem to make scientific packaging more sustainable. You can read our [announcement blog post](https://medium.com/@QuantStack/open-software-packaging-for-science-61cecee7fc23). The ecosystem also consists of `quetz`, an open source `conda` package server and `boa`, a fast `conda` package builder.  ### Installation  Please refer to the [`mamba` and `micromamba` installation guide](https://mamba.readthedocs.io/en/latest/installation.html) in the documentation.  ### Additional features in Mamba and Micromamba  `mamba` and `micromamba` come with features on top of stock `conda`.  ### `repoquery`  To efficiently query repositories and query package dependencies you can use `mamba repoquery` or `micromamba repoquery`. See the [repoquery documentation](https://mamba.readthedocs.io/en/latest/user_guide/mamba.html#repoquery) for details.  ### Installing lock files  `micromamba` can be used to install lock files generated by [conda-lock](https://conda-incubator.github.io/conda-lock/) without having to install `conda-lock`. Simply invoke e.g. `micromamba create -n my-env -f conda-lock.yml` with an environment lockfile named `*-lock.yml` or `*-lock.yaml`.","dev_url":"https://github.com/mamba-org/mamba","doc_url":"https://mamba.readthedocs.io/","home":"https://github.com/mamba-org/mamba","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"A fast drop-in alternative to conda, using libsolv for dependency resolution","text_prefix":false,"timestamp":1792057285,"version":"2.9.0"},"libmathdx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"This is a runtime package only. 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Status\r \r .. image:: https://pyup.io/repos/github/Hugovdberg/PIconnect/shield.svg\r     :target: https://pyup.io/repos/github/Hugovdberg/PIconnect/\r     :alt: Security Updates\r \r .. image:: https://api.codacy.com/project/badge/Grade/568734c85e07467c99e0e791d8eb17b6\r     :target: https://www.codacy.com/app/Hugovdberg/PIconnect?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=Hugovdberg/PIconnect&amp;utm_campaign=Badge_Grade\r     :alt: Automated code review\r \r .. image:: https://img.shields.io/badge/code%20style-black-000000.svg\r     :target: https://github.com/psf/black\r     :alt: Code style: black\r \r Python connector to OSIsoft PI SDK\r \r \r * Free software: MIT license\r * Documentation: https://piconnect.readthedocs.io.\r \r \r Features\r --------\r Features below are for both: PI Server and PIAF Database:\r \r * Extract recorded values\r * Extract interpolated values\r * Update a value\r * Summarize data before extraction with help of OSIsoft PI SDK\r * 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It automatically reads your ROS workspace's `package.xml` files and generates a `pixi.toml` manifest with all dependencies resolved from conda channels (primarily [robostack](https://robostack.org/)).  ## Why pixi-ros?  If you're a ROS developer, you're probably familiar with `rosdep` managing dependencies. `pixi-ros` gives you access to a more modern package management ecosystem:  - **Reproducible environments**: Lock files ensure everyone on your team has identical dependencies - **Cross-platform**: Works seamlessly on Linux, macOS, and Windows - **Fast and reliable**: Uses rattler (Rust implementation of conda) for speed - **No system dependencies**: Everything isolated in project environments  ## Quick Start  ### Installation  Install pixi first if you haven't already:  ```bash curl -fsSL https://pixi.sh/install.sh | bash ```  Or follow instructions at https://pixi.sh/latest/installation/  Install pixi-ros globally using pixi:  ```bash pixi global install pixi-ros ```  ### Initialize Your ROS Workspace  Navigate to your ROS workspace and run:  ```bash pixi ros init --distro humble ```  This will: 1. Discover all ROS packages in your workspace (by finding `package.xml` files) 2. Read dependencies from each `package.xml` 3. Map ROS package names to conda packages 4. Generate/update `pixi.toml` with proper channels and dependencies 5. Check package availability and warn about missing packages 6. Create helpful build/test/clean tasks  ## How It Works  ### Dependency Mapping  `pixi-ros` reads all dependency types from `package.xml` files.  It then does a best effort mapping of ROS package names to conda packages.  - **ROS packages**: `ros-{distro}-{package}` from robostack channels (e.g., `ros-humble-rclcpp`) - **System packages**: Mapped to conda-forge equivalents (e.g., `cmake`, `eigen`)  After the mapping, it validates package availability in the configured channels. This starts a connection with `https://prefix.dev` to check if packages exist.  ### Example  Given a `package.xml` with:  ```xml <depend>rclcpp</depend> <build_depend>ament_cmake</build_depend> <exec_depend>std_msgs</exec_depend> ```  `pixi ros init --distro humble` generates a `pixi.toml` with:  ```toml [dependencies] ros-humble-ament-cmake = \\\"*\\\" ros-humble-rclcpp = \\\"*\\\" ros-humble-std-msgs = \\\"*\\\" ```  ## Philosophy  `pixi-ros` aims to be a quick **gateway drug**. 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The package ada provides a straightforward,  well-documented, and broad boosting routine for classification, ideally suited  for small to moderate-sized data sets.","text_prefix":false,"timestamp":0,"version":"2.0_5.1"},"r-adabag":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=adabag","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/adabag/adabag_4.3.tar.gz","https://cran.r-project.org/src/contrib/adabag_4.3.tar.gz"],"subdirs":["noarch"],"summary":"It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. 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In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.","text_prefix":false,"timestamp":1774147752,"version":"2.2.1"},"r-adjustedcurves":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/RobinDenz1/adjustedCurves","home":"https://robindenz1.github.io/adjustedCurves/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/adjustedCurves/adjustedCurves_0.12.0.tar.gz","https://cloud.r-project.org/src/contrib/adjustedCurves_0.12.0.tar.gz"],"subdirs":["noarch"],"summary":"Estimate and plot confounder-adjusted survival curves using either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values', various forms of 'Inverse Probability of Treatment Weighting', two forms of 'Augmented Inverse Probability of Treatment Weighting', 'Empirical Likelihood Estimation' or 'Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times.  Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.","text_prefix":false,"timestamp":1791420914,"version":"0.12.0"},"r-adlift":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=adlift","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/adlift/adlift_1.4-6.tar.gz","https://cran.r-project.org/src/contrib/adlift_1.4-6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.","text_prefix":false,"timestamp":1667764529,"version":"1.4_6"},"r-admiral":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/pharmaverse/admiral","home":"https://pharmaverse.github.io/admiral/cran-release/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/admiral/admiral_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/admiral_1.5.0.tar.gz"],"subdirs":["noarch"],"summary":"A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). 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Additional utility helper functions to assist developers with maintaining documentation, testing and general upkeep of 'admiral' and 'admiral' extension packages.","text_prefix":false,"timestamp":1781475246,"version":"1.5.0"},"r-admisc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/dusadrian/admisc","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/admisc/admisc_0.41.tar.gz","https://cloud.r-project.org/src/contrib/admisc_0.41.tar.gz"],"subdirs":["linux-64","linux-aarch64","noarch","osx-64","osx-arm64","win-64"],"summary":"Contains functions used across packages 'DDIwR', 'QCA' and 'venn'. Interprets and translates, factorizes and negates SOP - Sum of Products expressions, for both binary and multi-value crisp sets, and extracts information (set names, set values) from those expressions. Other functions perform various other checks if possibly numeric (even if all numbers reside in a character vector) and coerce to numeric, or check if the numbers are whole. It also offers, among many others, a highly versatile recoding routine and some more flexible alternatives to the base functions 'with()' and 'within()'. SOP simplification functions in this package use related minimization from package 'QCA', which is recommended to be installed despite not being listed in the Imports field, due to circular dependency issues.","text_prefix":false,"timestamp":1788691284,"version":"0.41"},"r-admm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ADMM","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/ADMM_0.3.4.tar.gz","https://cran.r-project.org/src/contrib/Archive/ADMM/ADMM_0.3.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM). See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.","text_prefix":false,"timestamp":1758509393,"version":"0.3.4"},"r-aer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=AER","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/AER_1.2-17.tar.gz","https://cran.r-project.org/src/contrib/Archive/AER/AER_1.2-17.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette \"AER\" for a package overview.)","text_prefix":false,"timestamp":0,"version":"1.2_17"},"r-afex":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://afex.singmann.science/, https://github.com/singmann/afex","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/afex/afex_1.5-1.tar.gz","https://cran.r-project.org/src/contrib/afex_1.5-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot)  ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell  of the design. mixed() fits mixed models using lme4::lmer() and computes  p-values for all fixed effects using either Kenward-Roger or Satterthwaite  approximation for degrees of freedom (LMM only), parametric bootstrap  (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs).  afex_plot() provides a high-level interface for interaction or one-way  plots using ggplot2, combining raw data and model estimates. afex uses  type 3 sums of squares as default (imitating commercial statistical software).","text_prefix":false,"timestamp":1538253194,"version":"1.5_1"},"r-aggregation":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=aggregation","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/aggregation/aggregation_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/aggregation_1.0.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)].  Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), <doi:10.1101/190199>].","text_prefix":false,"timestamp":1533016499,"version":"1.0.2"},"r-agricolae":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://tarwi.lamolina.edu.pe/~fmendiburu","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/agricolae/agricolae_1.3-7.tar.gz","https://cran.r-project.org/src/contrib/agricolae_1.3-7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Original idea was presented in the thesis \"A statistical analysis tool for agricultural research\" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.","text_prefix":false,"timestamp":0,"version":"1.3_7"},"r-agtboost":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=agtboost","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/agtboost/agtboost_0.9.3.tar.gz","https://cran.r-project.org/src/contrib/agtboost_0.9.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fast and automatic gradient tree boosting designed to avoid manual tuning and cross-validation by utilizing an information theoretic approach. This makes the algorithm adaptive to the dataset at hand; it is completely automatic, and with minimal worries of overfitting. Consequently, the speed-ups relative to state-of-the-art implementations can be in the thousands while mathematical and technical knowledge required on the user are minimized.","text_prefix":false,"timestamp":1758699910,"version":"0.9.3"},"r-ahocorasicktrie":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/chambm/AhoCorasickTrie","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/AhoCorasickTrie_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/Archive/AhoCorasickTrie/AhoCorasickTrie_0.1.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Aho-Corasick is an optimal algorithm for finding many keywords in a text. It can locate all matches in a text in O(N+M) time; i.e., the time needed scales linearly with the number of keywords (N) and the size of the text (M). Compare this to the naive approach which takes O(N*M) time to loop through each pattern and scan for it in the text. This implementation builds the trie (the generic name of the data structure) and runs the search in a single function call. If you want to search multiple texts with the same trie, the function will take a list or vector of texts and return a list of matches to each text. By default, all 128 ASCII characters are allowed in both the keywords and the text. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. UTF-8 (Unicode) matching is not currently supported.","text_prefix":false,"timestamp":1588943953,"version":"0.1.3"},"r-aiccmodavg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=AICcmodavg","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/AICcmodavg_2.3-4.tar.gz","https://cran.r-project.org/src/contrib/Archive/AICcmodavg/AICcmodavg_2.3-4.tar.gz"],"subdirs":["noarch"],"summary":"Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes.  The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes.  The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities.  Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes.  Functions also implement model selection using BIC.  Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.","text_prefix":false,"timestamp":1589401205,"version":"2.3_4"},"r-airr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://docs.airr-community.org","license":"CC-BY-4.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/airr/airr_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/airr_2.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Schema definitions and read, write and validation tools for data  formatted in accordance with the AIRR Data Representation schemas defined  by the AIRR Community <http://docs.airr-community.org>.","text_prefix":false,"timestamp":1661679710,"version":"2.0.0"},"r-akima":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=akima","license":"ACM (Restricts use)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/akima/akima_0.6-3.6.tar.gz","https://cran.r-project.org/src/contrib/akima_0.6-3.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids.","text_prefix":false,"timestamp":1650358199,"version":"0.6_3.6"},"r-akmbiclust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=akmbiclust","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/akmbiclust/akmbiclust_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/akmbiclust_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) <arXiv:2009.04550>.","text_prefix":false,"timestamp":1758683522,"version":"0.1.0"},"r-alabama":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=alabama","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/alabama/alabama_2025.1.0.tar.gz","https://cran.r-project.org/src/contrib/alabama_2025.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.","text_prefix":false,"timestamp":1621935302,"version":"2025.1.0"},"r-alakazam":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://alakazam.readthedocs.org","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/alakazam/alakazam_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/alakazam_1.0.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides methods for high-throughput adaptive immune  receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In  particular, immunoglobulin (Ig) sequence lineage reconstruction,  lineage topology analysis, diversity profiling, amino acid property  analysis and gene usage. Citations:  Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>, Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.","text_prefix":false,"timestamp":1589326068,"version":"1.0.2"},"r-aleplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ALEPlot","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/ALEPlot_1.1.tar.gz","https://cran.r-project.org/src/contrib/Archive/ALEPlot/ALEPlot_1.1.tar.gz"],"subdirs":["noarch"],"summary":"Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.","text_prefix":false,"timestamp":1758667280,"version":"1.1"},"r-algdesign":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jvbraun/AlgDesign","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/AlgDesign_1.2.1.2.tar.gz","https://cran.r-project.org/src/contrib/Archive/AlgDesign/AlgDesign_1.2.1.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Algorithmic experimental designs. Calculates exact and approximate theory experimental designs for D,A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria.  The blocking can be done when whole and within plot factors interact.","text_prefix":false,"timestamp":1621758609,"version":"1.2.1.2"},"r-allcontributors":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://docs.ropensci.org/allcontributors/","home":"https://github.com/ropenscilabs/allcontributors","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/allcontributors/allcontributors_0.2.3.tar.gz","https://cran.r-project.org/src/contrib/allcontributors_0.2.3.tar.gz"],"subdirs":["noarch"],"summary":"Acknowledge all contributors to a project via a single function call. The function appends to a 'README' or other specified file(s) a table with names of all individuals who contributed via code or repository issues.  The package also includes several additional functions to extract and quantify contributions to any repository.","text_prefix":false,"timestamp":1775132343,"version":"0.2.3"},"r-allelicseries":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=AllelicSeries","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/AllelicSeries_0.1.1.5.tar.gz","https://cran.r-project.org/src/contrib/Archive/AllelicSeries/AllelicSeries_0.1.1.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2022) \"An allelic series rare variant association test for candidate gene discovery\" <doi:10.1101/2022.12.23.521658>.","text_prefix":false,"timestamp":1758671498,"version":"0.1.1.5"},"r-alluvial":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mbojan/alluvial","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/alluvial/alluvial_0.1-2.tar.gz","https://cran.r-project.org/src/contrib/alluvial_0.1-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Creating alluvial diagrams (also known as parallel sets plots) for multivariate and time series-like data.","text_prefix":false,"timestamp":0,"version":"0.1_2"},"r-alpaca":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/amrei-stammann/alpaca","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/alpaca/alpaca_0.3.5.tar.gz","https://cran.r-project.org/src/contrib/alpaca_0.3.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arXiv:2004.12655>.","text_prefix":false,"timestamp":1761562952,"version":"0.3.5"},"r-alphahull":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=alphahull","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/alphahull/alphahull_2.5.tar.gz","https://cran.r-project.org/src/contrib/alphahull_2.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.","text_prefix":false,"timestamp":1646675853,"version":"2.5"},"r-alphavantager":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/business-science/alphavantager","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/alphavantager/alphavantager_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/alphavantager_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Alpha Vantage has free historical financial information. All you need to do is get a free API key at <https://www.alphavantage.co>. Then you can use the R interface to retrieve free equity information. Refer to the Alpha Vantage website for more information.","text_prefix":false,"timestamp":1665969899,"version":"0.1.3"},"r-als":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ALS","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/ALS_0.0.7.tar.gz","https://cran.r-project.org/src/contrib/Archive/ALS/ALS_0.0.7.tar.gz"],"subdirs":["noarch"],"summary":"Alternating least squares is often used to resolve components contributing to data with a bilinear structure; the basic technique may be extended to alternating constrained least squares.  Commonly applied constraints include unimodality, non-negativity, and normalization of components. Several data matrices may be decomposed simultaneously by assuming that one of the two matrices in the bilinear decomposition is shared between datasets.","text_prefix":false,"timestamp":1621935210,"version":"0.0.7"},"r-amap":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=amap","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/amap/amap_0.8-20.tar.gz","https://cran.r-project.org/src/contrib/amap_0.8-20.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions).","text_prefix":false,"timestamp":1533272420,"version":"0.8_20"},"r-amelia":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://gking.harvard.edu/amelia","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Amelia_1.8.3.tar.gz","https://cran.r-project.org/src/contrib/Archive/Amelia/Amelia_1.8.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A tool that \"multiply imputes\" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables.  Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!).  The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data.  Amelia II also includes useful diagnostics of the fit of multiple imputation models.  The program works from the R command line or via a graphical user interface that does not require users to know R.","text_prefix":false,"timestamp":1665838730,"version":"1.8.3"},"r-ampir":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Legana/ampir","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ampir/ampir_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/ampir_1.1.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale. It incorporates two support vector machine models (\"precursor\" and \"mature\") trained on publicly available antimicrobial peptide data using calculated physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>. In order to support genome-wide analyses, these models are designed to accept any type of protein as input and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately represents your sequence type: for full length proteins, it is recommended to use the default \"precursor\" model. The alternative, \"mature\", model is best suited for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) <doi:10.1093/bioinformatics/btaa653>. The 'ampir' package is also available via a Shiny based GUI at <https://ampir.marine-omics.net/>.","text_prefix":false,"timestamp":1758692835,"version":"1.1.0"},"r-ampliconduo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=AmpliconDuo","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/AmpliconDuo_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/Archive/AmpliconDuo/AmpliconDuo_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads.","text_prefix":false,"timestamp":1588894934,"version":"1.1.1"},"r-analogue":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gavinsimpson/analogue","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/analogue/analogue_0.18.1.tar.gz","https://cran.r-project.org/src/contrib/analogue_0.18.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.","text_prefix":false,"timestamp":1758921433,"version":"0.18.1"},"r-anesrake":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=anesrake","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/anesrake/anesrake_0.80.tar.gz","https://cran.r-project.org/src/contrib/anesrake_0.80.tar.gz"],"subdirs":["noarch"],"summary":"Provides a comprehensive system for selecting variables and weighting data to match the specifications of the American National Election Studies. The package includes methods for identifying discrepant variables, raking data, and assessing the effects of the raking algorithm. It also allows automated re-raking if target variables fall outside identified bounds and allows greater user specification than other available raking algorithms. A variety of simple weighted statistics that were previously in this package (version .55 and earlier) have been moved to the package 'weights.'","text_prefix":false,"timestamp":1758806544,"version":"0.80"},"r-animation":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://yihui.name/animation","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/animation/animation_2.8.tar.gz","https://cran.r-project.org/src/contrib/animation_2.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Provides functions for animations in statistics, covering topics in probability theory, mathematical statistics, multivariate statistics, non-parametric statistics, sampling survey, linear models, time series, computational statistics, data mining and machine learning. These functions may be helpful in teaching statistics and data analysis. Also provided in this package are a series of functions to save animations to various formats, e.g. Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted into 'Sweave' / 'knitr' easily.","text_prefix":false,"timestamp":1540272668,"version":"2.8"},"r-anocva":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=anocva","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/anocva/anocva_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/anocva_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering.","text_prefix":false,"timestamp":1758746803,"version":"0.1.1"},"r-anom":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/PhilipPallmann/ANOM","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/ANOM_0.5.tar.gz","https://cran.r-project.org/src/contrib/Archive/ANOM/ANOM_0.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with 'multcomp', 'SimComp', 'nparcomp', or 'MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean.","text_prefix":false,"timestamp":0,"version":"0.5"},"r-anomalydetection":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/koalaverse/anomalyDetection","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/anomalyDetection/anomalyDetection_0.2.5.tar.gz","https://cran.r-project.org/src/contrib/anomalyDetection_0.2.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements procedures developed by Gutierrez et al. (2017, <https://journal.r-project.org/archive/2017/RJ-2017-039/index.html>)  to aid in detecting network log anomalies. By combining various multivariate  analytic approaches relevant to network anomaly detection, it provides cyber  analysts efficient means to detect suspected anomalies requiring further evaluation.","text_prefix":false,"timestamp":1758578231,"version":"0.2.5"},"r-antiword":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/ropensci/antiword#readme (devel) http://www.winfield.demon.nl (upstream)","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/antiword/antiword_1.3.5.tar.gz","https://cran.r-project.org/src/contrib/antiword_1.3.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Wraps the 'AntiWord' utility to extract text from Microsoft Word documents. 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In R a large number of function calls are often needed to obtain all of the desired information for a single APA style table. As well, the process of manually creating APA style tables in a word processor is prone to transcription errors. This package creates Word files (.doc files) containing APA style tables for several types of analyses. 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The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results.","text_prefix":false,"timestamp":1621996405,"version":"1.4.14"},"r-ape":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://ape-package.ird.fr/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ape/ape_5.8-1.tar.gz","https://cran.r-project.org/src/contrib/ape_5.8-1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.","text_prefix":false,"timestamp":1641715563,"version":"5.8_1"},"r-aphid":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/shaunpwilkinson/aphid","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/aphid/aphid_1.3.6.tar.gz","https://cran.r-project.org/src/contrib/aphid_1.3.6.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Designed for the development and application of hidden Markov models and profile HMMs for biological sequence analysis. Contains functions for multiple and pairwise sequence alignment, model construction and parameter optimization, file import/export, implementation of the forward, backward and Viterbi algorithms for conditional sequence probabilities, tree-based sequence weighting, and sequence simulation. Features a wide variety of potential applications including database searching, gene-finding and annotation, phylogenetic analysis and sequence classification. 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This package provides utilities to aligns associated subplots to a main plot at different sides (left, right, top and bottom) with axes exactly matched.","text_prefix":false,"timestamp":1630753137,"version":"0.3.2"},"r-aplpack":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.wiwi.uni-bielefeld.de/lehrbereiche/statoekoinf/comet/wolf/wolf_aplpack","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/aplpack/aplpack_1.3.5.tar.gz","https://cran.r-project.org/src/contrib/aplpack_1.3.5.tar.gz"],"subdirs":["noarch"],"summary":"Some functions for drawing some special plots: The function 'bagplot' plots a bagplot, 'faces' plots chernoff faces, 'iconplot' plots a representation of a frequency table or a data matrix, 'plothulls' plots hulls of a bivariate data set, 'plotsummary' plots a graphical summary of a data set, 'puticon' adds icons to a plot, 'skyline.hist' combines several histograms of a one dimensional data set in one plot, 'slider' functions supports some interactive graphics, 'spin3R' helps an inspection of a 3-dim point cloud, 'stem.leaf' plots a stem and leaf plot, 'stem.leaf.backback' plots back-to-back versions of stem and leaf plot.","text_prefix":false,"timestamp":1563735438,"version":"1.3.5"},"r-aptreeshape":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=apTreeshape","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/apTreeshape/apTreeshape_1.5-0.1.tar.gz","https://cran.r-project.org/src/contrib/apTreeshape_1.5-0.1.tar.gz"],"subdirs":["noarch"],"summary":"Simulation and analysis of phylogenetic tree topologies using statistical indices. It is a companion library of the 'ape' package. It provides additional functions for reading, plotting, manipulating phylogenetic trees. It also offers convenient web-access to public databases, and enables testing null models of macroevolution using corrected test statistics.  Trees of class \"phylo\" (from 'ape' package) can be converted easily. Implements methods described in Bortolussi et al. (2005) <doi:10.1093/bioinformatics/bti798> and Maliet et al. (2017)  <doi:10.1101/224295>.","text_prefix":false,"timestamp":1588881074,"version":"1.5_0.1"},"r-aqp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ncss-tech/aqp","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/aqp/aqp_2.3.2.tar.gz","https://cran.r-project.org/src/contrib/aqp_2.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.","text_prefix":false,"timestamp":0,"version":"2.3.2"},"r-archive":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://archive.r-lib.org/, https://github.com/r-lib/archive","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/archive/archive_1.1.13.tar.gz","https://cloud.r-project.org/src/contrib/archive_1.1.13.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Bindings to 'libarchive' <http://www.libarchive.org> the Multi-format archive and compression library. Offers R connections and direct extraction for many archive formats including 'tar', 'ZIP', '7-zip', 'RAR', 'CAB' and compression formats including 'gzip', 'bzip2', 'compress', 'lzma' and 'xz'.","text_prefix":false,"timestamp":1787234323,"version":"1.1.13"},"r-arg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ngreifer/arg","home":"https://ngreifer.github.io/arg/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/arg/arg_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/arg_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Checks function arguments, ideally for use in R packages. Uses a simple interface and produces clean, informative error messages using 'cli'.","text_prefix":false,"timestamp":1785414260,"version":"0.2.1"},"r-argonr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RinteRface/argonR","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/argonR/argonR_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/argonR_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"R wrapper around the argon HTML library. More at <https://demos.creative-tim.com/argon-design-system/>.","text_prefix":false,"timestamp":1563735471,"version":"0.2.0"},"r-argparse":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/trevorld/r-argparse","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/argparse/argparse_2.3.1.tar.gz","https://cran.r-project.org/src/contrib/argparse_2.3.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A command line parser to be used with Rscript to write \"#!\" shebang scripts that gracefully accept positional and optional arguments and automatically generate usage.","text_prefix":false,"timestamp":0,"version":"2.3.1"},"r-argparser":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://bitbucket.org/djhshih/argparser","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/argparser/argparser_0.7.3.tar.gz","https://cran.r-project.org/src/contrib/argparser_0.7.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Cross-platform command-line argument parser written purely in R with no external dependencies. It is useful with the Rscript front-end and facilitates turning an R script into an executable script.","text_prefix":false,"timestamp":0,"version":"0.7.3"},"r-argumentcheck":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/ArgumentCheck/index.html","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ArgumentCheck/ArgumentCheck_0.10.2.tar.gz","https://cran.r-project.org/src/contrib/ArgumentCheck_0.10.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The typical process of checking arguments in functions is iterative.  In this process, an error may be returned and the user may fix it only to receive another error on a different argument.  'ArgumentCheck' facilitates a more helpful way to perform argument checks allowing the programmer to run all of the checks and then return all of the errors and warnings in a single message.","text_prefix":false,"timestamp":1540159777,"version":"0.10.2"},"r-aricode":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jchiquet/aricode","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/aricode/aricode_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/aricode_1.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements an efficient O(n) algorithm based on bucket-sorting for fast computation of standard clustering comparison measures. 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Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.","text_prefix":false,"timestamp":1537050088,"version":"1.15_3"},"r-aroma.apd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"URL: http://www.aroma-project.org/, https://github.com/HenrikBengtsson/aroma.apd","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/aroma.apd/aroma.apd_0.7.1.tar.gz","https://cran.r-project.org/src/contrib/aroma.apd_0.7.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities.  Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets).  In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit.  This speeds up the read substantially.  This package is supporting the Aroma framework and should not be used elsewhere.)","text_prefix":false,"timestamp":1540303358,"version":"0.7.1"},"r-arrangements":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/randy3k/arrangements","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/arrangements/arrangements_1.1.10.tar.gz","https://cran.r-project.org/src/contrib/arrangements_1.1.10.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fast generators and iterators for permutations, combinations, integer partitions and compositions. The arrangements are in lexicographical order and generated iteratively in a memory efficient manner. It has been demonstrated that 'arrangements' outperforms most existing packages of similar kind. 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This can retrieve all dependencies that are used in \".R\" files in the \"R/\" directory, in \".Rmd\" files in \"vignettes/\" directory and in 'roxygen2' documentation of functions. There is a function to update the \"DESCRIPTION\" file of your package with 'CRAN' packages or any other remote package.  All functions to retrieve dependencies of \".R\" scripts and \".Rmd\" or \".qmd\" files can be used independently of a package development.","text_prefix":false,"timestamp":1790860227,"version":"1.2.0"},"r-attempt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ColinFay/attempt","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/attempt/attempt_0.3.1.tar.gz","https://cran.r-project.org/src/contrib/attempt_0.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Tools for defensive programming, inspired by 'purrr' mappers and  based on 'rlang'.'attempt' extends and facilitates defensive programming by  providing a consistent grammar, and provides a set of easy to use functions  for common tests and conditions. 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Built-in devices include Windows MM, Mac OS X AudioUnits and PortAudio (the last one is very experimental).","text_prefix":false,"timestamp":1632302875,"version":"0.1_12"},"r-automap":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=automap","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/automap/automap_1.1-20.tar.gz","https://cran.r-project.org/src/contrib/automap_1.1-20.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This package performs an automatic interpolation by automatically estimating the variogram and then calling gstat.","text_prefix":false,"timestamp":1539790445,"version":"1.1_20"},"r-autometric":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://wlandau.github.io/autometric/, https://github.com/wlandau/autometric","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/autometric/autometric_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/autometric_0.1.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Intense parallel workloads can be difficult to monitor. Packages 'crew.cluster', 'clustermq', and 'future.batchtools' distribute hundreds of worker processes over multiple computers. If a worker process exhausts its available memory, it may terminate silently, leaving the underlying problem difficult to detect or troubleshoot. Using the 'autometric' package, a worker can proactively monitor itself in a detached background thread. The worker process itself runs normally, and the thread writes to a log every few seconds. If the worker terminates unexpectedly, 'autometric' can read and visualize the log file to reveal potential resource-related reasons for the crash. 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This package interfaces directly to the C API and does not require any command line utilities.","text_prefix":false,"timestamp":1621777851,"version":"0.9.6"},"r-available":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropenscilabs/available","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/available/available_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/available_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Check if a given package name is available to use. It checks the name's validity. Checks if it is used on 'GitHub', 'CRAN' and 'Bioconductor'. Checks for unintended meanings by querying Urban Dictionary, 'Wiktionary' and Wikipedia.","text_prefix":false,"timestamp":1622615883,"version":"1.1.0"},"r-aws":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.wias-berlin.de/people/polzehl/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/aws/aws_2.5-7.tar.gz","https://cran.r-project.org/src/contrib/aws_2.5-7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"We provide a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation Approach to adaptive smoothing as described in \"J. Polzehl and V. Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1>\", \"J. Polzehl and V. Spokoiny (2004) <DOI:10.20347/WIAS.PREPRINT.998>\" and \"J. Polzehl, K. Papafitsoros, K. 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For use on 'EC2' instances, users will need to install the suggested package 'aws.ec2metadata' <https://cran.r-project.org/package=aws.ec2metadata>.","text_prefix":false,"timestamp":1531100014,"version":"0.6.2"},"r-awsmethods":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.wias-berlin.de/software/imaging/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/awsMethods/awsMethods_1.1-2.tar.gz","https://cloud.r-project.org/src/contrib/awsMethods_1.1-2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Defines the method extract and provides 'openMP' support as needed in several packages.","text_prefix":false,"timestamp":1553397504,"version":"1.1_2"},"r-azureauth":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Azure/AzureAuth https://github.com/Azure/AzureR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/AzureAuth/AzureAuth_1.3.5.tar.gz","https://cloud.r-project.org/src/contrib/AzureAuth_1.3.5.tar.gz"],"subdirs":["noarch"],"summary":"Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud <https://azure.microsoft.com/>. Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages.","text_prefix":false,"timestamp":1788033941,"version":"1.3.5"},"r-azuregraph":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Azure/AzureGraph https://github.com/Azure/AzureR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/AzureGraph/AzureGraph_1.3.5.tar.gz","https://cran.r-project.org/src/contrib/AzureGraph_1.3.5.tar.gz"],"subdirs":["noarch"],"summary":"A simple interface to the 'Microsoft Graph' API <https://docs.microsoft.com/en-us/graph/overview>. 'Graph' is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the 'Azure Active Directory' part, with a view to supporting interoperability of R and 'Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the 'AzureR' family of packages.","text_prefix":false,"timestamp":1758433339,"version":"1.3.5"},"r-azurermr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Azure/AzureRMR https://github.com/Azure/AzureR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/AzureRMR/AzureRMR_2.4.5.tar.gz","https://cran.r-project.org/src/contrib/AzureRMR_2.4.5.tar.gz"],"subdirs":["noarch"],"summary":"A lightweight but powerful R interface to the 'Azure Resource Manager' REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting 'Azure' resource groups, resources and templates. 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On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages.","text_prefix":false,"timestamp":1758619034,"version":"3.7.1"},"r-b64":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/extendr/b64","home":"https://extendr.github.io/b64/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/b64/b64_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/b64_0.1.3.tar.gz"],"subdirs":["linux-64"],"summary":"Provides a fast, lightweight, and vectorized base 64 engine to encode and decode character and raw vectors as well as files stored on disk.  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Two of these perform shrinkage estimation: one using a simple weighted method where the user can specify the degree of shrinkage required, and one using James-Stein shrinkage estimation for the case of unequal variances.","text_prefix":false,"timestamp":1773523999,"version":"1.0.0"},"r-bayesfactor":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://richarddmorey.github.io/BayesFactor/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BayesFactor/BayesFactor_0.9.12-4.8.tar.gz","https://cran.r-project.org/src/contrib/BayesFactor_0.9.12-4.8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.","text_prefix":false,"timestamp":1533357759,"version":"0.9.12_4.8"},"r-bayesforecast":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=bayesforecast","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bayesforecast/bayesforecast_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/bayesforecast_1.0.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series.  Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.","text_prefix":false,"timestamp":1622121098,"version":"1.0.5"},"r-bayeslogit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jwindle/BayesLogit","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BayesLogit/BayesLogit_2.4.tar.gz","https://cran.r-project.org/src/contrib/BayesLogit_2.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>.  Useful for logistic regression.","text_prefix":false,"timestamp":1780912146,"version":"2.4"},"r-bayesm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/cran/bayesm","home":"http://www.perossi.org/home/bsm-1","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bayesm/bayesm_3.1-7.tar.gz","https://cran.r-project.org/src/contrib/bayesm_3.1-7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Covers many important models used in marketing and micro-econometrics applications.  The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014).","text_prefix":false,"timestamp":1563517359,"version":"3.1_7"},"r-bayesplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://mc-stan.org/bayesplot","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bayesplot/bayesplot_1.15.0.tar.gz","https://cran.r-project.org/src/contrib/bayesplot_1.15.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Plotting functions for posterior analysis,  prior and posterior predictive checks, and MCMC diagnostics.  The package is designed not only to provide convenient functionality  for users, but also a common set of functions that can be easily used by  developers working on a variety of R packages for Bayesian modeling,  particularly (but not exclusively) packages interfacing with 'Stan'.","text_prefix":false,"timestamp":0,"version":"1.15.0"},"r-bayesrel":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/juliuspf/Bayesrel","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/Bayesrel/Bayesrel_0.8.0.tar.gz","https://cloud.r-project.org/src/contrib/Bayesrel_0.8.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functionality for the most common single test reliability estimates: Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower bound and coefficient omega. The Bayesian estimates are provided with credible intervals. The frequentist estimates are provided with bootstrapped confidence intervals The method for the Bayesian estimates, except for omega, is sampling from the posterior inverse 'Wishart' for the covariance matrix based measures (see 'Murphy', 2007, <https://www.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. In the case of omega it is 'Gibbs' Sampling from the joint conditional distributions of a single factor model ('Lee', 2007, <doi:10.1002/9780470024737>). The glb method uses adjusted code from the 'Rcsdp' package by 'Hector Corrada Bravo', <https://CRAN.R-project.org/package=Rcsdp>. This process applies a slightly adjusted solving algorithm from the 'CSDP' library by 'Brian Borchers' <https://github.com/coin-or/Csdp/wiki>, <doi:10.1080/10556789908805765>, but is wrapped in 'RcppArmadillo'. Guttman's Lambda-4 is from 'Benton' (2015) <doi:10.1007/978-3-319-07503-7_19>. The principal factor analysis for a version of frequentist omega is from 'Schlegel' (2017) <https://www.r-bloggers.com/2017/03/iterated-principal-factor-method-of-factor-analysis-with-r/>. The analytic confidence interval of alpha is from 'Bonett' and 'Wright' (2015) <doi:10.1002/job.1960>.","text_prefix":false,"timestamp":1666811675,"version":"0.8.0"},"r-bayestestr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/easystats/bayestestR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/bayestestR/bayestestR_0.20.0.tar.gz","https://cloud.r-project.org/src/contrib/bayestestR_0.20.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors).","text_prefix":false,"timestamp":1622457206,"version":"0.20.0"},"r-bayestools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://fbartos.github.io/BayesTools/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BayesTools/BayesTools_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/BayesTools_0.3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Provides tools for conducting Bayesian analyses. The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.","text_prefix":false,"timestamp":1635678091,"version":"0.3.0"},"r-bazar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/paulponcet/bazar","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bazar/bazar_1.0.11.tar.gz","https://cran.r-project.org/src/contrib/bazar_1.0.11.tar.gz"],"subdirs":["noarch"],"summary":"A collection of miscellaneous functions for  copying objects to the clipboard ('Copy'); manipulating strings ('concat', 'mgsub', 'trim', 'verlan');  loading or showing packages ('library_with_dep', 'require_with_dep',  'sessionPackages');  creating or testing for named lists ('nlist', 'as.nlist', 'is.nlist'),  formulas ('is.formula'), empty objects ('as.empty', 'is.empty'),  whole numbers ('as.wholenumber', 'is.wholenumber');  testing for equality ('almost.equal', 'almost.zero') and computing  uniqueness ('almost.unique');  getting modified versions of usual functions ('rle2', 'sumNA');  making a pause or a stop ('pause', 'stopif');  converting into a function ('as.fun');  providing a C like ternary operator ('condition %?% true %:% false');  finding packages and functions ('get_all_pkgs', 'get_all_funs'); and others ('erase', '%nin%', 'unwhich', 'top', 'bot', 'normalize').","text_prefix":false,"timestamp":1757733425,"version":"1.0.11"},"r-bb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.html","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BB/BB_2026.1.0.tar.gz","https://cran.r-project.org/src/contrib/BB_2026.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Barzilai-Borwein spectral methods for solving nonlinear system of equations, and for optimizing nonlinear objective functions subject to simple constraints. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette(\"BB\").","text_prefix":false,"timestamp":1537168913,"version":"2026.1.0"},"r-bbmisc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/berndbischl/BBmisc","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BBmisc/BBmisc_1.13.1.tar.gz","https://cran.r-project.org/src/contrib/BBmisc_1.13.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development.","text_prefix":false,"timestamp":1647128915,"version":"1.13.1"},"r-bbmle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=bbmle","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/bbmle/bbmle_1.0.26.tar.gz","https://cloud.r-project.org/src/contrib/bbmle_1.0.26.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.","text_prefix":false,"timestamp":1539921207,"version":"1.0.26"},"r-bbotk":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/mlr-org/bbotk","home":"https://bbotk.mlr-org.com","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bbotk/bbotk_1.10.0.tar.gz","https://cran.r-project.org/src/contrib/bbotk_1.10.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. 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Also provides  functions for obtaining system specifications, such as RAM, CPU type, and R version.","text_prefix":false,"timestamp":1588685890,"version":"1.0.8"},"r-benchmarkmedata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/csgillespie/benchmarkme-data","license":"GPL-2 | GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/benchmarkmeData/benchmarkmeData_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/benchmarkmeData_2.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Crowd sourced benchmarks from running the 'benchmarkme' package.","text_prefix":false,"timestamp":1572721587,"version":"2.0.0"},"r-berryfunctions":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/brry/berryFunctions","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/berryFunctions/berryFunctions_1.22.13.tar.gz","https://cran.r-project.org/src/contrib/berryFunctions_1.22.13.tar.gz"],"subdirs":["noarch"],"summary":"Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.","text_prefix":false,"timestamp":1665405237,"version":"1.22.13"},"r-best":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mikemeredith/BEST","home":"https://CRAN.R-project.org/package=BEST","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BEST/BEST_0.5.4.tar.gz","https://cran.r-project.org/src/contrib/BEST_0.5.4.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"An alternative to t-tests, producing posterior estimates for group means and standard deviations and their differences and effect sizes. 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Implements one-standard deviation rule for use with the `caret` package.","text_prefix":false,"timestamp":1573715680,"version":"0.37.3"},"r-bestnormalize":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/petersonR/bestNormalize","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bestNormalize/bestNormalize_1.9.2.tar.gz","https://cran.r-project.org/src/contrib/bestNormalize_1.9.2.tar.gz"],"subdirs":["noarch"],"summary":"Estimate a suite of normalizing transformations, including a new adaptation of a technique based on ranks which can guarantee normally distributed transformed data if there are no ties: ordered quantile normalization (ORQ). ORQ normalization combines a rank-mapping approach with a shifted logit approximation that allows the transformation to work on data outside the original domain. 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The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.","text_prefix":false,"timestamp":1563731715,"version":"1.1.2"},"r-bfa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=bfa","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bfa/bfa_0.4.tar.gz","https://cran.r-project.org/src/contrib/bfa_0.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides model fitting for several Bayesian factor models including Gaussian, ordinal probit, mixed and semiparametric Gaussian copula factor models under a range of priors.","text_prefix":false,"timestamp":1758402211,"version":"0.4"},"r-bglr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=BGLR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BGLR/BGLR_1.1.4.tar.gz","https://cran.r-project.org/src/contrib/BGLR_1.1.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Bayesian Generalized Linear Regression.","text_prefix":false,"timestamp":1636895979,"version":"1.1.4"},"r-bgmm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://bgmm.molgen.mpg.de/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bgmm/bgmm_1.8.5.tar.gz","https://cran.r-project.org/src/contrib/bgmm_1.8.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Two partially supervised mixture modeling methods:  soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling.  The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software  <doi:10.18637/jss.v047.i03>.","text_prefix":false,"timestamp":1539906738,"version":"1.8.5"},"r-bh":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=BH","license":"BSL-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BH/BH_1.90.0-1.tar.gz","https://cran.r-project.org/src/contrib/BH_1.90.0-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Boost provides free peer-reviewed portable C++ source  libraries.  A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking.  This  package aims to provide the most useful subset of Boost libraries  for template use among CRAN package. By placing these libraries in  this package, we offer a more efficient distribution system for CRAN  as replication of this code in the sources of other packages is  avoided. As of release 1.69.0-1, the following Boost libraries are included: 'algorithm' 'align' 'any' 'atomic' 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config' 'container' 'date_time' 'detail' 'dynamic_bitset' 'exception' 'filesystem' 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap' 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams' 'iterator' 'math' 'move' 'mpl' 'multiprcecision' 'numeric' 'pending' 'phoenix' 'preprocessor' 'propery_tree' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort' 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'utility' 'uuid'.","text_prefix":false,"timestamp":0,"version":"1.90.0_1"},"r-biasedurn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.agner.org/random/ http://www.r-project.org","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BiasedUrn/BiasedUrn_2.0.12.tar.gz","https://cran.r-project.org/src/contrib/BiasedUrn_2.0.12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Statistical models of biased sampling in the form of  univariate and multivariate noncentral hypergeometric distributions,  including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution  (also called extended hypergeometric distribution).   See vignette(\"UrnTheory\") for explanation of these distributions.","text_prefix":false,"timestamp":1666457785,"version":"2.0.12"},"r-bib2df":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/bib2df","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bib2df/bib2df_1.1.2.0.tar.gz","https://cran.r-project.org/src/contrib/bib2df_1.1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization.","text_prefix":false,"timestamp":1758436678,"version":"1.1.2.0"},"r-bibitr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=BiBitR","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BiBitR/BiBitR_0.3.1.tar.gz","https://cran.r-project.org/src/contrib/BiBitR_0.3.1.tar.gz"],"subdirs":["noarch"],"summary":"A simple R wrapper for the Java BiBit algorithm from \"A biclustering algorithm for extracting bit-patterns from binary datasets\" from Domingo et al. (2011) <DOI:10.1093/bioinformatics/btr464>. An simple adaption for the BiBit algorithm which allows noise in the biclusters is also introduced as well as a function to guide the algorithm towards given (sub)patterns. Further, a workflow to derive noisy biclusters from discoverd larger column patterns is included as well.","text_prefix":false,"timestamp":1758679976,"version":"0.3.1"},"r-bibliometrix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/massimoaria/bibliometrix","home":"https://www.bibliometrix.org","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/bibliometrix/bibliometrix_5.5.0.tar.gz","https://cloud.r-project.org/src/contrib/bibliometrix_5.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>. 'bibliometrix' provides various routines for importing bibliographic data from 'SCOPUS', 'Clarivate Analytics Web of Science' (<https://www.webofknowledge.com/>), 'Digital Science Dimensions' (<https://www.dimensions.ai/>), 'OpenAlex' (<https://openalex.org/>), 'Cochrane Library' (<https://www.cochranelibrary.com/>),  'Lens' (<https://lens.org>), and 'PubMed' (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.","text_prefix":false,"timestamp":1788019091,"version":"5.5.0"},"r-bibliometrixdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=bibliometrixData","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bibliometrixData/bibliometrixData_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/bibliometrixData_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"It contains some example datasets used in 'bibliometrix'. The data are bibliographic datasets exported from the 'SCOPUS' (<https://scopus.com>) and 'Clarivate Analytics Web of Science' (<https://www.webofscience.com/>) databases. They can be used to test the different features of the package 'bibliometrix' (<https://bibliometrix.org>).","text_prefix":false,"timestamp":1758015519,"version":"0.3.0"},"r-bibtex":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/romainfrancois/bibtex","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/bibtex/bibtex_0.5.3.tar.gz","https://cloud.r-project.org/src/contrib/bibtex_0.5.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","win-32","win-64"],"summary":"Utility to parse a bibtex file.","text_prefix":false,"timestamp":1577090475,"version":"0.5.3"},"r-biclust":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=biclust","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/biclust/biclust_2.0.3.1.tar.gz","https://cran.r-project.org/src/contrib/biclust_2.0.3.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The main function biclust() provides several algorithms to find biclusters in two-dimensional data: Cheng and Church (2000, ISBN:1-57735-115-0), spectral (2003) <doi:10.1101/gr.648603>, plaid model (2005) <doi:10.1016/j.csda.2004.02.003>, xmotifs (2003) <doi:10.1142/9789812776303_0008> and bimax (2006) <doi:10.1093/bioinformatics/btl060>. In addition, the package provides methods for data preprocessing (normalization and discretisation), visualisation, and validation of bicluster solutions.","text_prefix":false,"timestamp":1533730631,"version":"2.0.3.1"},"r-bien":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=BIEN","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BIEN/BIEN_1.2.8.tar.gz","https://cran.r-project.org/src/contrib/BIEN_1.2.8.tar.gz"],"subdirs":["noarch"],"summary":"Provides Tools for Accessing the Botanical Information and Ecology Network Database.  The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See <https://bien.nceas.ucsb.edu/bien/> for more Information).  This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.","text_prefix":false,"timestamp":1777539710,"version":"1.2.8"},"r-bigalgebra":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.bigmemory.org","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/bigalgebra/bigalgebra_3.1.1.tar.gz","https://cloud.r-project.org/src/contrib/bigalgebra_3.1.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects.","text_prefix":false,"timestamp":1665880576,"version":"3.1.1"},"r-biganalytics":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.bigmemory.org","license":"LGPL-3.0-only OR Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/biganalytics/biganalytics_1.1.22.tar.gz","https://cran.r-project.org/src/contrib/biganalytics_1.1.22.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Extend the 'bigmemory' package with various analytics. Functions 'bigkmeans' and 'binit' may also be used with native R objects. For 'tapply'-like functions, the bigtabulate package may also be helpful. 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Also, multiple assertion functions (e.g. to check class, length, values, files, arguments, etc.).","text_prefix":false,"timestamp":1621879110,"version":"0.2.0"},"r-bigd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rich-iannone/bigD","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bigD/bigD_0.3.1.tar.gz","https://cran.r-project.org/src/contrib/bigD_0.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Format dates and times flexibly and to whichever locales make sense. Parses dates, times, and date-times in various formats (including string-based ISO 8601 constructions). The formatting syntax gives the user many options for formatting the date and time output in a precise manner. Time zones in the input can be expressed in multiple ways and there are many options for formatting time zones in the output as well. Several of the provided helper functions allow for automatic generation of locale-aware formatting patterns based on date/time skeleton formats and standardized date/time formats with varying specificity.","text_prefix":false,"timestamp":1757570676,"version":"0.3.1"},"r-biglasso":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/YaohuiZeng/biglasso, https://arxiv.org/abs/1701.05936","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/biglasso/biglasso_1.6.1.tar.gz","https://cran.r-project.org/src/contrib/biglasso_1.6.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Extend lasso and elastic-net model fitting for ultrahigh-dimensional, multi-gigabyte data sets that cannot be loaded into memory. 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Packages 'biganalytics', 'bigtabulate', 'synchronicity', and 'bigalgebra' provide advanced functionality.","text_prefix":false,"timestamp":1651441142,"version":"4.6.6"},"r-bigmemory.sri":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=bigmemory.sri","license":"LGPL-3.0-only OR Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bigmemory.sri/bigmemory.sri_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/bigmemory.sri_0.1.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This package provides a shared resource interface for the bigmemory and synchronicity packages.","text_prefix":false,"timestamp":1536970187,"version":"0.1.8"},"r-bigparallelr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/privefl/bigparallelr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bigparallelr/bigparallelr_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/bigparallelr_0.3.2.tar.gz"],"subdirs":["noarch"],"summary":"Utility functions for easy parallelism in R. 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Package 'bigreadr' also provides some convenient wrappers around fread() and fwrite() from package 'data.table'.","text_prefix":false,"timestamp":1613575055,"version":"0.2.5"},"r-bigrquery":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-dbi/bigrquery","home":"https://bigrquery.r-dbi.org/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bigrquery/bigrquery_1.6.2.tar.gz","https://cran.r-project.org/src/contrib/bigrquery_1.6.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Easily talk to Google's 'BigQuery' database from R.","text_prefix":false,"timestamp":1551777319,"version":"1.6.2"},"r-bigsnpr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://privefl.github.io/bigsnpr/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bigsnpr/bigsnpr_1.12.21.tar.gz","https://cran.r-project.org/src/contrib/bigsnpr_1.12.21.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. 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The package  allows to work with pre-allocated memory for return values by calling .Call()  directly: when evaluating the speed of C-access with pre-allocated vector  memory, coping from bit to logical requires only 70% of the time for copying  from logical to logical; and copying from logical to bit comes at a  performance penalty of 150%. the package now contains further classes for  representing logical selections: 'bitwhich' for very skewed selections and  'ri' for selecting ranges of values for chunked processing. 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Class integer64 can be used in vectors, matrices, arrays and data.frames.  Methods are available for coercion from and to logicals, integers, doubles,  characters and factors as well as many elementwise and summary functions.  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For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance.  For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions.  Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.","text_prefix":false,"timestamp":1781302697,"version":"1.4.10"},"r-bmp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=bmp","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bmp/bmp_0.3.1.tar.gz","https://cran.r-project.org/src/contrib/bmp_0.3.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Reads Windows BMP format images. Currently limited to 8 bit greyscale images and 24,32 bit (A)RGB images. 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Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison.","text_prefix":false,"timestamp":1625999046,"version":"0.3.5"},"r-bnlearn":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.bnlearn.com/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bnlearn/bnlearn_5.2.1.tar.gz","https://cran.r-project.org/src/contrib/bnlearn_5.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Bayesian network structure learning, parameter learning and inference. 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The posterior updates the amount of mass on this point, leading to a posterior distribution that is actually sparse, in the sense that if you sample from it many coefficients are actually zeros.  Sampling from this posterior distribution is an elegant way to handle Bayesian variable selection and model averaging.  See <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.","text_prefix":false,"timestamp":1665597717,"version":"1.2.7"},"r-boot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=boot","license":"Unlimited","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/boot/boot_1.3-32.tar.gz","https://cran.r-project.org/src/contrib/boot_1.3-32.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions and datasets for bootstrapping from the book \"Bootstrap Methods and Their Application\" by A. C. 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This package provides methods for total species richness estimation (observed plus unobserved) and a method for modelling total diversity with covariates. breakaway() estimates total (observed plus unobserved) species richness. Microbial diversity datasets are characterized by a large number of rare species and a small number of highly abundant species. The class of models implemented by breakaway() is flexible enough to model both these features. breakaway_nof1() implements a similar procedure however does not require a singleton count. betta() provides a method for modelling total diversity with covariates in a way that accounts for its estimated nature and thus accounts for unobserved taxa, and betta_random() permits random effects modelling.","text_prefix":false,"timestamp":1621897575,"version":"3.0"},"r-breakdown":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pbiecek.github.io/breakDown/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/breakDown/breakDown_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/breakDown_0.2.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction.  Break Down Plot presents variable contributions in a concise graphical way.  This package work for binary classifiers and general regression models.","text_prefix":false,"timestamp":0,"version":"0.2.2"},"r-brew":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=brew","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/brew/brew_1.0-10.tar.gz","https://cran.r-project.org/src/contrib/brew_1.0-10.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module.","text_prefix":false,"timestamp":1536412555,"version":"1.0_10"},"r-brglm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/ikosmidis/brglm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/brglm/brglm_0.7.3.tar.gz","https://cran.r-project.org/src/contrib/brglm_0.7.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'.  More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.","text_prefix":false,"timestamp":1602483449,"version":"0.7.3"},"r-brglm2":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/ikosmidis/brglm2","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/brglm2/brglm2_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/brglm2_1.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression).","text_prefix":false,"timestamp":1603183616,"version":"1.1.0"},"r-bridgesampling":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/quentingronau/bridgesampling","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/bridgesampling/bridgesampling_1.2-1.tar.gz","https://cran.r-project.org/src/contrib/bridgesampling_1.2-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996,  <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).","text_prefix":false,"timestamp":1525908383,"version":"1.2_1"},"r-brio":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-lib/brio","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/brio/brio_1.1.5.tar.gz","https://cran.r-project.org/src/contrib/brio_1.1.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions to handle basic input output, these functions always read and write UTF-8 (8-bit Unicode Transformation Format) files and provide more explicit control over line endings.","text_prefix":false,"timestamp":1611179121,"version":"1.1.5"},"r-brisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ArkajyotiSaha/BRISC","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/BRISC/BRISC_1.0.6.tar.gz","https://cran.r-project.org/src/contrib/BRISC_1.0.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fits bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference on Spatial Covariances (BRISC) for large datasets using nearest neighbor Gaussian processes detailed in Saha and Datta (2018) <doi:10.1002/sta4.184>.","text_prefix":false,"timestamp":1758681342,"version":"1.0.6"},"r-brms":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/paul-buerkner/brms","home":"http://discourse.mc-stan.org","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/brms/brms_2.23.0.tar.gz","https://cran.r-project.org/src/contrib/brms_2.23.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions  and link functions are supported, allowing users to fit -- among others --  linear, robust linear, count data, survival, response times, ordinal,  zero-inflated, hurdle, and even self-defined mixture models all in a  multilevel context. Further modeling options include non-linear and  smooth terms, auto-correlation structures, censored data, meta-analytic  standard errors, and quite a few more. In addition, all parameters of the  response distribution can be predicted in order to perform distributional  regression. Prior specifications are flexible and explicitly encourage  users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive  checks and leave-one-out cross-validation. References: B\u00fcrkner (2017) <doi:10.18637/jss.v080.i01>; B\u00fcrkner (2018) <doi:10.32614/RJ-2018-017>; Carpenter et al. 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The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. 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These quantities can be directly taken from the output of existing software. Canopy provides a  general mathematical framework for pooling data across samples and sites to  infer the underlying parameters. For SNAs that fall within CNA regions, Canopy infers their temporal ordering and resolves their phase.  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The package's greatest contributions lie in the database style merge, aggregation and interface functions as well as in it's extensive use and propagation of row, column and vector names in most functions.","text_prefix":false,"timestamp":1539411307,"version":"1.1.2"},"r-carrier":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/carrier","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/carrier/carrier_0.3.0.4.tar.gz","https://cran.r-project.org/src/contrib/carrier_0.3.0.4.tar.gz"],"subdirs":["noarch"],"summary":"Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With the carrier package, it is easy to create functions that are isolated from their environment. 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Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. 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Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).","text_prefix":false,"timestamp":1758536554,"version":"0.1.1"},"r-catnet":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=catnet","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/catnet/catnet_1.16.1.tar.gz","https://cran.r-project.org/src/contrib/catnet_1.16.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Structure learning and parameter estimation of discrete Bayesian networks using likelihood-based criteria.  Exhaustive search for fixed node orders and stochastic search of optimal orders via simulated annealing algorithm are implemented.","text_prefix":false,"timestamp":1544636095,"version":"1.16.1"},"r-catools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=caTools","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/caTools/caTools_1.18.4.tar.gz","https://cran.r-project.org/src/contrib/caTools_1.18.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc.","text_prefix":false,"timestamp":1563364419,"version":"1.18.4"},"r-catr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=catR","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/catR/catR_3.17.tar.gz","https://cran.r-project.org/src/contrib/catR_3.17.tar.gz"],"subdirs":["noarch"],"summary":"Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>).","text_prefix":false,"timestamp":1758722409,"version":"3.17"},"r-catt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CATT","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CATT/CATT_2.0.tar.gz","https://cran.r-project.org/src/contrib/CATT_2.0.tar.gz"],"subdirs":["noarch"],"summary":"This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.","text_prefix":false,"timestamp":1758133319,"version":"2.0"},"r-causact":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/flyaflya/causact, https://www.causact.com/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/causact/causact_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/causact_0.6.0.tar.gz"],"subdirs":["noarch"],"summary":"Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on the sleek and elegant 'greta' package for Bayesian inference. 'greta', in turn, is an interface into 'TensorFlow' from 'R'. Install 'greta' using instructions available here: <https://www.causact.com/install-tensorflow-greta-and-causact.html>. See <https://github.com/flyaflya/causact> or <https://www.causact.com/> for more documentation.","text_prefix":false,"timestamp":1766293599,"version":"0.6.0"},"r-causalimpact":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/google/CausalImpact","home":"https://google.github.io/CausalImpact/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CausalImpact/CausalImpact_1.4.1.tar.gz","https://cran.r-project.org/src/contrib/CausalImpact_1.4.1.tar.gz"],"subdirs":["noarch"],"summary":"Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>. See the package documentation on GitHub <https://google.github.io/CausalImpact/> to get started.","text_prefix":false,"timestamp":1758943450,"version":"1.4.1"},"r-cba":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cba","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cba/cba_0.2-25.tar.gz","https://cran.r-project.org/src/contrib/cba_0.2-25.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.","text_prefix":false,"timestamp":1667862989,"version":"0.2_25"},"r-cbctools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jhelvy/cbcTools","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cbcTools/cbcTools_0.7.1.tar.gz","https://cran.r-project.org/src/contrib/cbcTools_0.7.1.tar.gz"],"subdirs":["noarch"],"summary":"Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, full factorial designs, orthogonal designs, D-optimal designs, and Bayesian D-efficient designs as well as designs with \"no choice\" options and \"labeled\" (also known as \"alternative specific\") designs. Conveniently inspect the design balance and overlap, and simulate choice data for a survey design either randomly or according to a multinomial or mixed logit utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Full factorial and orthogonal designs are obtained using the 'DoE.base' package (Gr\u00f6mping, 2018) <doi:10.18637/jss.v085.i05>. D-optimal designs are obtained using the 'AlgDesign' package (Wheeler, 2022) <https://CRAN.R-project.org/package=AlgDesign>. Bayesian D-efficient designs are obtained using the 'idefix' package (Traets et al, 2020) <doi:10.18637/jss.v096.i03>. Choice simulation and model estimation in power analyses are handled using the 'logitr' package (Helveston, 2023) <doi:10.18637/jss.v105.i10>.","text_prefix":false,"timestamp":1760994977,"version":"0.7.1"},"r-cbioportalr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/karissawhiting/cbioportalR, https://www.karissawhiting.com/cbioportalR/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cbioportalR/cbioportalR_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/cbioportalR_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides R users with direct access to genomic and clinical data from the 'cBioPortal' web resource via user-friendly functions that wrap 'cBioPortal's' existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations, copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'), microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.","text_prefix":false,"timestamp":1661261517,"version":"1.1.1"},"r-ccapp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/aalfons/ccaPP","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ccaPP/ccaPP_0.3.6.tar.gz","https://cran.r-project.org/src/contrib/ccaPP_0.3.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Canonical correlation analysis and maximum correlation via projection pursuit, as well as fast implementations of correlation estimators, with a focus on robust and nonparametric methods.","text_prefix":false,"timestamp":1563729106,"version":"0.3.6"},"r-ccdralgorithm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/itsrainingdata/ccdrAlgorithm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ccdrAlgorithm/ccdrAlgorithm_0.0.6.tar.gz","https://cran.r-project.org/src/contrib/ccdrAlgorithm_0.0.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) <http://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.","text_prefix":false,"timestamp":1621985144,"version":"0.0.6"},"r-cchsflow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Big-Life-Lab/cchsflow","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cchsflow/cchsflow_2.1.0.tar.gz","https://cran.r-project.org/src/contrib/cchsflow_2.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Supporting the use of the Canadian Community Health Survey (CCHS) by transforming variables from each cycle into harmonized, consistent versions that span survey cycles (currently, 2001 to 2018). CCHS data used in this library is accessed and adapted in accordance to the Statistics Canada Open Licence Agreement. This package uses rec_with_table(), which was developed from 'sjmisc' rec(). L\u00fcdecke D (2018). \"sjmisc: Data and Variable Transformation Functions\". Journal of Open Source Software, 3(26), 754. <doi:10.21105/joss.00754>.","text_prefix":false,"timestamp":1758651004,"version":"2.1.0"},"r-ccp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CCP","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CCP/CCP_1.2.tar.gz","https://cran.r-project.org/src/contrib/CCP_1.2.tar.gz"],"subdirs":["noarch"],"summary":"Significance tests for canonical correlation analysis, including asymptotic tests and a Monte Carlo method","text_prefix":false,"timestamp":1621934836,"version":"1.2"},"r-ccremover":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ccRemover","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ccRemover/ccRemover_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/ccRemover_1.0.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the  method is in Barron M. and Li J. (2016) <doi","text_prefix":false,"timestamp":0,"version":"1.0.4"},"r-cctensor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rikenbit/ccTensor","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ccTensor/ccTensor_1.0.3.tar.gz","https://cran.r-project.org/src/contrib/ccTensor_1.0.3.tar.gz"],"subdirs":["noarch"],"summary":"CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.","text_prefix":false,"timestamp":1772609058,"version":"1.0.3"},"r-cctools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cctools","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cctools/cctools_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/cctools_0.1.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements the uniform scaled beta distribution and the continuous convolution kernel density estimator.","text_prefix":false,"timestamp":1770887582,"version":"0.1.3"},"r-cdft":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CDFt","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CDFt/CDFt_1.2.tar.gz","https://cran.r-project.org/src/contrib/CDFt_1.2.tar.gz"],"subdirs":["noarch"],"summary":"This package proposes a statistical downscaling method for cumulative distribution functions (CDF), as well as the computation of the Cram\\`er-von Mises statistics U, and the Kolmogorov-Smirnov statistics KS.","text_prefix":false,"timestamp":1588847759,"version":"1.2"},"r-cdm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/alexanderrobitzsch/CDM, https://sites.google.com/view/alexander-robitzsch/software","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CDM/CDM_8.3-14.tar.gz","https://cran.r-project.org/src/contrib/CDM_8.3-14.tar.gz"],"subdirs":["linux-64","win-64"],"summary":"Functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. This package enables the estimation of the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>), the multiple group (polytomous) GDINA model (de la Torre, 2011, <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> or Robitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>) for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as Ravand and Robitzsch (2015).","text_prefix":false,"timestamp":1773525891,"version":"8.3_14"},"r-celestial":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=celestial","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/celestial/celestial_1.5.8.tar.gz","https://cran.r-project.org/src/contrib/celestial_1.5.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.","text_prefix":false,"timestamp":1539378176,"version":"1.5.8"},"r-cellranger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rsheets/cellranger","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cellranger/cellranger_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/cellranger_1.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Helper functions to work with spreadsheets and the \"A1:D10\" style of cell range specification.","text_prefix":false,"timestamp":1536472963,"version":"1.1.0"},"r-celltrackr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.motilitylab.net","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/celltrackR/celltrackR_1.2.2.tar.gz","https://cran.r-project.org/src/contrib/celltrackR_1.2.2.tar.gz"],"subdirs":["noarch"],"summary":"Methods for analyzing (cell) motion in two or three dimensions. Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>, Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>, Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>, Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>, Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>, Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>, Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.","text_prefix":false,"timestamp":1758448259,"version":"1.2.2"},"r-cenroc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cenROC","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cenROC/cenROC_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/cenROC_2.0.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Contains functions to estimate a smoothed and a non-smoothed (empirical) time-dependent ROC curve (receiver operating characteristic curve) and the corresponding area under the ROC curve (AUC) and the optimal cutoff point for the right and interval censored survival data, as described in Beyene and El Ghouch (2019)<https://dial.uclouvain.be/pr/boreal/object/boreal:219643>.","text_prefix":false,"timestamp":1666042036,"version":"2.0.0"},"r-censored":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tidymodels/censored","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/censored/censored_0.3.5.tar.gz","https://cloud.r-project.org/src/contrib/censored_0.3.5.tar.gz"],"subdirs":["noarch"],"summary":"Engines for survival models from the 'parsnip' package. These include parametric models (e.g., Jackson (2016) <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g., Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).","text_prefix":false,"timestamp":1656088261,"version":"0.3.5"},"r-censusapi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hrecht/censusapi","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/censusapi/censusapi_0.10.0.tar.gz","https://cran.r-project.org/src/contrib/censusapi_0.10.0.tar.gz"],"subdirs":["noarch"],"summary":"A wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata. Available datasets include the Decennial Census, American Community Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, Population Estimates and Projections, and more.","text_prefix":false,"timestamp":1628061278,"version":"0.10.0"},"r-censusgeography":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=censusGeography","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/censusGeography/censusGeography_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/censusGeography_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Converts the United States Census geographic code for city, state (FIP and ICP), region, and birthplace, into the name of the region. e.g. takes an input of Census city code 5330 to it's actual city, Philadelphia. 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Serialize these object prototypes to 'JSON' so they can be used to check and coerce data in production systems, and deserialize 'JSON' back to the correct object prototypes.","text_prefix":false,"timestamp":1758193372,"version":"0.1.0"},"r-cetcolor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/coatless/cetcolor, http://thecoatlessprofessor.com/projects/cetcolor/, http://peterkovesi.com/projects/colourmaps/","license":"CC-BY-SA-4.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cetcolor/cetcolor_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/cetcolor_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Collection of perceptually uniform colour maps made by Peter Kovesi (2015) \"Good Colour Maps: How to Design Them\" <arXiv:1509.03700> at the Centre for Exploration Targeting (CET).","text_prefix":false,"timestamp":1758611540,"version":"0.2.0"},"r-cffr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ropensci/cffr","home":"https://docs.ropensci.org/cffr/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/cffr/cffr_1.4.2.tar.gz","https://cloud.r-project.org/src/contrib/cffr_1.4.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The Citation File Format version 1.2.0 <doi:10.5281/zenodo.5171937> is a human and machine readable file format which provides citation metadata for software. 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It introduces a special plot viz. the \"chromosome heatmap\" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the large size of the chromosomes, it is impractical to visualize each element on the same plot. However, the plot provides a magnified view for each of chromosome locus to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order. 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These are recorded and executed per data chunk, so large files can be processed with limited memory using the 'LaF' package.","text_prefix":false,"timestamp":1770369536,"version":"0.6.2"},"r-cicerone":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cicerone.john-coene.com/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cicerone/cicerone_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/cicerone_1.0.4.tar.gz"],"subdirs":["noarch"],"summary":"Provide step by step guided tours of 'Shiny' applications.","text_prefix":false,"timestamp":1758093599,"version":"1.0.4"},"r-ciftitools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mandymejia/ciftiTools","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ciftiTools/ciftiTools_0.21.1.tar.gz","https://cloud.r-project.org/src/contrib/ciftiTools_0.21.1.tar.gz"],"subdirs":["noarch"],"summary":"CIFTI files contain brain imaging data in \"grayordinates,\" which represent the gray matter as cortical surface vertices (left and right) and subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). 'ciftiTools' provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the \"dscalar,\" \"dlabel,\" and \"dtseries\" intents. 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Here this package provides an implementation  of circular layout generation in R as well as an enhancement of available  software. The flexibility of the package is based on the usage of low-level  graphics functions such that self-defined high-level graphics can be easily  implemented by users for specific purposes. Together with the seamless  connection between the powerful computational and visual environment in R,  it gives users more convenience and freedom to design figures for  better understanding complex patterns behind multiple dimensional data.","text_prefix":false,"timestamp":0,"version":"0.4.18"},"r-circstats":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"S-plus original at http://statweb.calpoly.edu/lund/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CircStats/CircStats_0.2-7.tar.gz","https://cran.r-project.org/src/contrib/CircStats_0.2-7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Circular Statistics, from \"Topics in circular Statistics\" (2001) S. Rao Jammalamadaka and A. 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In addition, the package contains the datasets used within the analysis of the paper.","text_prefix":false,"timestamp":1621776520,"version":"1.3.3"},"r-citation":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/pik-piam/citation","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/citation/citation_0.12.2.tar.gz","https://cran.r-project.org/src/contrib/citation_0.12.2.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (<https://citation-file-format.github.io/>), extending the functionality already provided by the citation() function in the 'utils' package.","text_prefix":false,"timestamp":1758467092,"version":"0.12.2"},"r-ckmeans.1d.dp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Ckmeans.1d.dp","license":"LGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Ckmeans.1d.dp/Ckmeans.1d.dp_4.3.6.tar.gz","https://cran.r-project.org/src/contrib/Ckmeans.1d.dp_4.3.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. 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This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility.","text_prefix":false,"timestamp":1572945503,"version":"4.3.6"},"r-clarabel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/oxfordcontrol/clarabel-r/","home":"https://oxfordcontrol.github.io/clarabel-r/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/clarabel/clarabel_0.11.3.tar.gz","https://cloud.r-project.org/src/contrib/clarabel_0.11.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"A versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (<https://clarabel.org/stable/>). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, Clarabel handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. 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This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316>. 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette \"Translating Zelig to clarify\" for replicating this functionality.","text_prefix":false,"timestamp":1781079361,"version":"0.2.3"},"r-class":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.stats.ox.ac.uk/pub/MASS4/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/class/class_7.3-24.tar.gz","https://cran.r-project.org/src/contrib/class_7.3-24.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Various functions for classification, including k-nearest neighbour, Learning Vector Quantization and Self-Organizing Maps.","text_prefix":false,"timestamp":1621623240,"version":"7.3_24"},"r-classint":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-spatial/classInt/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/classInt/classInt_0.4-11.tar.gz","https://cran.r-project.org/src/contrib/classInt_0.4-11.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.","text_prefix":false,"timestamp":1665502253,"version":"0.4_11"},"r-cleanr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://gitlab.com/fvafrcu/cleanr","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cleanr/cleanr_1.4.0.tar.gz","https://cran.r-project.org/src/contrib/cleanr_1.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Check your R code for some of the most common layout flaws.  Many tried to teach us how to write code less dreadful, be it implicitly as B. W. Kernighan and D. M. Ritchie (1988) <ISBN:0-13-110362-8> in 'The C Programming Language' did, be it explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in 'Clean Code: A Handbook of Agile Software Craftsmanship' did.  So we should check our code for files too long or wide, functions with too many lines, too wide lines, too many arguments or too many levels of nesting. Note: This is not a static code analyzer like pylint or the like. Checkout <https://cran.r-project.org/package=lintr> instead.","text_prefix":false,"timestamp":1758564082,"version":"1.4.0"},"r-cleanrmd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pkg.garrickadenbuie.com/cleanrmd/, https://github.com/gadenbuie/cleanrmd","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/cleanrmd/cleanrmd_0.2.0.tar.gz","https://cloud.r-project.org/src/contrib/cleanrmd_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"A collection of clean 'R Markdown' HTML document templates using classy-looking classless CSS styles. 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It also contains a number of lower level 'CLI' elements: rules, boxes, trees, and 'Unicode' symbols with 'ASCII' alternatives. It support ANSI colors and text styles as well.","text_prefix":false,"timestamp":1662645720,"version":"3.6.6"},"r-cliapp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/cliapp#readme","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cliapp/cliapp_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/cliapp_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Create rich command line applications, with colors, headings, lists, alerts, progress bars, etc. It uses CSS for custom 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Further details on the extreme climate indices can be found at <http://etccdi.pacificclimate.org/list_27_indices.shtml> and in the package manual.","text_prefix":false,"timestamp":1757678488,"version":"1.1_11"},"r-climprojdiags":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://earth.bsc.es/gitlab/es/ClimProjDiags","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ClimProjDiags/ClimProjDiags_0.3.6.tar.gz","https://cran.r-project.org/src/contrib/ClimProjDiags_0.3.6.tar.gz"],"subdirs":["noarch"],"summary":"Set of tools to compute metrics and indices for climate analysis. The package provides functions to compute extreme indices, evaluate the agreement between models and combine theses models into an ensemble. Multi-model time series of climate indices can be computed either after averaging the 2-D fields from different models provided they share a common grid or by combining time series computed on the model native grid. Indices can be assigned weights and/or combined to construct new indices.","text_prefix":false,"timestamp":1665378065,"version":"0.3.6"},"r-clinfun":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clinfun","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/clinfun/clinfun_1.1.6.tar.gz","https://cloud.r-project.org/src/contrib/clinfun_1.1.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Utilities to make your clinical collaborations easier if not fun. It contains functions for designing studies such as Simon 2-stage and group sequential designs and for data analysis such as Jonckheere-Terpstra test and estimating survival quantiles.","text_prefix":false,"timestamp":1665824060,"version":"1.1.6"},"r-clinify":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://atorus-research.github.io/clinify/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clinify/clinify_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/clinify_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"The primary motivation of this package is to take the things that are great about the R packages 'flextable' <https://davidgohel.github.io/flextable/> and 'officer' <https://davidgohel.github.io/officer/>, take the standard and complex pieces of formatting clinical tables for regulatory use, and simplify the tedious pieces.","text_prefix":false,"timestamp":1787126100,"version":"0.4.0"},"r-clintrialx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.indraneelchakraborty.com/clintrialx/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clintrialx/clintrialx_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/clintrialx_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Are you spending too much time fetching and managing clinical trial data? Struggling with complex queries and bulk data extraction? What if you could simplify this process with just a few lines of code? Introducing 'clintrialx' - Fetch clinical trial data from sources like 'ClinicalTrials.gov' <https://clinicaltrials.gov/> and the 'Clinical Trials Transformation Initiative - Access to Aggregate Content of ClinicalTrials.gov' database <https://aact.ctti-clinicaltrials.org/>, supporting pagination and bulk downloads. Also, you can generate HTML reports based on the data obtained from the sources!","text_prefix":false,"timestamp":1759416904,"version":"0.1.1"},"r-clipr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mdlincoln/clipr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clipr/clipr_0.8.1.tar.gz","https://cran.r-project.org/src/contrib/clipr_0.8.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Simple utility functions to read from and write to the Windows, OS X, and X11 clipboards.","text_prefix":false,"timestamp":1537031634,"version":"0.8.1"},"r-clisymbols":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gaborcsardi/clisymbols","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clisymbols/clisymbols_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/clisymbols_1.2.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A small subset of Unicode symbols, that are useful when building command line applications. They fall back to alternatives on terminals that do not support Unicode. Many symbols were taken from the 'figures' 'npm' package (see <https://github.com/sindresorhus/figures>).","text_prefix":false,"timestamp":1535632558,"version":"1.2.0"},"r-clock":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://clock.r-lib.org, https://github.com/r-lib/clock","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clock/clock_0.7.4.tar.gz","https://cran.r-project.org/src/contrib/clock_0.7.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides a comprehensive library for date-time manipulations using a new family of orthogonal date-time classes (durations, time points, zoned-times, and calendars) that partition responsibilities so that the complexities of time zones are only considered when they are really needed. Capabilities include: date-time parsing, formatting, arithmetic, extraction and updating of components, and rounding.","text_prefix":false,"timestamp":1635521597,"version":"0.7.4"},"r-cloudos":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/lifebit-ai/cloudos","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cloudos/cloudos_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/cloudos_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"The 'CloudOS' client library for R makes it easy to interact with CloudOS <https://cloudos.lifebit.ai/> in the R environment for analysis.","text_prefix":false,"timestamp":1635278002,"version":"0.4.0"},"r-clue":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clue","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clue/clue_0.3-68.tar.gz","https://cran.r-project.org/src/contrib/clue_0.3-68.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"CLUster Ensembles.","text_prefix":false,"timestamp":1666111894,"version":"0.3_68"},"r-clues":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clues","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clues/clues_0.6.2.2.tar.gz","https://cran.r-project.org/src/contrib/clues_0.6.2.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"We developed the clues R package to provide functions  for automatically estimating the number of clusters and  getting the final cluster partition without any input  parameter except the stopping rule for convergence.  The package also provides functions to evaluate and compare the performances of partitions of a data set both numerically and graphically.","text_prefix":false,"timestamp":1570991510,"version":"0.6.2.2"},"r-clustassess":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Core-Bioinformatics/ClustAssess","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ClustAssess/ClustAssess_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/ClustAssess_1.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.","text_prefix":false,"timestamp":1758727073,"version":"1.1.0"},"r-clustcurv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/noramvillanueva/clustcurv","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clustcurv/clustcurv_3.0.1.tar.gz","https://cran.r-project.org/src/contrib/clustcurv_3.0.1.tar.gz"],"subdirs":["noarch"],"summary":"A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.","text_prefix":false,"timestamp":1765023000,"version":"3.0.1"},"r-cluster":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://svn.r-project.org/R-packages/trunk/cluster","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cluster/cluster_2.1.8.3.tar.gz","https://cran.r-project.org/src/contrib/cluster_2.1.8.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Methods for Cluster analysis.  Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) \"Finding Groups in Data\".","text_prefix":false,"timestamp":1554538678,"version":"2.1.8.3"},"r-clustercons":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/biomedicalinformaticsgroup/clusterCons","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clusterCons/clusterCons_1.2.tar.gz","https://cran.r-project.org/src/contrib/clusterCons_1.2.tar.gz"],"subdirs":["noarch"],"summary":"Functions for calculation of robustness measures for clusters and cluster membership based on generating consensus matrices from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime. The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take. See Simpson, T. I., Armstrong, J. D. & Jarman, A. P. (2010) <doi:10.1186/1471-2105-11-590> and Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003) <doi:10.1023/a:1023949509487>.","text_prefix":false,"timestamp":1758218838,"version":"1.2"},"r-clustercrit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http:www.r-project.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clusterCrit/clusterCrit_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/clusterCrit_1.3.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Compute clustering validation indices.","text_prefix":false,"timestamp":1758097954,"version":"1.3.0"},"r-clustergeneration":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clusterGeneration","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clusterGeneration/clusterGeneration_1.3.8.tar.gz","https://cran.r-project.org/src/contrib/clusterGeneration_1.3.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"We developed the clusterGeneration package to provide functions  for generating random clusters, generating random  covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters.  The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.","text_prefix":false,"timestamp":1539397744,"version":"1.3.8"},"r-clustermq":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/mschubert/clustermq","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clustermq/clustermq_0.10.1.tar.gz","https://cran.r-project.org/src/contrib/clustermq_0.10.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","noarch","osx-64","osx-arm64","win-64"],"summary":"Evaluate arbitrary function calls using workers on HPC schedulers in single line of code. All processing is done on the network without accessing the file system. Remote schedulers are supported via SSH.","text_prefix":false,"timestamp":1532807727,"version":"0.10.1"},"r-clusterr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/mlampros/ClusterR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ClusterR/ClusterR_1.3.7.tar.gz","https://cloud.r-project.org/src/contrib/ClusterR_1.3.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) \"Clustering in an Object-Oriented Environment\" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) \"Web-scale k-means clustering\" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) \"Armadillo: a template-based C++ library for linear algebra\" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) \"Clustering by Passing Messages Between Data Points\" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.","text_prefix":false,"timestamp":1546615341,"version":"1.3.7"},"r-clusterrepro":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.ncbi.nlm.nih.gov/pubmed/16613834.","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clusterRepro/clusterRepro_0.9.tar.gz","https://cran.r-project.org/src/contrib/clusterRepro_0.9.tar.gz"],"subdirs":["noarch"],"summary":"This is a function for validating microarray clusters via reproducibility, based on the paper referenced below.","text_prefix":false,"timestamp":1758706018,"version":"0.9"},"r-clusterses":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clusterSEs","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clusterSEs/clusterSEs_2.6.6.tar.gz","https://cran.r-project.org/src/contrib/clusterSEs_2.6.6.tar.gz"],"subdirs":["noarch"],"summary":"Calculate p-values and confidence intervals using cluster-adjusted t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.","text_prefix":false,"timestamp":1588840760,"version":"2.6.6"},"r-clustersim":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://keii.ue.wroc.pl/clusterSim","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clusterSim/clusterSim_0.51-6.tar.gz","https://cran.r-project.org/src/contrib/clusterSim_0.51-6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Distance measures (GDM1, GDM2,\tSokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,\tDavies-Bouldin),\tdata normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,\treplication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data).  (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>,  HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>,  RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,  JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>,  MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>,  JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>,  DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>,  CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>, HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>,  TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>,  BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>,  WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).","text_prefix":false,"timestamp":1554975656,"version":"0.51_6"},"r-clusteval":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clusteval","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clusteval/clusteval_0.1.tar.gz","https://cran.r-project.org/src/contrib/clusteval_0.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An R package that provides a suite of tools to evaluate clustering algorithms, clusterings, and individual clusters.","text_prefix":false,"timestamp":1758361293,"version":"0.1"},"r-clustimpute":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ClustImpute","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ClustImpute/ClustImpute_0.2.4.tar.gz","https://cran.r-project.org/src/contrib/ClustImpute_0.2.4.tar.gz"],"subdirs":["noarch"],"summary":"This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.","text_prefix":false,"timestamp":1607791388,"version":"0.2.4"},"r-clustmixtype":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clustMixType","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clustMixType/clustMixType_0.5-2.tar.gz","https://cran.r-project.org/src/contrib/clustMixType_0.5-2.tar.gz"],"subdirs":["noarch"],"summary":"Functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304 and G.Szepannek et al. (2025): Clustering Large Mixed-Type Data with Ordinal Variables, Adv Data Anal Classif 19, 749\u2013767.","text_prefix":false,"timestamp":1782814946,"version":"0.5_2"},"r-clustofvar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ClustOfVar","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ClustOfVar/ClustOfVar_1.2.tar.gz","https://cran.r-project.org/src/contrib/ClustOfVar_1.2.tar.gz"],"subdirs":["noarch"],"summary":"Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.","text_prefix":false,"timestamp":1758245720,"version":"1.2"},"r-clustree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/lazappi/clustree","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clustree/clustree_0.5.1.tar.gz","https://cran.r-project.org/src/contrib/clustree_0.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases.","text_prefix":false,"timestamp":1636380798,"version":"0.5.1"},"r-clustvarsel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clustvarsel","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clustvarsel/clustvarsel_2.3.5.tar.gz","https://cran.r-project.org/src/contrib/clustvarsel_2.3.5.tar.gz"],"subdirs":["noarch"],"summary":"Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.","text_prefix":false,"timestamp":1588840434,"version":"2.3.5"},"r-clv":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=clv","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clv/clv_0.3-2.5.tar.gz","https://cran.r-project.org/src/contrib/clv_0.3-2.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs produced by functions coming from package \"cluster\". Package contains also functions and examples of usage for cluster stability approach that might be applied to algorithms implemented in \"cluster\" package as well as user defined clustering algorithms.","text_prefix":false,"timestamp":1564956252,"version":"0.3_2.5"},"r-clvalid":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://guybrock.gpbrock.net/research","license":"LGPL-3-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/clValid/clValid_0.7.tar.gz","https://cran.r-project.org/src/contrib/clValid_0.7.tar.gz"],"subdirs":["noarch"],"summary":"Statistical and biological validation of clustering results.","text_prefix":false,"timestamp":1588961565,"version":"0.7"},"r-cmdfun":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://snystrom.github.io/cmdfun/, https://github.com/snystrom/cmdfun","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cmdfun/cmdfun_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/cmdfun_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Writing interfaces to command line software is cumbersome. 'cmdfun' provides a framework for building function calls to seamlessly interface with shell commands by allowing lazy evaluation of command line arguments. 'cmdfun' also provides methods for handling user-specific paths to tool installs or secrets like API keys. Its focus is to equally serve package builders who wish to wrap command line software, and to help analysts stay inside R when they might usually leave to execute non-R software.","text_prefix":false,"timestamp":1758600923,"version":"1.0.2"},"r-cmdstanr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/stan-dev/cmdstanr","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/stan-dev/cmdstanr/archive/v0.9.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"CmdStanR is a lightweight interface to Stan for R users","text_prefix":false,"timestamp":1704796081,"version":"0.9.0"},"r-cmls":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CMLS","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CMLS/CMLS_1.1.tar.gz","https://cran.r-project.org/src/contrib/CMLS_1.1.tar.gz"],"subdirs":["noarch"],"summary":"Solves multivariate least squares (MLS) problems subject to constraints on the coefficients, e.g., non-negativity, orthogonality, equality, inequality, monotonicity, unimodality, smoothness, etc. Includes flexible functions for solving MLS problems subject to user-specified equality and/or inequality constraints, as well as a wrapper function that implements 24 common constraint options. Also does k-fold or generalized cross-validation to tune constraint options for MLS problems. See ten Berge (1993, ISBN:9789066950832) for an overview of MLS problems, and see Goldfarb and Idnani (1983) <doi:10.1007/BF02591962> for a discussion of the underlying quadratic programming algorithm.","text_prefix":false,"timestamp":1665590244,"version":"1.1"},"r-cmm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cmm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cmm/cmm_1.0.tar.gz","https://cran.r-project.org/src/contrib/cmm_1.0.tar.gz"],"subdirs":["noarch"],"summary":"Quite extensive package for maximum likelihood estimation and weighted least squares estimation of categorical marginal models (CMMs; e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?; Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>.","text_prefix":false,"timestamp":1757977946,"version":"1.0"},"r-cmplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/YinLiLin/R-CMplot","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CMplot/CMplot_4.5.1.tar.gz","https://cran.r-project.org/src/contrib/CMplot_4.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Manhattan plot, a type of scatter plot, was widely used to display the association results. However, it is usually time-consuming and laborious for a non-specialist user to write scripts and adjust parameters of an elaborate plot. Moreover, the ever-growing traits measured have necessitated the integration of results from different Genome-wide association study researches. Circle Manhattan Plot is the first open R package that can lay out. Genome-wide association study P-value results in both traditional rectangular patterns, QQ-plot and novel circular ones. United in only one bull's eye style plot, association results from multiple traits can be compared interactively, thereby to reveal both similarities and differences between signals. Additional functions include: highlight signals, a group of SNPs, chromosome visualization and candidate genes around SNPs.","text_prefix":false,"timestamp":1653380969,"version":"4.5.1"},"r-cmprsk":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.R-project.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cmprsk/cmprsk_2.2-12.tar.gz","https://cran.r-project.org/src/contrib/cmprsk_2.2-12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154 <DOI:10.1214/aos/1176350951>, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509, <DOI:10.1080/01621459.1999.10474144>.","text_prefix":false,"timestamp":1621827702,"version":"2.2_12"},"r-cmprskcoxmsm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cmprskcoxmsm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cmprskcoxmsm/cmprskcoxmsm_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/cmprskcoxmsm_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Uses inverse probability weighting methods to estimate treatment effect under marginal structure model for the cause-specific hazard of competing risk events. Estimates also the cumulative incidence function (i.e. risk) of the potential outcomes, and provides inference on risk difference and risk ratio. Reference: Kalbfleisch & Prentice (2002)<doi:10.1002/9781118032985>; Hernan et al (2001)<doi:10.1198/016214501753168154>.","text_prefix":false,"timestamp":1758270948,"version":"0.2.1"},"r-cnogpro":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CNOGpro","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CNOGpro/CNOGpro_1.1.tar.gz","https://cran.r-project.org/src/contrib/CNOGpro_1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Methods for assigning copy number states and breakpoints in resequencing experiments of prokaryotic organisms.","text_prefix":false,"timestamp":0,"version":"1.1"},"r-cnorm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.psychometrica.de/cNorm_en.html, https://github.com/WLenhard/cNORM","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cNORM/cNORM_3.6.2.tar.gz","https://cran.r-project.org/src/contrib/cNORM_3.6.2.tar.gz"],"subdirs":["noarch"],"summary":"Conventional methods for producing standard scores in psychometrics or biometrics are often plagued with \"jumps\" or \"gaps\" (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores. The continuous norming method introduced by A. Lenhard et al. (2016, <doi:10.1177/1073191116656437>; 2019, <doi:10.1371/journal.pone.0222279>) and generates continuous test norm scores on the basis of the raw data from standardization samples, without requiring assumptions about the distribution of the raw data: Norm scores are directly established from raw data by modeling the latter ones as a function of both percentile scores and an explanatory variable (e.g., age). The method minimizes bias arising from sampling and measurement error, while handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost all of the variance in the original norm data sample. An online demonstration is available via <https://cnorm.shinyapps.io/cNORM/>.","text_prefix":false,"timestamp":1665814650,"version":"3.6.2"},"r-cobalt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ngreifer/cobalt","home":"https://ngreifer.github.io/cobalt/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/cobalt/cobalt_5.0.0.tar.gz","https://cloud.r-project.org/src/contrib/cobalt_5.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'WeightIt', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with longitudinal treatments.","text_prefix":false,"timestamp":1667521249,"version":"5.0.0"},"r-cobs":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://curves-etc.r-forge.r-project.org/ http://r-forge.r-project.org/R/?group_id=846","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cobs/cobs_1.3-9-1.tar.gz","https://cran.r-project.org/src/contrib/cobs_1.3-9-1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Qualitatively Constrained (Regression) Smoothing Splines via Linear Programming and Sparse Matrices.","text_prefix":false,"timestamp":1563726704,"version":"1.3_9_1"},"r-cocor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://comparingcorrelations.org","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cocor/cocor_1.1-4.tar.gz","https://cran.r-project.org/src/contrib/cocor_1.1-4.tar.gz"],"subdirs":["noarch"],"summary":"Statistical tests for the comparison between two correlations based on either independent or dependent groups. Dependent correlations can either be overlapping or nonoverlapping. A web interface is available on the website http://comparingcorrelations.org. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from https://rkward.kde.org to use this feature. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard.","text_prefix":false,"timestamp":1626013133,"version":"1.1_4"},"r-coda":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=coda","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coda/coda_0.19-4.1.tar.gz","https://cran.r-project.org/src/contrib/coda_0.19-4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain.","text_prefix":false,"timestamp":1536447992,"version":"0.19_4.1"},"r-codemeta":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/cboettig/codemeta","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/codemeta/codemeta_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/codemeta_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"The 'Codemeta' Project defines a 'JSON-LD' format for describing software metadata, as detailed at <https://codemeta.github.io>. This package provides core utilities to generate this metadata with a minimum of dependencies.","text_prefix":false,"timestamp":1757947096,"version":"0.1.1"},"r-codemetar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/codemetar, https://ropensci.github.io/codemetar","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/codemetar/codemetar_0.3.7.tar.gz","https://cran.r-project.org/src/contrib/codemetar_0.3.7.tar.gz"],"subdirs":["noarch"],"summary":"The 'Codemeta' Project defines a 'JSON-LD' format for describing software metadata, as detailed at <https://codemeta.github.io>. This package provides utilities to generate, parse, and modify 'codemeta.json' files automatically for R packages, as well as tools and examples for working with 'codemeta.json' 'JSON-LD' more generally.","text_prefix":false,"timestamp":1588838304,"version":"0.3.7"},"r-codetools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=codetools","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/codetools/codetools_0.2-20.tar.gz","https://cran.r-project.org/src/contrib/codetools_0.2-20.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Code analysis tools for R.","text_prefix":false,"timestamp":1536444990,"version":"0.2_20"},"r-codingmatrices":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=codingMatrices","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/codingMatrices/codingMatrices_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/codingMatrices_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"A collection of coding functions as alternatives to the standard functions in the stats package, which have names starting with 'contr.'.  Their main advantage is that they provide a consistent method for defining marginal effects in factorial models.  In a simple one-way ANOVA model the intercept term is always the simple average of the class means.","text_prefix":false,"timestamp":1773519839,"version":"0.4.0"},"r-coenocliner":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gavinsimpson/coenocliner/","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coenocliner/coenocliner_0.2-4.tar.gz","https://cran.r-project.org/src/contrib/coenocliner_0.2-4.tar.gz"],"subdirs":["noarch"],"summary":"Simulate species occurrence and abundances (counts) along gradients.","text_prefix":false,"timestamp":1588838183,"version":"0.2_4"},"r-coin":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://codeberg.org/thothorn/coin/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coin/coin_1.4-5.tar.gz","https://cran.r-project.org/src/contrib/coin_1.4-5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in <doi:10.18637/jss.v028.i08>.","text_prefix":false,"timestamp":1588553370,"version":"1.4_5"},"r-collapse":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SebKrantz/collapse","home":"https://sebkrantz.github.io/collapse/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/collapse/collapse_2.1.7.tar.gz","https://cran.r-project.org/src/contrib/collapse_2.1.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, and programmer friendly through a flexible, parsimonious, and robust API. Core functionality includes a rich set of S3 generic grouped and weighted   statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations and OpenMP multithreading (reducing the   need for iteration/vectorization in R), and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable   from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further   includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast   data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data.   It is well integrated with basic R, 'dplyr'/'tibble', 'data.table', 'sf', 'plm' (panel-series and data frames), and accommodates (by virtue of smart internal   attribute handling) many other classes (like 'ts', 'xts'/'zoo' or 'tsibble').","text_prefix":false,"timestamp":1660576571,"version":"2.1.7"},"r-collapsibletree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/AdeelK93/collapsibleTree, https://AdeelK93.github.io/collapsibleTree/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/collapsibleTree/collapsibleTree_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/collapsibleTree_0.1.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it. Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame. See 'collapsibleTree' website for more information and examples.","text_prefix":false,"timestamp":1541091502,"version":"0.1.8"},"r-collateral":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://rensa.co/collateral/index.html, https://github.com/rensa/collateral","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/collateral/collateral_0.5.2.tar.gz","https://cran.r-project.org/src/contrib/collateral_0.5.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The purrr package allows you to capture the side effects (errors, warning, messages and other output) of functions using safely() and quietly(). Using collateral, you can quickly see which elements of a list (or list-column) returned results, which threw errors and which returned warnings or other output.","text_prefix":false,"timestamp":1543257437,"version":"0.5.2"},"r-collections":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://randy3k.github.io/collections","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/collections/collections_0.3.12.tar.gz","https://cran.r-project.org/src/contrib/collections_0.3.12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides high performance container data types such as Queue, Stack, Deque, Dict and OrderedDict. Benchmarks <https://randy3k.github.io/collections/articles/benchmark.html> have shown that these containers are asymptotically more efficient than those offered by other packages.","text_prefix":false,"timestamp":1666242281,"version":"0.3.12"},"r-coloc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/chr1swallace/coloc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coloc/coloc_5.2.3.tar.gz","https://cran.r-project.org/src/contrib/coloc_5.2.3.tar.gz"],"subdirs":["noarch"],"summary":"Performs the colocalisation tests described in Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>, Wallace (2020) <doi:10.1371/journal.pgen.1008720>, Wallace (2021) <doi:10.1371/journal.pgen.1009440>.","text_prefix":false,"timestamp":1758319991,"version":"5.2.3"},"r-colorblindness":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=colorBlindness","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/colorBlindness/colorBlindness_0.1.9.tar.gz","https://cran.r-project.org/src/contrib/colorBlindness_0.1.9.tar.gz"],"subdirs":["noarch"],"summary":"Provide the safe color set for color blindness, the simulator of protanopia, deuteranopia. The color sets are collected from: Wong, B. (2011) <doi:10.1038/nmeth.1618>, and <http://mkweb.bcgsc.ca/biovis2012/>. The simulations of the appearance of the colors to color-deficient viewers were based on algorithms in Vienot, F., Brettel, H. and Mollon, J.D. (1999) <doi:10.1002/(SICI)1520-6378(199908)24:4%3C243::AID-COL5%3E3.0.CO;2-3>. The cvdPlot() function to generate 'ggplot' grobs of simulations were modified from <https://github.com/clauswilke/colorblindr>.","text_prefix":false,"timestamp":1758565060,"version":"0.1.9"},"r-colordf":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://january3.github.io/colorDF/, https://github.com/january3/colorDF/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/colorDF/colorDF_0.1.7.tar.gz","https://cran.r-project.org/src/contrib/colorDF_0.1.7.tar.gz"],"subdirs":["noarch"],"summary":"Colorful Data Frames in the terminal. The new class does change the behaviour of any of the objects, but adds a style definition and a print method. Using ANSI escape codes, it colors the terminal output of data frames. Some column types (such as p-values and identifiers) are automatically recognized.","text_prefix":false,"timestamp":1664116554,"version":"0.1.7"},"r-colorramps":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=colorRamps","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/colorRamps/colorRamps_2.3.4.tar.gz","https://cran.r-project.org/src/contrib/colorRamps_2.3.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Builds gradient color maps","text_prefix":false,"timestamp":1539220232,"version":"2.3.4"},"r-colorspace":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://colorspace.R-Forge.R-project.org, http://hclwizard.org/","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/colorspace/colorspace_2.1-3.tar.gz","https://cran.r-project.org/src/contrib/colorspace_2.1-3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at <https://colorspace.R-Forge.R-project.org/> and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, <doi:10.18637/jss.v096.i01>).","text_prefix":false,"timestamp":1665107335,"version":"2.1_3"},"r-colortools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/colortools/index.html","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/colortools/colortools_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/colortools_0.1.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"R package with handy functions to help users select and play with color schemes in an HSV color model","text_prefix":false,"timestamp":0,"version":"0.1.5"},"r-colourpicker":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/daattali/colourpicker","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/colourpicker/colourpicker_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/colourpicker_1.3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A colour picker that can be used as an input in Shiny apps or Rmarkdown documents. The colour picker supports alpha opacity, custom colour palettes, and many more options. A Plot Colour Helper tool is available as an RStudio Addin, which helps you pick colours to use in your plots. A more generic Colour Picker RStudio Addin is also provided to let  you select colours to use in your R code.","text_prefix":false,"timestamp":1538358675,"version":"1.3.0"},"r-colourvalues":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://symbolixau.github.io/colourvalues/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/colourvalues/colourvalues_0.3.11.tar.gz","https://cran.r-project.org/src/contrib/colourvalues_0.3.11.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by St\u00e9fan van der Walt and Nathaniel Smith, and were set as the default palette for the 'Python' 'Matplotlib' library <https://matplotlib.org/>. Other palettes available in this library have been derived from 'RColorBrewer' <https://CRAN.R-project.org/package=RColorBrewer> and 'colorspace' <https://CRAN.R-project.org/package=colorspace> packages.","text_prefix":false,"timestamp":1665302538,"version":"0.3.11"},"r-cols4all":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://mtennekes.github.io/cols4all/, https://github.com/mtennekes/cols4all","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cols4all/cols4all_0.10.tar.gz","https://cran.r-project.org/src/contrib/cols4all_0.10.tar.gz"],"subdirs":["noarch"],"summary":"Color palettes for all people, including those with color vision deficiency. Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes cyclic and bivariate color palettes. Furthermore, a color for missing values is assigned to each palette.","text_prefix":false,"timestamp":1758722450,"version":"0.10"},"r-combinat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=combinat","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/combinat/combinat_0.0-8.tar.gz","https://cran.r-project.org/src/contrib/combinat_0.0-8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"routines for combinatorics","text_prefix":false,"timestamp":1539297974,"version":"0.0_8"},"r-combinepvalue":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CombinePValue","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CombinePValue/CombinePValue_1.0.tar.gz","https://cran.r-project.org/src/contrib/CombinePValue_1.0.tar.gz"],"subdirs":["noarch"],"summary":"We offer two statistical tests to combine p-values: selfcontained.test vs competitive.test. The goal is to test whether a vector of pvalues are jointly significant when we combine them together.","text_prefix":false,"timestamp":1758263402,"version":"1.0"},"r-cometexacttest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/raphael-group/comet","home":"https://raphael-group.github.io/projects/comet/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cometExactTest/cometExactTest_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/cometExactTest_0.1.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test.","text_prefix":false,"timestamp":1758712420,"version":"0.1.5"},"r-comics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://dcq.tau.ac.il/ , http://csgi.tau.ac.il/VoCAL/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ComICS/ComICS_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/ComICS_1.0.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.","text_prefix":false,"timestamp":1758533968,"version":"1.0.4"},"r-common":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/dbosak01/common/","home":"https://common.r-sassy.org","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/common/common_1.1.5.tar.gz","https://cran.r-project.org/src/contrib/common_1.1.5.tar.gz"],"subdirs":["noarch"],"summary":"Contains functions for solving commonly encountered problems while programming in R. This package is intended to provide a lightweight supplement to Base R, and will be useful for almost any R user.","text_prefix":false,"timestamp":1771855384,"version":"1.1.5"},"r-commonmark":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://github.com/jeroen/commonmark (devel) https://github.github.com/gfm/ (spec)","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/commonmark/commonmark_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/commonmark_2.0.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The CommonMark specification defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text.","text_prefix":false,"timestamp":1665772684,"version":"2.0.0"},"r-comorbidity":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ellessenne/comorbidity/","home":"https://ellessenne.github.io/comorbidity/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/comorbidity/comorbidity_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/comorbidity_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Computing comorbidity indices and scores such as the weighted Charlson score (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>) using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>). Australian and Swedish modifications of the Charlson Comorbidity Index are available as well (Sundararajan, 2004 <doi:10.1016/j.jclinepi.2004.03.012> and Ludvigsson, 2021 <doi:10.2147/CLEP.S282475>).","text_prefix":false,"timestamp":1758470817,"version":"1.1.0"},"r-compare":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=compare","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/compare/compare_0.2-6.tar.gz","https://cran.r-project.org/src/contrib/compare_0.2-6.tar.gz"],"subdirs":["noarch"],"summary":"Functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical.","text_prefix":false,"timestamp":1758369729,"version":"0.2_6"},"r-comparegroups":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"www.comparegroups.eu","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/compareGroups/compareGroups_4.10.4.tar.gz","https://cloud.r-project.org/src/contrib/compareGroups_4.10.4.tar.gz"],"subdirs":["noarch"],"summary":"Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.","text_prefix":false,"timestamp":1598356770,"version":"4.10.4"},"r-complexupset":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/krassowski/complex-upset, https://krassowski.github.io/complex-upset/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ComplexUpset/ComplexUpset_1.3.3.tar.gz","https://cran.r-project.org/src/contrib/ComplexUpset_1.3.3.tar.gz"],"subdirs":["noarch"],"summary":"UpSet plots are an improvement over Venn Diagram for set overlap visualizations. Striving to bring the best of the 'UpSetR' and 'ggplot2', this package offers a way to create complex overlap visualisations, using simple and familiar tools, i.e. geoms of 'ggplot2'. For introduction to UpSet concept, see Lex et al. (2014) <doi:10.1109/TVCG.2014.2346248>.","text_prefix":false,"timestamp":1622087774,"version":"1.3.3"},"r-compositions":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.stat.boogaart.de/compositions","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/compositions/compositions_2.0-9.tar.gz","https://cran.r-project.org/src/contrib/compositions_2.0-9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.","text_prefix":false,"timestamp":1577146535,"version":"2.0_9"},"r-compquadform":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CompQuadForm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CompQuadForm/CompQuadForm_1.4.4.tar.gz","https://cran.r-project.org/src/contrib/CompQuadForm_1.4.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.","text_prefix":false,"timestamp":1757760246,"version":"1.4.4"},"r-comprehenr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gdemin/comprehenr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/comprehenr/comprehenr_0.6.10.tar.gz","https://cran.r-project.org/src/contrib/comprehenr_0.6.10.tar.gz"],"subdirs":["noarch"],"summary":"Provides 'Python'-style list comprehensions. List comprehension expressions use usual loops (for(), while() and repeat()) and usual if() as list producers. In many cases it gives more concise notation than standard \"*apply + filter\" strategy.","text_prefix":false,"timestamp":1773522959,"version":"0.6.10"},"r-compute.es":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://acdelre.github.io","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/compute.es/compute.es_0.2-6.tar.gz","https://cran.r-project.org/src/contrib/compute.es_0.2-6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Several functions are available for calculating the most widely used effect sizes (ES), along with their variances, confidence intervals and p-values.  The output includes ES's of d (mean difference), g (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and OR (odds ratio and log odds ratio). In addition, NNT (number needed to treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are computed. This package uses recommended formulas as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). A free web application is available at <https://acdelre.github.io/apps/compute_es/>.","text_prefix":false,"timestamp":0,"version":"0.2_6"},"r-concaveman":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.github.com/joelgombin/concaveman","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/concaveman/concaveman_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/concaveman_1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"The concaveman function ports the 'concaveman' (<https://github.com/mapbox/concaveman>) library from 'mapbox'. It computes the concave polygon(s) for one or several set of points.","text_prefix":false,"timestamp":1588835968,"version":"1.2.0"},"r-condformat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/zeehio/condformat","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/condformat/condformat_0.11.0.tar.gz","https://cran.r-project.org/src/contrib/condformat_0.11.0.tar.gz"],"subdirs":["noarch"],"summary":"Apply and visualize conditional formatting to data frames in R. It renders a data frame with cells formatted according to criteria defined by rules, using a tidy evaluation syntax. The table is printed either opening a web browser or within the 'RStudio' viewer if available. The conditional formatting rules allow to highlight cells matching a condition or add a gradient background to a given column. This package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and exporting to an 'xlsx' file.","text_prefix":false,"timestamp":1665871822,"version":"0.11.0"},"r-conditionz":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropenscilabs/conditionz","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/conditionz/conditionz_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/conditionz_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides ability to control how many times in function calls conditions are thrown (shown to the user). Includes control of warnings and messages.","text_prefix":false,"timestamp":1757638451,"version":"0.1.0"},"r-condsurv":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=condSURV","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/condSURV/condSURV_2.0.4.tar.gz","https://cran.r-project.org/src/contrib/condSURV_2.0.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Method to implement some newly developed methods for the estimation of the conditional survival function.","text_prefix":false,"timestamp":1636366481,"version":"2.0.4"},"r-condvis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"http://markajoc.github.io/condvis/","home":"https://github.com/markajoc/condvis/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/condvis/condvis_0.5-2.tar.gz","https://cran.r-project.org/src/contrib/condvis_0.5-2.tar.gz"],"subdirs":["noarch"],"summary":"Exploring fitted models by interactively taking 2-D and 3-D sections in data space.","text_prefix":false,"timestamp":1774623004,"version":"0.5_2"},"r-conf.design":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=conf.design","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/conf.design/conf.design_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/conf.design_2.0.0.tar.gz"],"subdirs":["noarch"],"summary":"This small library contains a series of simple tools for constructing and manipulating confounded and fractional factorial designs.","text_prefix":false,"timestamp":1757874659,"version":"2.0.0"},"r-config":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/config","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/config/config_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/config_0.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value.","text_prefix":false,"timestamp":0,"version":"0.3.2"},"r-configr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Miachol/configr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/configr/configr_0.3.6.tar.gz","https://cloud.r-project.org/src/contrib/configr_0.3.6.tar.gz"],"subdirs":["noarch"],"summary":"Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'.","text_prefix":false,"timestamp":1791879636,"version":"0.3.6"},"r-confintr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mayer79/confintr","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/confintr/confintr_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/confintr_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (incl. median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. 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Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.","text_prefix":false,"timestamp":1665592901,"version":"1.1_7"},"r-coranking":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/gdkrmr/coRanking","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coRanking/coRanking_0.2.5.tar.gz","https://cran.r-project.org/src/contrib/coRanking_0.2.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Calculates the co-ranking matrix to assess the quality of a dimensionality reduction.","text_prefix":false,"timestamp":1665059682,"version":"0.2.5"},"r-corbi":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/wulingyun/Corbi","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Corbi/Corbi_0.6-2.tar.gz","https://cran.r-project.org/src/contrib/Corbi_0.6-2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides a bundle of basic and fundamental bioinformatics tools, such as network querying and alignment, subnetwork extraction and search, network biomarker identification.","text_prefix":false,"timestamp":1592077824,"version":"0.6_2"},"r-coro":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/coro","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coro/coro_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/coro_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides 'coroutines' for R, a family of functions that can be suspended and resumed later on. 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The details of the method are explained in Schafer and Strimmer (2005)  <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007)  <DOI:10.2202/1544-6115.1252>.  The approach is both computationally as well as statistically very efficient, it is applicable to \"small n, large p\" data,  and always returns a positive definite and well-conditioned covariance matrix.   In addition to inferring the covariance matrix the package also provides  shrinkage estimators for partial correlations and partial variances.   The inverse of the covariance and correlation matrix  can be efficiently computed, as well as any arbitrary power of the  shrinkage correlation matrix.  Furthermore, functions are available for fast  singular value decomposition, for computing the pseudoinverse, and for  checking the rank and positive definiteness of a matrix.","text_prefix":false,"timestamp":0,"version":"1.6.10"},"r-correctr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://hendersontrent.github.io/correctR/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/correctR/correctR_0.3.1.tar.gz","https://cran.r-project.org/src/contrib/correctR_0.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.","text_prefix":false,"timestamp":1758689910,"version":"0.3.1"},"r-correlation":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://easystats.github.io/correlation/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/correlation/correlation_0.8.8.tar.gz","https://cran.r-project.org/src/contrib/correlation_0.8.8.tar.gz"],"subdirs":["noarch"],"summary":"Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. 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See Friendly (2002) <doi:10.1198/000313002533>.","text_prefix":false,"timestamp":1588831072,"version":"1.15"},"r-corrplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/taiyun/corrplot","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/corrplot/corrplot_0.95.tar.gz","https://cran.r-project.org/src/contrib/corrplot_0.95.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A graphical display of a correlation matrix or general matrix. 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In particular, this package makes it easy to combine multiple 'ggplot2' plots into one and label them with letters, e.g. A, B, C, etc., as is often required for scientific publications. The package also provides a streamlined and clean theme that is used in the Wilke lab, hence the package name, which stands for Claus O. Wilke's plot package.","text_prefix":false,"timestamp":0,"version":"1.2.0"},"r-coxboost":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CoxBoost","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/CoxBoost/CoxBoost_1.5.2.tar.gz","https://cloud.r-project.org/src/contrib/CoxBoost_1.5.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This package provides routines for fitting Cox models by likelihood based boosting for a single endpoint or in presence of competing risks","text_prefix":false,"timestamp":1790299816,"version":"1.5.2"},"r-coxme":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=coxme","license":"LGPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coxme/coxme_2.2-22.tar.gz","https://cran.r-project.org/src/contrib/coxme_2.2-22.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This package fits Cox proportional hazards models containing both  fixed and random effects.  The random effects can have a general form, of which familial interactions (a \"kinship\" matrix) is a particular special case.  Note that the simplest case of a mixed effects Cox model, i.e. a single random  per-group intercept, is also called a \"frailty\" model.  The approach is based on Rippatti and Palgren, Biometrics 2002.","text_prefix":false,"timestamp":1567848421,"version":"2.2_22"},"r-coxphf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://cemsiis.meduniwien.ac.at/kb/wf/software/statistische-software/fccoxphf/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coxphf/coxphf_1.13.4.tar.gz","https://cran.r-project.org/src/contrib/coxphf_1.13.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function). The program fits profile penalized likelihood confidence intervals which were proved to outperform Wald confidence intervals.","text_prefix":false,"timestamp":1588831744,"version":"1.13.4"},"r-coxrobust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ShanaScogin/coxrobust","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/coxrobust/coxrobust_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/coxrobust_1.0.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An implementation of robust estimation in Cox model. Functionality includes fitting efficiently and robustly Cox proportional hazards regression model in its basic form, where explanatory variables are time independent with one event per subject.  Method is based on a smooth modification of the partial likelihood.","text_prefix":false,"timestamp":1773517354,"version":"1.0.2"},"r-cpgassoc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=CpGassoc","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CpGassoc/CpGassoc_2.70.tar.gz","https://cran.r-project.org/src/contrib/CpGassoc_2.70.tar.gz"],"subdirs":["noarch"],"summary":"Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.","text_prefix":false,"timestamp":1758297742,"version":"2.70"},"r-cplm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/actuaryzhang/cplm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cplm/cplm_0.7-12.1.tar.gz","https://cran.r-project.org/src/contrib/cplm_0.7-12.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <https://link.springer.com/article/10.1007/s11222-012-9343-7>.","text_prefix":false,"timestamp":1563721460,"version":"0.7_12.1"},"r-cpp11":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/cpp11","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cpp11/cpp11_0.5.5.tar.gz","https://cran.r-project.org/src/contrib/cpp11_0.5.5.tar.gz"],"subdirs":["noarch"],"summary":"Provides a header only, C++11 interface to R's C interface. Compared to other approaches 'cpp11' strives to be safe against long jumps from the C API as well as C++ exceptions, conform to normal R function semantics and supports interaction with 'ALTREP' vectors.","text_prefix":false,"timestamp":1635965240,"version":"0.5.5"},"r-cpp11armadillo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/pachadotdev/cpp11armadillo","home":"https://pacha.dev/cpp11armadillo/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cpp11armadillo/cpp11armadillo_0.5.4.tar.gz","https://cran.r-project.org/src/contrib/cpp11armadillo_0.5.4.tar.gz"],"subdirs":["noarch"],"summary":"Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. This implementation is detailed in Vargas Sepulveda and Schneider Malamud (2024) <doi:10.48550/arXiv.2408.11074>.","text_prefix":false,"timestamp":1757610664,"version":"0.5.4"},"r-cpp4r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/pachadotdev/cpp4r","home":"https://cpp4r.org","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cpp4r/cpp4r_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/cpp4r_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a header only, 'C++' interface to 'R' with enhancements over 'cpp11'. Enforces copy-on-write semantics consistent with 'R' behavior. Offers native support for ALTREP objects, 'UTF-8' string handling, modern 'C++11' features and idioms, and reduced memory requirements. Allows for vendoring, making it useful for restricted environments. Compared to 'cpp11', it adds support for converting 'C++' maps to 'R' lists, 'Roxygen' documentation directly in 'C++' code, proper handling of matrix attributes, support for nullable external pointers, bidirectional copy of complex number types, flexibility in type conversions, use of nullable pointers, and various performance optimizations.","text_prefix":false,"timestamp":1784638169,"version":"1.1.0"},"r-cpprouting":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/vlarmet/cppRouting","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cppRouting/cppRouting_3.2.tar.gz","https://cran.r-project.org/src/contrib/cppRouting_3.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://archive.siam.org/meetings/alenex05/papers/03agoldberg.pdf>), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>), new bidirectional A* (Pijls & Post (2009) <https://repub.eur.nl/pub/16100/ei2009-10.pdf>), Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) <doi:10.1007/978-3-540-68552-4_24>), PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) <doi:10.1016/j.jpdc.2012.02.007>). Algorithms for solving the traffic assignment problem are All-or-Nothing assignment, Method of Successive Averages, Frank-Wolfe algorithm (M. Fukushima (1984) <doi:10.1016/0191-2615(84)90029-8>), Conjugate and Bi-Conjugate Frank-Wolfe algorithms (M. Mitradjieva, P. O. Lindberg (2012) <doi:10.1287/trsc.1120.0409>), Algorithm-B (R. B. Dial (2006) <doi:10.1016/j.trb.2006.02.008>).","text_prefix":false,"timestamp":1766482957,"version":"3.2"},"r-cramer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cramer","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cramer/cramer_0.9-4.tar.gz","https://cran.r-project.org/src/contrib/cramer_0.9-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Provides R routine for the so called two-sample Cramer-Test.  This nonparametric two-sample-test on equality of the underlying distributions can be applied to  multivariate data as well as univariate data. It offers two  possibilities to approximate the critical value both of which  are included in this package.","text_prefix":false,"timestamp":1540063817,"version":"0.9_4"},"r-cranlogs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-hub/cranlogs, https://r-hub.github.io/cranlogs","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cranlogs/cranlogs_2.1.1.tar.gz","https://cran.r-project.org/src/contrib/cranlogs_2.1.1.tar.gz"],"subdirs":["noarch"],"summary":"'API' to the database of 'CRAN' package downloads from the 'RStudio' 'CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>, see <https://github.com/r-hub/cranlogs.app> for the raw 'API'.","text_prefix":false,"timestamp":1758457407,"version":"2.1.1"},"r-crayon":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/crayon#readme","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/crayon/crayon_1.5.3.tar.gz","https://cran.r-project.org/src/contrib/crayon_1.5.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Colored terminal output on terminals that support 'ANSI' color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI' color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the 'chalk' 'JavaScript' project.","text_prefix":false,"timestamp":0,"version":"1.5.3"},"r-credentials":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/credentials (website) https://github.com/r-lib/credentials","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/credentials/credentials_2.0.3.tar.gz","https://cran.r-project.org/src/contrib/credentials_2.0.3.tar.gz"],"subdirs":["noarch"],"summary":"Setup and retrieve HTTPS and SSH credentials for use with 'git' and other services. For HTTPS remotes the package interfaces the 'git-credential' utility which 'git' uses to store HTTP usernames and passwords. For SSH remotes we provide convenient functions to find or generate appropriate SSH keys. The package both helps the user to setup a local git installation, and also provides a back-end for git/ssh client libraries to authenticate with existing user credentials.","text_prefix":false,"timestamp":1621844800,"version":"2.0.3"},"r-crew":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/wlandau/crew","home":"https://wlandau.github.io/crew/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/crew/crew_1.3.3.tar.gz","https://cloud.r-project.org/src/contrib/crew_1.3.3.tar.gz"],"subdirs":["noarch"],"summary":"In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'NNG'-powered 'mirai' R package by Gao (2023) <https://CRAN.R-project.org/package=mirai> is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The 'crew' package extends 'mirai' with a unifying interface for third-party worker launchers. Inspiration also comes from packages. 'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>, 'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>), and 'batchtools' by Lang, Bischel, and Surmann (2017) <doi:10.21105/joss.00135>.","text_prefix":false,"timestamp":1788565242,"version":"1.3.3"},"r-crew.cluster":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/wlandau/crew.cluster","home":"https://wlandau.github.io/crew.cluster/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/crew.cluster/crew.cluster_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/crew.cluster_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'crew.cluster' package extends the 'mirai'-powered 'crew' package with worker launcher plugins for traditional high-performance computing systems. Inspiration also comes from packages 'mirai' by Gao (2023) <https://github.com/shikokuchuo/mirai>, 'future' by Bengtsson (2021) <doi:10.32614/RJ-2021-048>, 'rrq' by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) <doi:10.1093/bioinformatics/btz284>), and 'batchtools' by Lang, Bischl, and Surmann (2017). <doi:10.21105/joss.00135>.","text_prefix":false,"timestamp":1758453915,"version":"0.4.0"},"r-crf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/wulingyun/CRF","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/CRF/CRF_0.4-3.tar.gz","https://cran.r-project.org/src/contrib/CRF_0.4-3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements modeling and computational tools for conditional random fields (CRF) model as well as other probabilistic undirected graphical models of discrete data with pairwise and unary potentials.","text_prefix":false,"timestamp":1563721169,"version":"0.4_3"},"r-crochet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/agrueneberg/crochet","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/crochet/crochet_2.3.0.tar.gz","https://cran.r-project.org/src/contrib/crochet_2.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Functions to help implement the extraction / subsetting / indexing function '[' and replacement function '[<-' of custom matrix-like types (based on S3, S4, etc.), modeled as closely to the base matrix class as possible (with tests to prove it).","text_prefix":false,"timestamp":1758010651,"version":"2.3.0"},"r-cropcircles":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/doehm/cropcircles","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cropcircles/cropcircles_0.2.4.tar.gz","https://cran.r-project.org/src/contrib/cropcircles_0.2.4.tar.gz"],"subdirs":["noarch"],"summary":"Images are cropped to a circle with a transparent background. The function takes a vector of images, either local or from a link, and circle crops the image. Paths to the cropped image are returned for plotting with 'ggplot2'.","text_prefix":false,"timestamp":1667690045,"version":"0.2.4"},"r-crossdes":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=crossdes","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/crossdes/crossdes_1.1-2.tar.gz","https://cran.r-project.org/src/contrib/crossdes_1.1-2.tar.gz"],"subdirs":["noarch"],"summary":"Contains functions for the construction of carryover balanced crossover designs. In addition contains functions to check given designs for balance.","text_prefix":false,"timestamp":1758200583,"version":"1.1_2"},"r-crosstalk":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://rstudio.github.io/crosstalk/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/crosstalk/crosstalk_1.2.2.tar.gz","https://cran.r-project.org/src/contrib/crosstalk_1.2.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files). Currently supports linked brushing and filtering.","text_prefix":false,"timestamp":1536752582,"version":"1.2.2"},"r-crs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/JeffreyRacine/R-Package-crs","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/crs/crs_0.15-46.tar.gz","https://cloud.r-project.org/src/contrib/crs_0.15-46.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.","text_prefix":false,"timestamp":1787238874,"version":"0.15_46"},"r-crsmeta":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hypertidy/crsmeta","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/crsmeta/crsmeta_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/crsmeta_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Obtain coordinate system metadata from various data formats. There are functions to extract a 'CRS' (coordinate reference system, <https://en.wikipedia.org/wiki/Spatial_reference_system>) in 'EPSG' (European Petroleum Survey Group, <http://www.epsg.org/>), 'PROJ4' <https://proj.org/>, or 'WKT2' (Well-Known Text 2, <http://docs.opengeospatial.org/is/12-063r5/12-063r5.html>) forms. This is purely for getting simple metadata from in-memory formats, please use other tools for out of memory data sources.","text_prefix":false,"timestamp":1757921704,"version":"0.3.0"},"r-crul":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/crul/, https://github.com/ropensci/crul, https://books.ropensci.org/http-testing/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/crul/crul_1.6.0.tar.gz","https://cran.r-project.org/src/contrib/crul_1.6.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A simple HTTP client, with tools for making HTTP requests, and mocking HTTP requests. The package is built on R6, and takes inspiration from Ruby's 'faraday' gem (<https://rubygems.org/gems/faraday>). The package name is a play on curl, the widely used command line tool for HTTP, and this package is built on top of the R package 'curl', an interface to 'libcurl' (<https://curl.se/libcurl/>).","text_prefix":false,"timestamp":1538945265,"version":"1.6.0"},"r-ctrdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=ctrdata, https://rfhb.github.io/ctrdata/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ctrdata/ctrdata_1.26.3.tar.gz","https://cloud.r-project.org/src/contrib/ctrdata_1.26.3.tar.gz"],"subdirs":["noarch"],"summary":"A system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from three public registers, the 'European Union Clinical Trials Register' ('EUCTR', <https://www.clinicaltrialsregister.eu/>), 'ClinicalTrials.gov' ('CTGOV', <https://clinicaltrials.gov/>), the 'ISRCTN' (<http://www.isrctn.com/>) and the 'European Union Clinical Trials Information System' ('CTIS', <https://euclinicaltrials.eu/>). Trial information is downloaded, converted and stored in a database ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). Functions are included to identify deduplicated records, to easily find and extract variables (fields) of interest even from complex nesting as used by the registers, and to update previous queries. 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These can be used to estimate the level of expected 'endogenous' taxa at different abundance levels retrieved from metagenomic samples, when comparing to samples of known sampling site or source. Method described in Fellows Yates, J. A. et. al. 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The curry package provides three different ways of performing partial function application by fixing arguments from either end of the argument list (currying and tail currying) or by fixing multiple named arguments (partial application). This package provides this functionality through the %<%, %-<%, and %><% operators which allows for a programming style comparable to modern functional languages. 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The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002) <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided, as well.","text_prefix":false,"timestamp":1787558444,"version":"0.6.1"},"r-cvauc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ledell/cvAUC","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cvAUC/cvAUC_1.1.4.tar.gz","https://cran.r-project.org/src/contrib/cvAUC_1.1.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.","text_prefix":false,"timestamp":1540065871,"version":"1.1.4"},"r-cvms":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ludvigolsen/cvms","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cvms/cvms_2.0.1.tar.gz","https://cran.r-project.org/src/contrib/cvms_2.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. 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Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.","text_prefix":false,"timestamp":1536516272,"version":"0.2_3"},"r-cvtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cvTools","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cvTools/cvTools_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/cvTools_0.3.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tools that allow developers to write functions for cross-validation with minimal programming effort and assist users with model selection.","text_prefix":false,"timestamp":1539412394,"version":"0.3.3"},"r-cvxbiclustr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cvxbiclustr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://cran.r-project.org/src/contrib/Archive/cvxbiclustr/cvxbiclustr_0.0.1.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"An iterative algorithm for solving a convex formulation of the biclustering problem.","text_prefix":false,"timestamp":1758739613,"version":"0.0.1"},"r-cvxr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/cvxgrp/CVXR","home":"https://cvxr.rbind.io, https://www.cvxgrp.org/CVXR/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/CVXR/CVXR_1.9.3.tar.gz","https://cloud.r-project.org/src/contrib/CVXR_1.9.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). 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It also provides high-level wrappers for input/output functions for seamlessly adding encryption to existing analyses.","text_prefix":false,"timestamp":1583177469,"version":"1.1.7"},"r-cytometree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=cytometree","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/cytometree/cytometree_2.0.6.tar.gz","https://cran.r-project.org/src/contrib/cytometree_2.0.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Given the hypothesis of a bi-modal distribution of cells for each marker, the algorithm constructs a binary tree, the nodes of which are subpopulations of cells. At each node, observed cells and markers are modeled by both a family of normal distributions and a family of bi-modal normal mixture distributions. 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Hover the mouse pointer over a cell to show details, drag a rectangle to zoom, and click row/column labels to highlight.","text_prefix":false,"timestamp":1539012407,"version":"0.6.1.2"},"r-d3network":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://christophergandrud.github.io/d3Network/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/d3Network/d3Network_0.5.2.1.tar.gz","https://cran.r-project.org/src/contrib/d3Network_0.5.2.1.tar.gz"],"subdirs":["noarch"],"summary":"This packages is intended to make it easy to create D3 JavaScript network, tree, dendrogram, and Sankey graphs from R using data frames. !!! NOTE: Active development has moved to the networkD3 package. !!!","text_prefix":false,"timestamp":1758057692,"version":"0.5.2.1"},"r-d3r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/timelyportfolio/d3r","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/d3r/d3r_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/d3r_1.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a suite of functions to help ease the use of 'd3.js' in R. These helpers include 'htmltools::htmlDependency' functions, hierarchy builders, and conversion tools for 'partykit', 'igraph,' 'table', and 'data.frame' R objects into the 'JSON' that 'd3.js' expects.","text_prefix":false,"timestamp":1540932650,"version":"1.1.0"},"r-daff":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/edwindj/daff","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/daff/daff_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/daff_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Diff, patch and merge for data frames. Document changes in data sets and use them to apply patches. Changes to data can be made visible by using render_diff. The V8 package is used to wrap the 'daff.js' JavaScript library which is included in the package.","text_prefix":false,"timestamp":1758669618,"version":"1.1.1"},"r-dagitty":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.dagitty.net, https://github.com/jtextor/dagitty","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dagitty/dagitty_0.3-4.tar.gz","https://cran.r-project.org/src/contrib/dagitty_0.3-4.tar.gz"],"subdirs":["noarch"],"summary":"A port of the web-based software 'DAGitty', available at <http://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.","text_prefix":false,"timestamp":1605220243,"version":"0.3_4"},"r-dagwood":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dagwood","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dagwood/dagwood_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/dagwood_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"DAGs With Omitted Objects Displayed (DAGWOOD) is a framework to help reveal key hidden assumptions in a causal DAG. This package provides an implementation of the DAGWOOD algorithm. 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Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack of direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. 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Data are discovered from National Water Information System <https://waterservices.usgs.gov/> and <https://waterdata.usgs.gov/nwis>. 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The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: \"Analyzing the Fine Structure of Distributions\" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: \"Projection-Based Clustering through Self-Organization and Swarm Intelligence\" (2018) <DOI:10.1007/978-3-658-20540-9>.","text_prefix":false,"timestamp":1622699795,"version":"1.4.0"},"r-datawizard":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://easystats.github.io/datawizard/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/datawizard/datawizard_1.4.1.tar.gz","https://cloud.r-project.org/src/contrib/datawizard_1.4.1.tar.gz"],"subdirs":["noarch"],"summary":"A lightweight package to easily manipulate, clean, transform, and prepare your data for analysis. It also forms the data wrangling backend for the packages in the 'easystats' ecosystem.","text_prefix":false,"timestamp":1633345577,"version":"1.4.1"},"r-datefixr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/datefixR/, https://github.com/ropensci/datefixR","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/datefixR/datefixR_1.7.0.tar.gz","https://cran.r-project.org/src/contrib/datefixR_1.7.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"There are many different formats dates are commonly represented with: the order of day, month, or year can differ, different separators (\"-\", \"/\", or whitespace) can be used, months can be numerical, names, or abbreviations and year given as two digits or four. 'datefixR' takes dates in all these different formats and converts them to R's built-in date class. If 'datefixR' cannot standardize a date, such as because it is too malformed, then the user is told which date cannot be standardized and the corresponding ID for the row. 'datefixR' also allows the imputation of missing days and months with user-controlled behavior.","text_prefix":false,"timestamp":1758693795,"version":"1.7.0"},"r-dbarts":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/vdorie/dbarts","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dbarts/dbarts_0.9-33.tar.gz","https://cran.r-project.org/src/contrib/dbarts_0.9-33.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package 'BayesTree'.","text_prefix":false,"timestamp":1665807970,"version":"0.9_33"},"r-dbi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://r-dbi.github.io/DBI","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DBI/DBI_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/DBI_1.3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A database interface definition for communication between R and relational database management systems.  All classes in this package are virtual and need to be extended by the various R/DBMS implementations.","text_prefix":false,"timestamp":0,"version":"1.3.0"},"r-dbitest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/r-dbi/DBItest","home":"https://dbitest.r-dbi.org","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DBItest/DBItest_1.8.3.tar.gz","https://cran.r-project.org/src/contrib/DBItest_1.8.3.tar.gz"],"subdirs":["noarch"],"summary":"A helper that tests DBI back ends for conformity to the interface.","text_prefix":false,"timestamp":1784801932,"version":"1.8.3"},"r-dbmodelr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"An ORM to handle database request through R objects.","dev_url":"https://github.com/LainPavot/DBModelR","doc_source_url":"https://github.com/LainPavot/DBModelR/blob/v0.2.0/README.md","doc_url":"https://github.com/LainPavot/DBModelR","home":"https://github.com/LainPavot/DBModelR","license":"AGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/LainPavot/DBModelR/archive/v0.2.0.zip","subdirs":["noarch"],"summary":"An ORM in R","text_prefix":false,"timestamp":1607969133,"version":"0.2.0"},"r-dbplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/edgararuiz/dbplot","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dbplot/dbplot_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/dbplot_0.4.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Leverages 'dplyr' to process the calculations of a plot inside a database.  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Basic features works with any database that has a 'DBI' back end; more advanced features require 'SQL' translation to be provided by the package author.","text_prefix":false,"timestamp":1529786407,"version":"2.6.0"},"r-dbscan":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/mhahsler/dbscan","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/dbscan/dbscan_1.2.7.tar.gz","https://cloud.r-project.org/src/contrib/dbscan_1.2.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A fast reimplementation of several density-based algorithms of the DBSCAN family for spatial data. 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An R interface to fast kNN and fixed-radius NN search is also provided.","text_prefix":false,"timestamp":1619076430,"version":"1.2.7"},"r-dcifer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://eppicenter.github.io/dcifer/","home":"https://github.com/EPPIcenter/dcifer","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dcifer/dcifer_1.5.2.tar.gz","https://cran.r-project.org/src/contrib/dcifer_1.5.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data","text_prefix":false,"timestamp":1783536511,"version":"1.5.2"},"r-dcurver":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/oguzhanogreden/dcurver","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dcurver/dcurver_0.9.3.tar.gz","https://cran.r-project.org/src/contrib/dcurver_0.9.3.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density, Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001) <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of the loglikelihood and a random generator for Davidian curves.","text_prefix":false,"timestamp":1588620164,"version":"0.9.3"},"r-ddalpha":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ddalpha","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ddalpha/ddalpha_1.3.16.tar.gz","https://cran.r-project.org/src/contrib/ddalpha_1.3.16.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.","text_prefix":false,"timestamp":0,"version":"1.3.16"},"r-ddoutlier":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jhmadsen/DDoutlier","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DDoutlier/DDoutlier_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/DDoutlier_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) <doi:10.1145/782010.782021>, Angiulli, F., & Pizzuti, C. (2002) <doi:10.1007/3-540-45681-3_2>, Hautamaki, V., & Ismo, K. (2004) <doi:10.1109/ICPR.2004.1334558> and Zhang, K., Hutter, M. & Jin, H. (2009) <doi:10.1007/978-3-642-01307-2_84>. For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) <doi:10.1007/3-540-47887-6_53>, Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) <doi:10.1007/11731139_68>, Schubert, E., Zimek, A. & Kriegel, H-P. (2014) <doi:10.1137/1.9781611973440.63>, Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) <doi:10.1007/978-3-540-73499-4_6>, Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) <doi:10.1109/ICDE.2003.1260802>, Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) <doi:10.1145/342009.335388>, Kriegel, H.-P., Kr\u00f6ger, P., Schubert, E., & Zimek, A. (2009) <doi:10.1145/1645953.1646195>, Zhu, Q., Feng, Ji. & Huang, J. 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An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R.","text_prefix":false,"timestamp":1539274394,"version":"1.16.0"},"r-ddrtree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DDRTree","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DDRTree/DDRTree_0.1.6.tar.gz","https://cran.r-project.org/src/contrib/DDRTree_0.1.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.","text_prefix":false,"timestamp":1772710077,"version":"0.1.6"},"r-debugme":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/debugme#readme","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/debugme/debugme_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/debugme_1.2.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Specify debug messages as special string constants, and control debugging of packages via environment variables.","text_prefix":false,"timestamp":1540061648,"version":"1.2.0"},"r-debugr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jsugarelli/debugr/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/debugr/debugr_0.0.1.tar.gz","https://cran.r-project.org/src/contrib/debugr_0.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Tool to print out the value of R objects/expressions while running an R script. 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Function \"code\" can be used to code plain text to code if there is a 1:1 relation between the two. The concept relies on 'keyvalue' objects used for translation. There are several 'keyvalue' objects included in the areas of geographical regional codes, administrative health care unit codes, diagnosis codes and more. It is also easy to extend the use by arbitrary  code sets.","text_prefix":false,"timestamp":1537508627,"version":"1.2.3"},"r-deconvolver":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://bnaras.github.io/deconvolveR/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/deconvolveR/deconvolveR_1.2-1.tar.gz","https://cran.r-project.org/src/contrib/deconvolveR_1.2-1.tar.gz"],"subdirs":["noarch"],"summary":"Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior (\"g-modeling\") by deconvolution and Empirical Bayes methods. 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Other functions are also included to assist with other areas of deep time visualization.","dev_url":"https://github.com/willgearty/deeptime","doc_url":"http://williamgearty.com/deeptime/","home":"https://github.com/willgearty/deeptime","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/deeptime/deeptime_2.4.0.tar.gz","https://cran.r-project.org/src/contrib/deeptime_2.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Facilitates highly customizable and reproducible visualizations of data over geological time intervals","text_prefix":false,"timestamp":1783714455,"version":"2.4.0"},"r-deeptimedata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/willgearty/deeptimedata","doc_url":"https://williamgearty.com/deeptimedata/","home":"https://github.com/willgearty/deeptimedata","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/deeptimedata/deeptimedata_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/deeptimedata_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Geologic pattern data from <https://ngmdb.usgs.gov/fgdc_gds/geolsymstd.php>. 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The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial.","text_prefix":false,"timestamp":1642683960,"version":"8.4.3"},"r-deldir":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=deldir","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/deldir/deldir_2.0-4.tar.gz","https://cran.r-project.org/src/contrib/deldir_2.0-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Calculates the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. Plots triangulations and tessellations in various ways.  Clips tessellations to sub-windows. Calculates perimeters of tessellations.  Summarises information about the tiles of the tessellation.","text_prefix":false,"timestamp":1633825489,"version":"2.0_4"},"r-demerelate":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Demerelate","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Demerelate/Demerelate_0.9-3.tar.gz","https://cran.r-project.org/src/contrib/Demerelate_0.9-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions to calculate pairwise relatedness on diploid genetic datasets. Different estimators for relatedness can be combined with information on geographical distances. Information on heterozygosity, allele- and genotype diversity as well as genetic F-statistics are provided for each population.","text_prefix":false,"timestamp":1539806492,"version":"0.9_3"},"r-deming":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=deming","license":"LGPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/deming/deming_1.4-1.tar.gz","https://cran.r-project.org/src/contrib/deming_1.4-1.tar.gz"],"subdirs":["noarch"],"summary":"Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.","text_prefix":false,"timestamp":1758156685,"version":"1.4_1"},"r-dendextend":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/talgalili/dendextend/, https://cran.r-project.org/package=dendextend, https://www.r-statistics.com/tag/dendextend/, https://academic.oup.com/bioinformatics/article/31/22/3718/240978/dendextend-an-R-package-for-visualizing-adjusting","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dendextend/dendextend_1.19.1.tar.gz","https://cran.r-project.org/src/contrib/dendextend_1.19.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.","text_prefix":false,"timestamp":0,"version":"1.19.1"},"r-dendroextras":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dendroextras","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/dendroextras/dendroextras_0.2.4.tar.gz","https://cloud.r-project.org/src/contrib/dendroextras_0.2.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides extra functions to manipulate dendrograms that build on the base functions provided by the 'stats' package. The main functionality it is designed to add is the ability to colour all the edges in an object of class 'dendrogram' according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut 'dendrogram' and 'hclust' objects and to  set/get labels.","text_prefix":false,"timestamp":1538348702,"version":"0.2.4"},"r-dendsort":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/dendsort","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dendsort/dendsort_0.3.4.tar.gz","https://cran.r-project.org/src/contrib/dendsort_0.3.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization.","text_prefix":false,"timestamp":0,"version":"0.3.4"},"r-denoiser":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=denoiseR","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/denoiseR/denoiseR_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/denoiseR_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Estimate a low rank matrix from noisy data using singular values thresholding and shrinking functions. Impute missing values with matrix completion. The method is described in <arXiv:1602.01206>.","text_prefix":false,"timestamp":1758486056,"version":"1.0.2"},"r-densestbayes":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=densEstBayes","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/densEstBayes/densEstBayes_1.0-2.2.tar.gz","https://cran.r-project.org/src/contrib/densEstBayes_1.0-2.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. 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The aim is to curate a collection of its variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it provides implementations of the algorithms 'jDE' by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133> for single-objective optimization and 'NCDE' by Qu et al. (2012) <doi:10.1109/TEVC.2011.2161873> for multimodal optimization (single-objective problems with multiple solutions).","text_prefix":false,"timestamp":1536617677,"version":"1.2_1"},"r-depmixs4":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://depmix.github.io/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/depmixS4/depmixS4_1.5-3.tar.gz","https://cran.r-project.org/src/contrib/depmixS4_1.5-3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).","text_prefix":false,"timestamp":1567863014,"version":"1.5_3"},"r-deps":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://hub.analythium.io/deps/, https://github.com/analythium/deps","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/deps/deps_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/deps_0.4.1.tar.gz"],"subdirs":["noarch"],"summary":"Manage your source code dependencies by decorating your existing R code with special, 'roxygen'-style comments.","text_prefix":false,"timestamp":1768035384,"version":"0.4.1"},"r-deriv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Deriv","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/Deriv/Deriv_4.3.5.tar.gz","https://cloud.r-project.org/src/contrib/Deriv_4.3.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","noarch","osx-64","osx-arm64","win-64"],"summary":"R-based solution for symbolic differentiation. It admits user-defined function as well as function substitution in arguments of functions to be differentiated. Some symbolic simplification is part of the work.","text_prefix":false,"timestamp":0,"version":"4.3.5"},"r-desc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/desc#readme","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/desc/desc_1.4.3.tar.gz","https://cran.r-project.org/src/contrib/desc_1.4.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages.","text_prefix":false,"timestamp":0,"version":"1.4.3"},"r-desctools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/AndriSignorell/DescTools/","home":"https://andrisignorell.github.io/DescTools/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DescTools/DescTools_0.99.60.tar.gz","https://cran.r-project.org/src/contrib/DescTools_0.99.60.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.","text_prefix":false,"timestamp":1543061522,"version":"0.99.60"},"r-desolve":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/tpetzoldt/deSolve/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/deSolve/deSolve_1.42.tar.gz","https://cran.r-project.org/src/contrib/deSolve_1.42.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions that solve initial value problems of a system of first-order ordinary differential equations ('ODE'), of partial differential equations ('PDE'), of differential algebraic equations ('DAE'), and of delay differential equations.  The functions provide an interface to the FORTRAN functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK' collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk' and a C-implementation of solvers of the 'Runge-Kutta' family with fixed or variable time steps.  The package contains routines designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D partial differential equations ('PDE') that have been converted to 'ODEs' by numerical differencing.","text_prefix":false,"timestamp":1649953827,"version":"1.42"},"r-destinie":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/nosretraites/Destinie-2","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/nosretraites/Destinie-2/archive/588198941ced3926fcb57c360d41a82e588bef77.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"Dynamic microsimulation model","text_prefix":false,"timestamp":1687191502,"version":"0.0"},"r-details":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/yonicd/details","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/details/details_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/details_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Create a details HTML tag around R objects to place in a Markdown, 'Rmarkdown' and 'roxygen2' documentation.","text_prefix":false,"timestamp":1622147075,"version":"0.4.0"},"r-detectseparation":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ikosmidis/detectseparation","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/detectseparation/detectseparation_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/detectseparation_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007, <https://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a>) using 'ROI' <https://cran.r-project.org/package=ROI> or 'lpSolveAPI' <https://cran.r-project.org/package=lpSolveAPI>. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the 'brglm2' <https://cran.r-project.org/package=brglm2> package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration. According to the results in Lesaffre & Albert (1989, <https://www.jstor.org/stable/2345845>), divergence of those ratios indicates data separation.","text_prefix":false,"timestamp":1636279600,"version":"0.4.0"},"r-devtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/r-lib/devtools","home":"https://devtools.r-lib.org/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/devtools/devtools_2.5.2.tar.gz","https://cran.r-project.org/src/contrib/devtools_2.5.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Collection of package development tools.","text_prefix":false,"timestamp":0,"version":"2.5.2"},"r-dfba":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DFBA","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/DFBA/DFBA_0.1.0.tar.gz","https://cloud.r-project.org/src/contrib/DFBA_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"A set of functions to perform distribution-free Bayesian analyses. Included are Bayesian analogues to the frequentist Mann-Whitney U test, the Wilcoxon Signed-Ranks test, Kendall's Tau Rank Correlation Coefficient, Goodman and Kruskal's Gamma, McNemar's Test, the binomial test, the sign test, the median test, as well as distribution-free methods for testing contrasts among condition and for computing Bayes factors for hypotheses. The package also includes procedures to estimate the power of distribution-free Bayesian tests based on data simulations using various probability models for the data. The set of functions provide data analysts with a set of Bayesian procedures that avoids requiring parametric assumptions about measurement error and is robust to problem of extreme outlier scores.","text_prefix":false,"timestamp":1787170946,"version":"0.1.0"},"r-dfdr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dfdr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dfdr/dfdr_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/dfdr_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Implementation of automatically computing derivatives of functions (see Mailund Thomas (2017) <doi:10.1007/978-1-4842-2881-4>). Moreover, calculating gradients, Hessian and Jacobian matrices is possible.","text_prefix":false,"timestamp":1758188465,"version":"0.2.0"},"r-dfidx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=dfidx","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dfidx/dfidx_0.2-0.tar.gz","https://cran.r-project.org/src/contrib/dfidx_0.2-0.tar.gz"],"subdirs":["noarch"],"summary":"Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.","text_prefix":false,"timestamp":1622035599,"version":"0.2_0"},"r-dfms":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/dfms/, https://github.com/ropensci/dfms","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dfms/dfms_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/dfms_1.0.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.","text_prefix":false,"timestamp":1781832612,"version":"1.0.1"},"r-dfoptim":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dfoptim/dfoptim_2023.1.0.tar.gz","https://cran.r-project.org/src/contrib/dfoptim_2023.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.","text_prefix":false,"timestamp":1537053258,"version":"2023.1.0"},"r-dgeobj":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DGEobj","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DGEobj/DGEobj_1.1.2.tar.gz","https://cran.r-project.org/src/contrib/DGEobj_1.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides a flexible container to manage and annotate Differential Gene Expression (DGE) analysis results","text_prefix":false,"timestamp":1758007319,"version":"1.1.2"},"r-dgeobj.utils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DGEobj.utils","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DGEobj.utils/DGEobj.utils_1.0.6.tar.gz","https://cran.r-project.org/src/contrib/DGEobj.utils_1.0.6.tar.gz"],"subdirs":["noarch"],"summary":"Provides a function toolkit to facilitate reproducible RNA-Seq Differential Gene Expression (DGE) analysis (Law (2015) <doi:10.12688/f1000research.9005.3>).  The tools include both analysis work-flow and utility functions: mapping/unit conversion, count normalization, accounting for unknown covariates, and more.  This is a complement/cohort to the 'DGEobj' package that provides a flexible container to manage and annotate Differential Gene Expression analysis results.","text_prefix":false,"timestamp":1758620180,"version":"1.0.6"},"r-dggridr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-barnes/dggridR/","license":"AGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dggridR/dggridR_3.1.1.tar.gz","https://cran.r-project.org/src/contrib/dggridR_3.1.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.","text_prefix":false,"timestamp":1665808453,"version":"3.1.1"},"r-dgof":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dgof","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dgof/dgof_1.5.1.tar.gz","https://cran.r-project.org/src/contrib/dgof_1.5.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This package contains a proposed revision to the stats::ks.test() function and the associated ks.test.Rd help page.  With one minor exception, it does not change the existing behavior of ks.test(), and it adds features necessary for doing one-sample tests with hypothesized discrete distributions.  The package also contains cvm.test(), for doing one-sample Cramer-von Mises goodness-of-fit tests.","text_prefix":false,"timestamp":1635845925,"version":"1.5.1"},"r-dharma":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://florianhartig.github.io/DHARMa/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DHARMa/DHARMa_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/DHARMa_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB' and 'spaMM', generalized additive models ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.","text_prefix":false,"timestamp":1599602132,"version":"0.5.0"},"r-diagram":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=diagram","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/diagram/diagram_1.6.5.tar.gz","https://cran.r-project.org/src/contrib/diagram_1.6.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams,  visualising webs, electrical networks, etc. Support for the book \"A practical guide to ecological modelling - using R as a simulation platform\" by Karline Soetaert and Peter M.J. Herman (2009), Springer. and the book \"Solving Differential Equations in R\" by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer. Includes demo(flowchart), demo(plotmat), demo(plotweb).","text_prefix":false,"timestamp":1539260395,"version":"1.6.5"},"r-diagrammer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rich-iannone/DiagrammeR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DiagrammeR/DiagrammeR_1.0.12.tar.gz","https://cran.r-project.org/src/contrib/DiagrammeR_1.0.12.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.","text_prefix":false,"timestamp":1540640389,"version":"1.0.12"},"r-diagrammersvg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rich-iannone/DiagrammeRsvg","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DiagrammeRsvg/DiagrammeRsvg_0.1.tar.gz","https://cran.r-project.org/src/contrib/DiagrammeRsvg_0.1.tar.gz"],"subdirs":["noarch"],"summary":"Allows for export of DiagrammeR Graphviz objects to SVG.","text_prefix":false,"timestamp":1758512806,"version":"0.1"},"r-dials":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://tidymodels.github.io/dials","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dials/dials_1.4.4.tar.gz","https://cran.r-project.org/src/contrib/dials_1.4.4.tar.gz"],"subdirs":["noarch"],"summary":"Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.","text_prefix":false,"timestamp":1591811104,"version":"1.4.4"},"r-dicedesign":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://dice.emse.fr/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DiceDesign/DiceDesign_1.10.tar.gz","https://cran.r-project.org/src/contrib/DiceDesign_1.10.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Space-Filling Designs and Uniformity Criteria.","text_prefix":false,"timestamp":1588560349,"version":"1.10"},"r-dicekriging":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DiceKriging","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/DiceKriging/DiceKriging_1.6.1.tar.gz","https://cloud.r-project.org/src/contrib/DiceKriging_1.6.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Estimation, validation and prediction of kriging models.","text_prefix":false,"timestamp":1588621077,"version":"1.6.1"},"r-dicer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/AlineTalhouk/diceR/","home":"https://alinetalhouk.github.io/diceR/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/diceR/diceR_3.2.0.tar.gz","https://cloud.r-project.org/src/contrib/diceR_3.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Performs cluster analysis using an ensemble clustering framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>.  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Ordinary R vectors are unsuitable for working with values of associative maps because elements of an R vector may be accessed by reference to their location in the vector, but associative maps are stored in arbitrary order.  However, when associating keys with values one needs both parts to be in 1-1 correspondence, so one cannot dispense with the order entirely.  The 'disordR' package includes a single S4 class, disord.  This class allows one to perform only those operations appropriate for manipulating values of associative maps and prevents any other operation (such as accessing an element at a particular location).  A useful heuristic is that one is only allowed to access or modify a disord object using a python list comprehension.  The idea is to prevent ill-defined operations on values (or keys) of associative maps, whose order is undefined or at best implementation-specific, while allowing and facilitating sensible operations.  The package is needed for development versions of 'mvp', 'hyper2', 'spray', 'clifford', and 'freealg'.","text_prefix":false,"timestamp":1653556279,"version":"0.9_8_6"},"r-dispersionindicators":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://unh-pfem-gitlab.ara.inrae.fr/packages/dispersion_indicators/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dispersionIndicators/dispersionIndicators_0.1.6.tar.gz","https://cran.r-project.org/src/contrib/dispersionIndicators_0.1.6.tar.gz"],"subdirs":["noarch"],"summary":"Provides methods for analyzing the dispersion of tabular datasets with batched and ordered samples. Based on convex hull or integrated covariance Mahalanobis, several indicators are implemented for inter and intra batch dispersion analysis. It is designed to facilitate robust statistical assessment of data variability, supporting applications in exploratory data analysis and quality control, for such datasets as the one found in metabololomics studies. For more details see Salanon (2024) <doi:10.1016/j.chemolab.2024.105148> and Salanon (2025) <doi:10.1101/2025.08.01.668073>.","text_prefix":false,"timestamp":1785829894,"version":"0.1.6"},"r-dissutils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dissUtils","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dissUtils/dissUtils_1.0.tar.gz","https://cran.r-project.org/src/contrib/dissUtils_1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix.","text_prefix":false,"timestamp":1758161967,"version":"1.0"},"r-distances":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/fsavje/distances","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/distances/distances_0.1.13.tar.gz","https://cran.r-project.org/src/contrib/distances_0.1.13.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides tools for constructing, manipulating and using distance metrics.","text_prefix":false,"timestamp":1551735053,"version":"0.1.13"},"r-distcomp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://dx.doi.org/10.18637/jss.v077.i13","license":"LGPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/distcomp/distcomp_1.3-4.tar.gz","https://cran.r-project.org/src/contrib/distcomp_1.3-4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using ''opencpu'', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R ''survival'' package. (That is, the underlying Cox model code is derived from that in the R ''survival'' package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via ''shiny'') for the implemented methods to help in designing and deploying the computations.","text_prefix":false,"timestamp":1603053305,"version":"1.3_4"},"r-distill":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/distill","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/distill/distill_1.6.tar.gz","https://cran.r-project.org/src/contrib/distill_1.6.tar.gz"],"subdirs":["noarch"],"summary":"Scientific and technical article format for the web. 'Distill' articles feature attractive, reader-friendly typography, flexible layout options for visualizations, and full support for footnotes and citations.","text_prefix":false,"timestamp":1634148051,"version":"1.6"},"r-distillery":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.ral.ucar.edu/staff/ericg","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/distillery/distillery_1.2-2.tar.gz","https://cran.r-project.org/src/contrib/distillery_1.2-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Some very simple method functions for confidence interval calculation, bootstrap resampling, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.","text_prefix":false,"timestamp":1539744730,"version":"1.2_2"},"r-distory":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=distory","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/distory/distory_1.4.5.tar.gz","https://cran.r-project.org/src/contrib/distory_1.4.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Geodesic distance between phylogenetic trees and associated functions. The theoretical background of 'distory' is published in Billera et al. (2001) \"Geometry of the space of phylogenetic trees.\" <doi:10.1006/aama.2001.0759>.","text_prefix":false,"timestamp":1758107144,"version":"1.4.5"},"r-distr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://distr.r-forge.r-project.org/","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/distr/distr_2.9.7.tar.gz","https://cran.r-project.org/src/contrib/distr_2.9.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"S4-classes and methods for distributions.","text_prefix":false,"timestamp":1545863005,"version":"2.9.7"},"r-distr6":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/alan-turing-institute/distr6/","home":"https://alan-turing-institute.github.io/distr6/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://raphaels1.r-universe.dev/src/contrib/distr6_1.8.4.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"An R6 object oriented distributions package. Unified interface for 42 probability distributions and 11 kernels including functionality for multiple scientific types. Additionally functionality for composite distributions and numerical imputation. Design patterns including wrappers and decorators are described in Gamma et al. (1994, ISBN:0-201-63361-2). For quick reference of probability distributions including d/p/q/r functions and results we refer to McLaughlin, M. P. (2001). Additionally Devroye (1986, ISBN:0-387-96305-7) for sampling the Dirichlet distribution, Gentle (2009) <doi:10.1007/978-0-387-98144-4> for sampling the Multivariate Normal distribution and Michael et al. (1976) <doi:10.2307/2683801> for sampling the Wald distribution.","text_prefix":false,"timestamp":1613505189,"version":"1.8.4"},"r-distrex":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://distr.r-forge.r-project.org/","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/distrEx/distrEx_2.9.6.tar.gz","https://cran.r-project.org/src/contrib/distrEx_2.9.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Extends package 'distr' by functionals, distances, and conditional distributions.","text_prefix":false,"timestamp":1546658115,"version":"2.9.6"},"r-distributional":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mitchelloharawild/distributional","home":"https://pkg.mitchelloharawild.com/distributional/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/distributional/distributional_0.9.0.tar.gz","https://cloud.r-project.org/src/contrib/distributional_0.9.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Vectorised distribution objects with tools for manipulating, visualising, and using probability distributions. Designed to allow model prediction outputs to return distributions rather than their parameters, allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.","text_prefix":false,"timestamp":1592301255,"version":"0.9.0"},"r-distributions3":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/alexpghayes/distributions3","home":"https://alexpghayes.github.io/distributions3/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/distributions3/distributions3_0.2.4.tar.gz","https://cran.r-project.org/src/contrib/distributions3_0.2.4.tar.gz"],"subdirs":["noarch"],"summary":"Tools to create and manipulate probability distributions using S3.  Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions.  Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.","text_prefix":false,"timestamp":1784717886,"version":"0.2.4"},"r-distributionutils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DistributionUtils","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DistributionUtils/DistributionUtils_0.6-2.tar.gz","https://cran.r-project.org/src/contrib/DistributionUtils_0.6-2.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Utilities are provided which are of use in the packages I have developed for dealing with distributions. Currently these packages are GeneralizedHyperbolic, VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these packages requires DistributionUtils. Functionality includes sample skewness and kurtosis, log-histogram, tail plots, moments by integration, changing the point about which a moment is calculated, functions for testing distributions using inversion tests and the Massart inequality. Also includes an implementation of the incomplete Bessel K function.","text_prefix":false,"timestamp":1757829088,"version":"0.6_2"},"r-distutils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"A collection of C++ functions for fast computation of distances and elastic energies","home":"https://github.com/Albluca/distutils","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/Albluca/distutils/archive/cd82bcac0aa919261813a542f2e4a4939eeecace.zip","subdirs":["linux-64","osx-64","win-64"],"summary":"A fast implementation of distance computation with RCpp","text_prefix":false,"timestamp":1758093787,"version":"1.0"},"r-divemove":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/spluque/diveMove","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/diveMove/diveMove_1.6.4.tar.gz","https://cran.r-project.org/src/contrib/diveMove_1.6.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Utilities to represent, visualize, filter, analyse, and summarize time-depth recorder (TDR) data.  Miscellaneous functions for handling location data are also provided.","text_prefix":false,"timestamp":1563717530,"version":"1.6.4"},"r-diversity":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://diversityinlife.weebly.com/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/diveRsity/diveRsity_1.9.90.tar.gz","https://cran.r-project.org/src/contrib/diveRsity_1.9.90.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Allows the calculation of both genetic diversity partition statistics, genetic differentiation statistics, and locus informativeness for ancestry assignment. It also provides users with various option to calculate bootstrapped 95\\% confidence intervals both across loci, for pairwise population comparisons, and to plot these results interactively. Parallel computing capabilities and pairwise results without bootstrapping are provided. Also calculates F-statistics from Weir and Cockerham (1984). Various plotting features are provided, as well as Chi-square tests of genetic heterogeneity. Functionality for the calculation of various diversity parameters is possible for RAD-seq derived SNP data sets containing thousands of marker loci. A shiny application for the development of microsatellite multiplexes is also available.","text_prefix":false,"timestamp":1758740660,"version":"1.9.90"},"r-dixontest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dixonTest","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dixonTest/dixonTest_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/dixonTest_1.0.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"For outlier detection in small and normally distributed samples the ratio test of Dixon (Q-test) can be used. Density, distribution function, quantile function and random generation for Dixon's ratio statistics are provided as wrapper functions. The core applies McBane's Fortran functions <doi:10.18637/jss.v016.i03> that use Gaussian quadrature for a numerical solution.","text_prefix":false,"timestamp":1758493029,"version":"1.0.4"},"r-dlm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dlm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dlm/dlm_1.1-6.1.tar.gz","https://cran.r-project.org/src/contrib/dlm_1.1-6.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.","text_prefix":false,"timestamp":1786615741,"version":"1.1_6.1"},"r-dlnm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gasparrini/dlnm, http://www.ag-myresearch.com/package-dlnm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dlnm/dlnm_2.4.10.tar.gz","https://cran.r-project.org/src/contrib/dlnm_2.4.10.tar.gz"],"subdirs":["noarch"],"summary":"Collection of functions for distributed lag linear and non-linear models.","text_prefix":false,"timestamp":1733979233,"version":"2.4.10"},"r-dlookr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/choonghyunryu/dlookr/","home":"https://choonghyunryu.github.io/dlookr/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dlookr/dlookr_0.6.5.tar.gz","https://cran.r-project.org/src/contrib/dlookr_0.6.5.tar.gz"],"subdirs":["noarch"],"summary":"A collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values and outliers and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and relationship between target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputates missing values and outliers, resolving skewness. And it creates automated reports that support these three tasks.","text_prefix":false,"timestamp":1649702986,"version":"0.6.5"},"r-dlstats":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/GuangchuangYu/dlstats","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dlstats/dlstats_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/dlstats_0.1.8.tar.gz"],"subdirs":["noarch"],"summary":"Monthly download stats of 'CRAN' and 'Bioconductor' packages. Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <https://cranlogs.r-pkg.org:443>. 'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.","text_prefix":false,"timestamp":1665531885,"version":"0.1.8"},"r-dmfiller":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/catsch/DM_FILLER","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/catsch/DM_FILLER/archive/refs/tags/V2.6.tar.gz","subdirs":["noarch"],"summary":"DM_FILLER reports BGC delayed mode information into argo files according to ARGO recommandations","text_prefix":false,"timestamp":1762297955,"version":"2.6"},"r-dml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/terrytangyuan/dml","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dml/dml_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/dml_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.","text_prefix":false,"timestamp":1758660281,"version":"1.1.0"},"r-dmt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dmt","license":"BSD_2_clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dmt/dmt_0.8.20.tar.gz","https://cran.r-project.org/src/contrib/dmt_0.8.20.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Probabilistic dependency modeling toolkit.","text_prefix":false,"timestamp":1539920270,"version":"0.8.20"},"r-dmwr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DMwR","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DMwR/DMwR_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/DMwR_0.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This package includes functions and data accompanying the book  \"Data Mining with R, learning with case studies\" by Luis Torgo, CRC Press 2010.","text_prefix":false,"timestamp":1540056990,"version":"0.4.1"},"r-dmwr2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ltorgo/DMwR2","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DMwR2/DMwR2_0.0.2.tar.gz","https://cran.r-project.org/src/contrib/DMwR2_0.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Functions and data accompanying the second edition of the book  \"Data Mining with R, learning with case studies\" by Luis Torgo, published by CRC Press.","text_prefix":false,"timestamp":1758703175,"version":"0.0.2"},"r-dnapath":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dnapath","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dnapath/dnapath_0.7.6.tar.gz","https://cran.r-project.org/src/contrib/dnapath_0.7.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Integrates pathway information into the differential network analysis of two gene expression datasets as described in Grimes, Potter, and Datta (2019) <doi:10.1038/s41598-019-41918-3>. Provides summary functions to break down the results at the pathway, gene, or individual connection level. The differential networks for each pathway of interest can be plotted, and the visualization will highlight any differentially expressed genes and all of the gene-gene associations that are significantly differentially connected.","text_prefix":false,"timestamp":1758740238,"version":"0.7.6"},"r-doby":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/hojsgaard/doBy","home":"https://github.com/hojsgaard/doBy","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/doBy/doBy_4.7.2.tar.gz","https://cran.r-project.org/src/contrib/doBy_4.7.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Utility package containing: Main categories: Working with grouped data: 'do' something to data when  stratified 'by' some variables.  General linear estimates. Data handling utilities. Functional programming, in particular restrict functions to a smaller domain. Miscellaneous functions for data handling. Model stability in connection with model selection. Miscellaneous other tools.","text_prefix":false,"timestamp":1588595316,"version":"4.7.2"},"r-doc2vec":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bnosac/doc2vec","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/doc2vec/doc2vec_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/doc2vec_0.2.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms, namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. The techniques in the package are detailed in the paper \"Distributed Representations of Sentences and Documents\" by Mikolov et al. (2014), available at <arXiv:1405.4053>. The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering. It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps these document embeddings to a lower-dimensional space using the 'Uniform Manifold Approximation and Projection' (UMAP) clustering algorithm and finds dense areas in that space using a 'Hierarchical Density-Based Clustering' technique (HDBSCAN). These dense areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can be found which are representative of the topic. More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at <arXiv:2008.09470>.","text_prefix":false,"timestamp":1764242414,"version":"0.2.2"},"r-dockerfiler":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ThinkR-open/dockerfiler","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dockerfiler/dockerfiler_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/dockerfiler_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Build a Dockerfile straight from your R session. 'dockerfiler' allows you to create step by step a Dockerfile, and provide convenient tools to wrap R code inside this Dockerfile.","text_prefix":false,"timestamp":1630696132,"version":"1.0.0"},"r-docopt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/docopt/docopt.R","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/docopt/docopt_0.7.2.tar.gz","https://cran.r-project.org/src/contrib/docopt_0.7.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Define a command-line interface by just giving it a description in the specific format.","text_prefix":false,"timestamp":0,"version":"0.7.2"},"r-documenter":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=documenter","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/documenter/documenter_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/documenter_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"It is sometimes necessary to create documentation for all files in a directory. Doing so by hand can be very tedious. This task is made fast and reproducible using the functionality of 'documenter'. It aggregates all text files in a directory and its subdirectories into a single word document in a semi-automated fashion.","text_prefix":false,"timestamp":1665800147,"version":"0.1.3"},"r-docxtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://graphdr.github.io/docxtools","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/docxtools/docxtools_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/docxtools_0.3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A set of helper functions for using R Markdown to create documents in docx format, especially documents for use in a classroom or workshop setting.","text_prefix":false,"timestamp":1530942584,"version":"0.3.0"},"r-docxtractr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://gitlab.com/hrbrmstr/docxtractr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/docxtractr/docxtractr_0.6.5.tar.gz","https://cran.r-project.org/src/contrib/docxtractr_0.6.5.tar.gz"],"subdirs":["noarch"],"summary":"'Microsoft Word' 'docx' files provide an 'XML' structure that is fairly straightforward to navigate, especially when it applies to 'Word' tables and comments. Tools are provided to determine table count/structure, comment count and also to extract/clean tables and comments from 'Microsoft Word' 'docx' documents. There is also nascent support for '.doc' and '.pptx' files.","text_prefix":false,"timestamp":1758584153,"version":"0.6.5"},"r-doe.base":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://prof.bht-berlin.de/groemping/DoE/, https://prof.bht-berlin.de/groemping/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DoE.base/DoE.base_1.2-5.tar.gz","https://cran.r-project.org/src/contrib/DoE.base_1.2-5.tar.gz"],"subdirs":["noarch"],"summary":"Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.","text_prefix":false,"timestamp":1758126628,"version":"1.2_5"},"r-dofuture":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/doFuture","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/doFuture/doFuture_1.4.0.tar.gz","https://cloud.r-project.org/src/contrib/doFuture_1.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a '%dopar%' adapter such that any type of futures can be used as backends for the 'foreach' framework.","text_prefix":false,"timestamp":1552939594,"version":"1.4.0"},"r-domc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=doMC","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/doMC/doMC_1.3.8.tar.gz","https://cran.r-project.org/src/contrib/doMC_1.3.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Provides a parallel backend for the %dopar% function using the multicore functionality of the parallel package.","text_prefix":false,"timestamp":0,"version":"1.3.8"},"r-doparallel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=doParallel","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/doParallel/doParallel_1.0.17.tar.gz","https://cran.r-project.org/src/contrib/doParallel_1.0.17.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a parallel backend for the %dopar% function using the parallel package.","text_prefix":false,"timestamp":0,"version":"1.0.17"},"r-dorng":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://renozao.github.io/doRNG","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/doRNG/doRNG_1.8.6.3.tar.gz","https://cran.r-project.org/src/contrib/doRNG_1.8.6.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator [L'Ecuyer (1999), <DOI:10.1287/opre.47.1.159>]. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.","text_prefix":false,"timestamp":1539314165,"version":"1.8.6.3"},"r-dosefinding":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DoseFinding","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DoseFinding/DoseFinding_1.4-1.tar.gz","https://cran.r-project.org/src/contrib/DoseFinding_1.4-1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).","text_prefix":false,"timestamp":1563503073,"version":"1.4_1"},"r-dosnow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/doSNOW/index.html","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/doSNOW/doSNOW_1.0.20.tar.gz","https://cran.r-project.org/src/contrib/doSNOW_1.0.20.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a parallel backend for the %dopar% function using Luke Tierney's snow package.","text_prefix":false,"timestamp":0,"version":"1.0.20"},"r-dot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://haghish.com/dot","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DOT/DOT_0.1.tar.gz","https://cran.r-project.org/src/contrib/DOT_0.1.tar.gz"],"subdirs":["noarch"],"summary":"Renders DOT diagram markup language in R and also provides the possibility to export the graphs in PostScript and SVG (Scalable Vector Graphics) formats. In addition, it supports literate programming packages such as 'knitr' and 'rmarkdown'.","text_prefix":false,"timestamp":1758750124,"version":"0.1"},"r-dotcall64":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://git.math.uzh.ch/reinhard.furrer/dotCall64","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dotCall64/dotCall64_1.2.tar.gz","https://cran.r-project.org/src/contrib/dotCall64_1.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides .C64(), which is an enhanced version of .C() and .Fortran() from the foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code.","text_prefix":false,"timestamp":1621634263,"version":"1.2"},"r-dotty":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://kevinushey.github.io/dotty/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dotty/dotty_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/dotty_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a `.` object which can be used for unpacking assignments. For example, `.[rows, columns] <- dim(cars)` could be used to pull the number of rows and number of columns from `dim(cars)` into individual variables `rows` and `columns` in a single step.","text_prefix":false,"timestamp":1758125242,"version":"0.1.0"},"r-dotwhisker":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://fsolt.org/dotwhisker/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dotwhisker/dotwhisker_0.8.6.tar.gz","https://cran.r-project.org/src/contrib/dotwhisker_0.8.6.tar.gz"],"subdirs":["noarch"],"summary":"Create quick and easy dot-and-whisker plots of regression results. It takes as input either (1) a coefficient table in standard form or (2) one (or a list of) fitted model objects (of any type that has methods implemented in the 'parameters' package). It returns 'ggplot' objects that can be further customized using tools from the 'ggplot2' package. The package also includes helper functions for tasks such as rescaling coefficients or relabeling predictor variables. See more methodological discussion of the visualization and data management methods used in this package in Kastellec and Leoni (2007) <doi:10.1017/S1537592707072209> and Gelman (2008) <doi:10.1002/sim.3107>.","text_prefix":false,"timestamp":1629707719,"version":"0.8.6"},"r-downlit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://downlit.r-lib.org/, https://github.com/r-lib/downlit","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/downlit/downlit_0.4.5.tar.gz","https://cran.r-project.org/src/contrib/downlit_0.4.5.tar.gz"],"subdirs":["noarch"],"summary":"Syntax highlighting of R code, specifically designed for the needs of 'RMarkdown' packages like 'pkgdown', 'hugodown', and 'bookdown'. It includes linking of function calls to their documentation on the web, and automatic translation of ANSI escapes in output to the equivalent HTML.","text_prefix":false,"timestamp":1621946503,"version":"0.4.5"},"r-downloader":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/wch/downloader","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/downloader/downloader_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/downloader_0.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a wrapper for the download.file function, making it possible to download files over HTTPS on Windows, Mac OS X, and other Unix-like platforms. The 'RCurl' package provides this functionality (and much more) but can be difficult to install because it must be compiled with external dependencies. This package has no external dependencies, so it is much easier to install.","text_prefix":false,"timestamp":1536532382,"version":"0.4.1"},"r-downloadthis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/fmmattioni/downloadthis","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/downloadthis/downloadthis_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/downloadthis_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Implement download buttons in HTML output from 'rmarkdown' without the need for 'runtime:shiny'.","text_prefix":false,"timestamp":1758707881,"version":"0.5.0"},"r-downscaler":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SantanderMetGroup/downscaleR","doc_url":"https://github.com/SantanderMetGroup/downscaleR","home":"https://github.com/SantanderMetGroup/climate4R","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/downscaleR/archive/refs/tags/v3.3.4.tar.gz","subdirs":["noarch"],"summary":"Tools for climate data calibration (bias correction, quantile mapping etc.) and perfect-prog downscaling, as part of the climate4R framework (<http://meteo.unican.es/climate4R>).","text_prefix":false,"timestamp":1652984564,"version":"3.3.4"},"r-downscaler.keras":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SantanderMetGroup/downscaleR.keras","doc_url":"https://github.com/SantanderMetGroup/climate4R","home":"https://github.com/SantanderMetGroup/climate4R","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/downscaleR.keras/archive/refs/tags/v1.1.1.tar.gz","subdirs":["noarch"],"summary":"Tools for climate downscaling with deep neural networks models building on keras, as part of the climate4R bundle (<http://meteo.unican.es/climate4R>).","text_prefix":false,"timestamp":1758023379,"version":"1.1.1"},"r-dparser":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dparser","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/dparser/dparser_1.3.1-14.tar.gz","https://cloud.r-project.org/src/contrib/dparser_1.3.1-14.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A Scannerless GLR parser/parser generator.  Note that GLR standing for \"generalized LR\", where L stands for \"left-to-right\" and R stands for \"rightmost (derivation)\".  For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita (1987) algorithm. (Paper can be found at <http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/J/J87/J87-1004.pdf>). The original 'dparser' package documentation can be found at <http://dparser.sourceforge.net/>.  This allows you to add mini-languages to R (like RxODE's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like 'NONMEM' to automatically translate them to R code.   To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use #include <dparser.h>.  This also provides a R-based port of the make_dparser <http://dparser.sourceforge.net/d/make_dparser.cat> command called mkdparser().  Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar testing.  The fastest parsing, of course, occurs at the C level, and is suggested.","text_prefix":false,"timestamp":1665013259,"version":"1.3.1_14"},"r-dplr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/AndyBunn/dplR","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dplR/dplR_1.7.9.tar.gz","https://cran.r-project.org/src/contrib/dplR_1.7.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Perform tree-ring analyses such as detrending, chronology building, and cross dating.  Read and write standard file formats used in dendrochronology.","text_prefix":false,"timestamp":1572845940,"version":"1.7.9"},"r-dplyr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dplyr/dplyr_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/dplyr_1.2.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A fast, consistent tool for working with data frame like objects, both in memory and out of memory.","text_prefix":false,"timestamp":1557842279,"version":"1.2.1"},"r-dppackage":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.mat.puc.cl/~ajara","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DPpackage/DPpackage_1.1-7.4.tar.gz","https://cran.r-project.org/src/contrib/DPpackage_1.1-7.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions to perform inference via simulation from the posterior distributions for Bayesian nonparametric and semiparametric models. Although the name of the package was motivated by the Dirichlet Process prior, the package considers and will consider other priors on functional spaces. So far, DPpackage includes models considering Dirichlet Processes, Dependent Dirichlet Processes, Dependent Poisson- Dirichlet Processes, Hierarchical Dirichlet Processes, Polya Trees, Linear Dependent Tailfree Processes, Mixtures of Triangular distributions, Random Bernstein polynomials priors and Dependent Bernstein Polynomials.  The package also includes models considering Penalized B-Splines.  Includes semiparametric models for marginal and conditional density estimation, ROC curve analysis, interval censored data, binary regression models, generalized linear mixed models, IRT type models, and generalized additive models. Also contains functions to compute Pseudo-Bayes factors for model comparison, and to elicitate the precision parameter of the Dirichlet Process. To maximize computational efficiency, the actual sampling for each model is done in compiled FORTRAN. The functions return objects which can be subsequently analyzed with functions provided in the 'coda' package.","text_prefix":false,"timestamp":1621829318,"version":"1.1_7.4"},"r-dpq":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DPQ","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/DPQ/DPQ_0.6-2.tar.gz","https://cloud.r-project.org/src/contrib/DPQ_0.6-2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Computations for approximations and alternatives for the 'DPQ' (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. -- This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve standard R pbeta(), qgamma(), ..., etc: {'\"dpq\"'-functions}.","text_prefix":false,"timestamp":1625998921,"version":"0.6_2"},"r-dqrng":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.daqana.org/dqrng, https://github.com/daqana/dqrng","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dqrng/dqrng_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/dqrng_0.3.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Several fast random number generators are provided as C++ header only libraries: The PCG family by O'Neill (2014 <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>) as well as Xoroshiro128+ and Xoshiro256+ by Blackman and Vigna (2018 <arXiv:1805.01407>). In addition fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>). These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+ and Xoshiro256+ as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011 <doi:10.1145/2063384.2063405>) as provided by the package 'sitmo'.","text_prefix":false,"timestamp":1588589432,"version":"0.3.2"},"r-drake":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/ropensci/drake","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/drake/drake_7.13.11.tar.gz","https://cran.r-project.org/src/contrib/drake_7.13.11.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://ropensci.github.io/drake/> and the online manual <https://ropenscilabs.github.io/drake-manual/>.","text_prefix":false,"timestamp":1580587455,"version":"7.13.11"},"r-drat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://dirk.eddelbuettel.com/code/drat.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/drat/drat_0.2.5.tar.gz","https://cran.r-project.org/src/contrib/drat_0.2.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Creation and use of R Repositories via helper functions to insert packages into a repository, and to add repository information  to the current R session. Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template.","text_prefix":false,"timestamp":1535618029,"version":"0.2.5"},"r-drawer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/lz100/drawer","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/drawer/drawer_0.2.0.1.tar.gz","https://cran.r-project.org/src/contrib/drawer_0.2.0.1.tar.gz"],"subdirs":["noarch"],"summary":"An interactive image editing tool that can be added as part of the HTML in Shiny, R markdown or any type of HTML document. Often times, plots, photos are embedded in the web application/file. 'drawer' can take screenshots of these image-like elements, or any part of the HTML document and send to an image editing space called 'canvas' to allow users immediately edit the screenshot(s) within the same document. Users can quickly combine, compare different screenshots, upload their own images and maybe make a scientific figure.","text_prefix":false,"timestamp":1649667041,"version":"0.2.0.1"},"r-drawr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DRaWR","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DRaWR/DRaWR_1.0.3.tar.gz","https://cran.r-project.org/src/contrib/DRaWR_1.0.3.tar.gz"],"subdirs":["noarch"],"summary":"We present DRaWR, a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types, preserving more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only the relevant properties. We then rerank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork.","text_prefix":false,"timestamp":1621958132,"version":"1.0.3"},"r-drc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/drc/index.html","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/drc/drc_3.0-1.tar.gz","https://cran.r-project.org/src/contrib/drc_3.0-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.","text_prefix":false,"timestamp":1539304466,"version":"3.0_1"},"r-drdid":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/pedrohcgs/DRDID","home":"https://psantanna.com/DRDID/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DRDID/DRDID_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/DRDID_1.3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.","text_prefix":false,"timestamp":1781077096,"version":"1.3.0"},"r-dreamerr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dreamerr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dreamerr/dreamerr_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/dreamerr_1.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). Provides a simple, powerful and flexible way to check the arguments passed to functions. The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging.","text_prefix":false,"timestamp":1766522957,"version":"1.5.0"},"r-drimpute":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ikwak2/DrImpute","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DrImpute/DrImpute_1.0.tar.gz","https://cran.r-project.org/src/contrib/DrImpute_1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.","text_prefix":false,"timestamp":1758367203,"version":"1.0"},"r-drought4r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SantanderMetGroup/drought4R","doc_url":"https://github.com/SantanderMetGroup/drought4R","home":"https://github.com/SantanderMetGroup/climate4R","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/drought4R/archive/refs/tags/v0.3.2.tar.gz","subdirs":["noarch"],"summary":"Evapotranspiration routines and drought index calculation using the climate4R framework <https://github.com/SantanderMetGroup/climate4R>.","text_prefix":false,"timestamp":1758659917,"version":"0.3.2"},"r-drr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gdkrmr/DRR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DRR/DRR_0.0.5.tar.gz","https://cran.r-project.org/src/contrib/DRR_0.0.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An Implementation of Dimensionality Reduction via Regression using Kernel Ridge Regression.","text_prefix":false,"timestamp":1536520098,"version":"0.0.5"},"r-drtmle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/benkeser/drtmle","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/drtmle/drtmle_1.1.2.tar.gz","https://cran.r-project.org/src/contrib/drtmle_1.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ van der Laan (2014), <doi:10.1515/ijb-2012-0038>).","text_prefix":false,"timestamp":1622673170,"version":"1.1.2"},"r-dse":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://tsanalysis.r-forge.r-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dse/dse_2020.2-1.tar.gz","https://cran.r-project.org/src/contrib/dse_2020.2-1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools for multivariate, linear, time-invariant, time series models. This includes ARMA and state-space representations, and methods for converting between them. It also includes simulation methods and several estimation functions. The package has functions for looking at model roots, stability, and forecasts at different horizons. The ARMA model representation is general, so that VAR, VARX, ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman filter and smoother estimates can be obtained from the state space model, and state-space model reduction techniques are implemented. An introduction and User's Guide is available in a vignette.","text_prefix":false,"timestamp":1758651516,"version":"2020.2_1"},"r-dslabs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dslabs","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dslabs/dslabs_0.9.1.tar.gz","https://cran.r-project.org/src/contrib/dslabs_0.9.1.tar.gz"],"subdirs":["noarch"],"summary":"Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.","text_prefix":false,"timestamp":1764660564,"version":"0.9.1"},"r-dssat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=DSSAT","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DSSAT/DSSAT_0.0.9.tar.gz","https://cran.r-project.org/src/contrib/DSSAT_0.0.9.tar.gz"],"subdirs":["noarch"],"summary":"The purpose of this package is to provide a comprehensive R interface to the Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM; see <https://dssat.net> for more information). The package provides cross-platform functions to read and write input files, run DSSAT-CSM, and read output files.","text_prefix":false,"timestamp":1758559237,"version":"0.0.9"},"r-dt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://rstudio.github.io/DT","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/DT/DT_0.34.0.tar.gz","https://cran.r-project.org/src/contrib/DT_0.34.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'.","text_prefix":false,"timestamp":0,"version":"0.34.0"},"r-dtangle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dtangle","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/dtangle/dtangle_2.0.10.tar.gz","https://cloud.r-project.org/src/contrib/dtangle_2.0.10.tar.gz"],"subdirs":["noarch"],"summary":"Deconvolving cell types from high-throughput gene profiling data.","text_prefix":false,"timestamp":1567060586,"version":"2.0.10"},"r-dtplyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hadley/dtplyr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dtplyr/dtplyr_1.3.3.tar.gz","https://cran.r-project.org/src/contrib/dtplyr_1.3.3.tar.gz"],"subdirs":["noarch"],"summary":"This implements the data table back-end for 'dplyr' so that you can seamlessly use data table and 'dplyr' together.","text_prefix":false,"timestamp":1642642876,"version":"1.3.3"},"r-dtrackr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://terminological.github.io/dtrackr/index.html, https://github.com/terminological/dtrackr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dtrackr/dtrackr_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/dtrackr_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Track and document 'dplyr' data pipelines. As you filter, mutate, and join your way through a data set, 'dtrackr' seamlessly keeps track of your data flow and makes publication ready documentation of a data pipeline simple.","text_prefix":false,"timestamp":1758722381,"version":"0.5.0"},"r-dtt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.r-project.org, http://www.komsta.net/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dtt/dtt_0.1-2.1.tar.gz","https://cran.r-project.org/src/contrib/dtt_0.1-2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This package provides functions for 1D and 2D Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Hartley Transform (DHT).","text_prefix":false,"timestamp":1538309504,"version":"0.1_2.1"},"r-dtw":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"doc_url":"https://dynamictimewarping.github.io","home":"http://dtw.r-forge.r-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dtw/dtw_1.23-3.tar.gz","https://cran.r-project.org/src/contrib/dtw_1.23-3.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"A comprehensive implementation of dynamic time warping (DTW) algorithms in R.  DTW computes the optimal (least cumulative distance) alignment between points of two time series.  Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on.  Provides cumulative distances, alignments, specialized plot styles, etc.","text_prefix":false,"timestamp":1543010001,"version":"1.23_3"},"r-dtwclust":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/asardaes/dtwclust","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dtwclust/dtwclust_6.0.0.tar.gz","https://cran.r-project.org/src/contrib/dtwclust_6.0.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Time series clustering along with optimized techniques related to the Dynamic Time Warping distance and its corresponding lower bounds. Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided.  All included distance functions have custom loops optimized for the  calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.","text_prefix":false,"timestamp":1549905897,"version":"6.0.0"},"r-duckdb":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/cwida/duckdb","home":"https://duckdb.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/duckdb/duckdb_1.5.5.tar.gz","https://cran.r-project.org/src/contrib/duckdb_1.5.5.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"The DuckDB project is an embedded analytical data management system with support for the Structured Query Language (SQL). This package includes all of DuckDB and a R Database Interface (DBI) connector.","text_prefix":false,"timestamp":1623677605,"version":"1.5.5"},"r-duckdbfs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/cboettig/duckdbfs","home":"https://cboettig.github.io/duckdbfs/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/duckdbfs/duckdbfs_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/duckdbfs_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides friendly wrappers for creating 'duckdb'-backed connections to tabular datasets ('csv', parquet, etc) on local or remote file systems. This mimics the behaviour of \"open_dataset\" in the 'arrow' package, but in addition to 'S3' file system also generalizes to any list of 'http' URLs.","text_prefix":false,"timestamp":1782052645,"version":"0.1.2"},"r-duckplyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/duckdblabs/duckplyr","home":"https://duckdblabs.github.io/duckplyr/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/duckplyr/duckplyr_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/duckplyr_1.2.1.tar.gz"],"subdirs":["noarch"],"summary":"A drop-in replacement for 'dplyr', powered by 'DuckDB' for performance. Also defines a set of generics that provide a low-level implementer's interface for the high-level user interface of 'dplyr'.","text_prefix":false,"timestamp":1773964830,"version":"1.2.1"},"r-dummies":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.decisionpatterns.com","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dummies/dummies_1.5.6.tar.gz","https://cran.r-project.org/src/contrib/dummies_1.5.6.tar.gz"],"subdirs":["noarch"],"summary":"Expands factors, characters and other eligible classes into dummy/indicator variables.","text_prefix":false,"timestamp":1758470785,"version":"1.5.6"},"r-dunlin":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://insightsengineering.github.io/dunlin/, https://github.com/insightsengineering/dunlin/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dunlin/dunlin_0.1.12.tar.gz","https://cran.r-project.org/src/contrib/dunlin_0.1.12.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions to preprocess data and organize them in a format amenable to use by chevron.","text_prefix":false,"timestamp":1766438481,"version":"0.1.12"},"r-dunn.test":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=dunn.test","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/dunn.test/dunn.test_1.4.1.tar.gz","https://cran.r-project.org/src/contrib/dunn.test_1.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 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Is able to read multiple comma separated tables from a local directory, a zip file or a zip file on a remote directory.","text_prefix":false,"timestamp":1757886030,"version":"1.0.8"},"r-easydescribe":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=EasyDescribe","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/EasyDescribe/EasyDescribe_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/EasyDescribe_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Descriptive Statistics is essential for publishing articles. This package can perform descriptive statistics according to different data types. If the data is a continuous variable, the mean and standard deviation or median and quartiles are automatically output; if the data is a categorical variable, the number and percentage are automatically output. In addition, if you enter two variables, the first variable will be described hierarchically based on the second variable and the statistical differences between different groups will be compared using appropriate statistical methods. And for groups more than two, the post hoc test will be applied. For more information on the methods we used, please see the following references: Libiseller, C. and Grimvall, A. (2002) <doi:10.1002/env.507>, Patefield, W. M. (1981) <doi:10.2307/2346669>, Hope, A. C. A. (1968) <doi:10.1111/J.2517-6161.1968.TB00759.X>, Mehta, C. R. and Patel, N. R. (1983) <doi:10.1080/01621459.1983.10477989>, Mehta, C. R. and Patel, N. R. (1986) <doi:10.1145/6497.214326>, Clarkson, D. B., Fan, Y. and Joe, H. (1993) <doi:10.1145/168173.168412>, Cochran, W. G. (1954) <doi:10.2307/3001616>, Armitage, P. (1955) <doi:10.2307/3001775>, Szabo, A. (2016) <doi:10.1080/00031305.2017.1407823>, David, F. B. (1972) <doi:10.1080/01621459.1972.10481279>, Joanes, D. N. and Gill, C. A. (1998) <doi:10.1111/1467-9884.00122>, Dunn, O. J. (1964) <doi:10.1080/00401706.1964.10490181>, Copenhaver, M. D. and Holland, B. S. (1988) <doi:10.1080/00949658808811082>, Chambers, J. M., Freeny, A. and Heiberger, R. M. (1992) <doi:10.1201/9780203738535-5>, Shaffer, J. P. (1995) <doi:10.1146/annurev.ps.46.020195.003021>, Myles, H. and Douglas, A. W. (1973) <doi:10.2307/2063815>, Rahman, M. and Tiwari, R. (2012) <doi:10.4236/health.2012.410139>. Thode, H. J. 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Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package.","text_prefix":false,"timestamp":1539011857,"version":"1.4_12"},"r-ebimetagenomics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ebimetagenomics","license":"LGPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ebimetagenomics/ebimetagenomics_0.6.tar.gz","https://cran.r-project.org/src/contrib/ebimetagenomics_0.6.tar.gz"],"subdirs":["noarch"],"summary":"Functions for querying the EBI Metagenomics Portal <https://www.ebi.ac.uk/metagenomics/>. The current main focus is on taxa abundance data, but the intention is that this package should evolve into a general purpose package for working with EBI Metagenomics data using R.","text_prefix":false,"timestamp":1621999957,"version":"0.6"},"r-echarts4r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://echarts4r.john-coene.com/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/echarts4r/echarts4r_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/echarts4r_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Easily create interactive charts by leveraging the 'Echarts Javascript' library which includes 34 chart types, themes, 'Shiny' proxies and animations.","text_prefix":false,"timestamp":1550582579,"version":"0.5.0"},"r-ecodist":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/phiala/ecodist","home":"https://CRAN.R-project.org/package=ecodist","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ecodist/ecodist_2.1.3.tar.gz","https://cran.r-project.org/src/contrib/ecodist_2.1.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.","text_prefix":false,"timestamp":1588819571,"version":"2.1.3"},"r-ecodive":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Computes alpha and beta diversity metrics using concurrent 'C' threads. 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If you use this package in your research, please cite the official JOSS paper: Smith et al., (2026) <doi:10.21105/joss.09777>.","dev_url":"https://github.com/cmmr/ecodive","home":"https://cmmr.github.io/ecodive","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ecodive/ecodive_2.2.6.tar.gz","https://cran.r-project.org/src/contrib/ecodive_2.2.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Parallel and Memory-Efficient Ecological Diversity Metrics","text_prefix":false,"timestamp":1780105767,"version":"2.2.6"},"r-ecosolver":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://bnaras.github.io/ECOSolveR/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ECOSolveR/ECOSolveR_0.6.2.tar.gz","https://cloud.r-project.org/src/contrib/ECOSolveR_0.6.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"R interface to the Embedded COnic Solver (ECOS), an efficient and robust C library for convex problems. 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Two methods make use of dynamic  programming and pruning, with no distributional  assumptions other than the existence of certain absolute  moments in one method. Hierarchical and exact search methods  are included. All methods return the set of estimated change- points as well as other summary information.","text_prefix":false,"timestamp":1563710561,"version":"3.1.6"},"r-ecume":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Ecume","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Ecume/Ecume_0.9.2.tar.gz","https://cran.r-project.org/src/contrib/Ecume_0.9.2.tar.gz"],"subdirs":["noarch"],"summary":"We implement (or re-implements in R) a variety of statistical tools. They are focused on non-parametric two-sample (or k-sample) distribution comparisons in the univariate or multivariate case. See the vignette for more info.","text_prefix":false,"timestamp":1758581708,"version":"0.9.2"},"r-eddington":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/pegeler/eddington2","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/eddington/eddington_4.3.0.tar.gz","https://cran.r-project.org/src/contrib/eddington_4.3.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Compute a cyclist's Eddington number, including efficiently computing cumulative E over a vector. A cyclist's Eddington number <https://en.wikipedia.org/wiki/Arthur_Eddington#Eddington_number_for_cycling> is the maximum number satisfying the condition such that a cyclist has ridden E miles or greater in E days. The algorithm in this package is an improvement over the conventional approach because both summary statistics and cumulative statistics can be computed in linear time, since it does not require initial sorting of the data. These functions may also be used for computing h-indices for authors, a metric described by Hirsch (2005) <doi:10.1073/pnas.0507655102>. Both are specific applications of computing the side length of a Durfee square <https://en.wikipedia.org/wiki/Durfee_square>.","text_prefix":false,"timestamp":1776130630,"version":"4.3.0"},"r-edfreader":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=edfReader","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/edfReader/edfReader_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/edfReader_1.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>, BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>, and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>. The files are read in two steps: first the header is read and then the signals (using the header object as a parameter).","text_prefix":false,"timestamp":1757929153,"version":"1.2.1"},"r-edina":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tmsalab/edina","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/edina/edina_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/edina_0.1.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. 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These modules are intended for reuse across applications.","text_prefix":false,"timestamp":1758689111,"version":"0.1.8"},"r-edmdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://tmsalab.github.io/edmdata/, https://github.com/tmsalab/edmdata/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/edmdata/edmdata_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/edmdata_1.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Collection of data sets from various assessments that can be used to evaluate psychometric models. These data sets have been analyzed in the following papers that introduced new methodology as part of the application section: Chen, Y., Culpepper, S. A., & Liang, F. (2020) <doi:10.1007/s11336-019-09693-2> Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>, Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>, Culpepper, S. 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(2015) <doi:10.3102/1076998615595403>.","text_prefix":false,"timestamp":1621929717,"version":"1.3.0"},"r-edsurvey":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/American-Institutes-for-Research/EdSurvey","home":"https://www.air.org/project/nces-data-r-project-edsurvey","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/EdSurvey/EdSurvey_4.0.7.tar.gz","https://cran.r-project.org/src/contrib/EdSurvey_4.0.7.tar.gz"],"subdirs":["noarch"],"summary":"Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd).","text_prefix":false,"timestamp":1626762903,"version":"4.0.7"},"r-effects":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.r-project.org, https://socialsciences.mcmaster.ca/jfox/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/effects/effects_4.2-5.tar.gz","https://cran.r-project.org/src/contrib/effects_4.2-5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Graphical and tabular effect displays, e.g., of interactions, for  various statistical models with linear predictors.","text_prefix":false,"timestamp":1531175616,"version":"4.2_5"},"r-effectsize":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/easystats/effectsize","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/effectsize/effectsize_1.0.3.tar.gz","https://cran.r-project.org/src/contrib/effectsize_1.0.3.tar.gz"],"subdirs":["noarch"],"summary":"Provide utilities to work with indices of effect size and standardized parameters for a wide variety of models (see support list of insight; L\u00fcdecke, Waggoner & Makowski (2019) <doi:10.21105/joss.01412>), allowing computation and conversion of indices such as Cohen's d, r, odds, etc.","text_prefix":false,"timestamp":1582669288,"version":"1.0.3"},"r-effsize":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/mtorchiano/effsize/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/effsize/effsize_0.8.1.tar.gz","https://cran.r-project.org/src/contrib/effsize_0.8.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of functions to compute the standardized  effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A).  The computation algorithms have been optimized to allow efficient computation even  with very large data sets.","text_prefix":false,"timestamp":0,"version":"0.8.1"},"r-egcm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=egcm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/egcm/egcm_1.0.13.tar.gz","https://cran.r-project.org/src/contrib/egcm_1.0.13.tar.gz"],"subdirs":["noarch"],"summary":"An easy-to-use implementation of the Engle-Granger two-step procedure for identifying pairs of cointegrated series.  It is geared towards the analysis of pairs of securities.  Summary and plot functions are provided, and the package is able to fetch closing prices of securities from Yahoo.  A variety of unit root tests are supported, and an improved unit root test is included.","text_prefix":false,"timestamp":1758722054,"version":"1.0.13"},"r-egg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=egg","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/egg/egg_0.4.5.tar.gz","https://cran.r-project.org/src/contrib/egg_0.4.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Miscellaneous functions to help customise 'ggplot2' objects. High-level functions are provided to post-process 'ggplot2' layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom 'geom', and helper functions to enforce symmetric scales or add tags to facetted plots.","text_prefix":false,"timestamp":1536508942,"version":"0.4.5"},"r-eicu.demo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/eth-mds/eicu-demo","home":"https://physionet.org/content/eicu-crd-demo/2.0/","license":"ODbL-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://github.com/eth-mds/eicu-demo/archive/refs/tags/v1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"The eICU Collaborative Research Database is a multi-center database comprised of deidentified health data for over 200,000 admissions to ICUs across the United States between 2014-2015. The database includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, and treatment information. An open-access subset of eICU, comprising 2,500 unit stays selected from 20 of the larger hospitals in the eICU Collaborative Research Database is distributed under the Open Database License (ODbL) v1.0 and is provided as R package for convenience.","text_prefix":false,"timestamp":1758691131,"version":"1.0.1"},"r-eigenmodel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pdhoff.github.io/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/eigenmodel/eigenmodel_1.12.tar.gz","https://cran.r-project.org/src/contrib/eigenmodel_1.12.tar.gz"],"subdirs":["noarch"],"summary":"Estimation of the parameters in a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accommodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification. See Hoff (2007) <arXiv:0711.1146> for details on the model.","text_prefix":false,"timestamp":1768895349,"version":"1.12"},"r-eikosograms":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rwoldford/eikosograms","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/eikosograms/eikosograms_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/eikosograms_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths, represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes theorem as a completely trivial consequence). They are markedly superior to Venn diagrams for this purpose, especially in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. As data analysis tools, eikosograms display categorical data in a manner similar to Mosaic plots, especially when only two variates are involved (the only case in which they are essentially identical, though eikosograms purposely disallow spacing between rectangles). Unlike Mosaic plots, eikosograms do not alternate axes as each new categorical variate (beyond two) is introduced. Instead, only one categorical variate, designated the \"response\", presents on the vertical axis and all others, designated the \"conditioning\" variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is therefore much better suited to a response model analysis (e.g. logistic model) than is a Mosaic plot. Mosaic plots are better suited to log-linear style modelling as in discrete multivariate analysis. Of course, eikosograms are also suited to discrete multivariate analysis with each variate in turn appearing as the response. This makes it better suited than Mosaic plots to discrete graphical models based on conditional independence graphs (i.e. \"Bayesian Networks\" or \"BayesNets\"). The eikosogram and its superiority to Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>, and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.","text_prefix":false,"timestamp":1768169768,"version":"1.0.0"},"r-einsum":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=einsum","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/einsum/einsum_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/einsum_0.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The summation notation suggested by Einstein (1916) <doi:10.1002/andp.19163540702> is a concise mathematical notation that implicitly sums over repeated indices of n-dimensional arrays. Many ordinary matrix operations (e.g. transpose, matrix multiplication, scalar product, 'diag()', trace etc.) can be written using Einstein notation. The notation is particularly convenient for expressing operations on arrays with more than two dimensions because the respective operators ('tensor products') might not have a standardized name.","text_prefix":false,"timestamp":1783602444,"version":"0.2.0"},"r-elasticnet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.stat.umn.edu/~hzou","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/elasticnet/elasticnet_1.3.tar.gz","https://cran.r-project.org/src/contrib/elasticnet_1.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for estimating sparse Principal Components. The Lasso solution paths can be computed by the same function. First version: 2005-10.","text_prefix":false,"timestamp":1524085403,"version":"1.3"},"r-elitism":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=elitism","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/elitism/elitism_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/elitism_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name \"elitism\" is an acronym of \"e\"quipment for \"l\"ogarithmic and l\"i\"near \"ti\"me \"s\"tepwise \"m\"ultiple hypothesis testing. Version 1.0.0 was released on June 26, 2019. See Gou, J., and Zhang, F. (2020). Quick multiple test procedures and p-value adjustments. Technical report.","text_prefix":false,"timestamp":1758714778,"version":"1.1.1"},"r-ellipse":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ellipse","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ellipse/ellipse_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/ellipse_0.5.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains various routines for drawing ellipses and ellipse-like confidence regions, implementing the plots described in Murdoch and Chow (1996), A graphical display of large correlation matrices, The American Statistician 50, 178-180. There are also routines implementing the profile plots described in Bates and Watts (1988), Nonlinear Regression Analysis and its Applications.","text_prefix":false,"timestamp":0,"version":"0.5.0"},"r-ellipsis":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://ellipsis.r-lib.org, https://github.com/r-lib/ellipsis","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ellipsis/ellipsis_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/ellipsis_0.3.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The ellipsis is a powerful tool for extending functions. Unfortunately this power comes at a cost: misspelled arguments will be silently ignored. The ellipsis package provides a collection of functions to catch problems and alert the user.","text_prefix":false,"timestamp":1551047470,"version":"0.3.3"},"r-elliptic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RobinHankin/elliptic.git","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/elliptic/elliptic_1.5-1.tar.gz","https://cran.r-project.org/src/contrib/elliptic_1.5-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A suite of elliptic and related functions including Weierstrass and Jacobi forms.  Also includes various tools for manipulating and visualizing complex functions.","text_prefix":false,"timestamp":1536430513,"version":"1.5_1"},"r-ellmer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidyverse/ellmer","home":"https://ellmer.tidyverse.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ellmer/ellmer_0.5.0.tar.gz","https://cloud.r-project.org/src/contrib/ellmer_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Chat with large language models from a range of providers including 'Claude' <https://claude.ai>, 'OpenAI' <https://chatgpt.com>, and more. Supports streaming, asynchronous calls, tool calling, and structured data extraction.","text_prefix":false,"timestamp":1789500378,"version":"0.5.0"},"r-elpigraph.r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Albluca/ElPiGraph.R","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/Albluca/ElPiGraph.R/archive/251415810d1cf2a973eaa51d0f8e520e14dbe1cb.zip","subdirs":["linux-64","osx-64"],"summary":"An R package to construct Elastic Principal Graphs","text_prefix":false,"timestamp":1758753931,"version":"1.0.0"},"r-elsa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://r-gis.net","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/elsa/elsa_1.1-28.tar.gz","https://cran.r-project.org/src/contrib/elsa_1.1-28.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) <doi:10.1016/j.spasta.2018.10.001>.","text_prefix":false,"timestamp":1687179116,"version":"1.1_28"},"r-emayili":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://datawookie.github.io/emayili/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emayili/emayili_0.9.3.tar.gz","https://cran.r-project.org/src/contrib/emayili_0.9.3.tar.gz"],"subdirs":["noarch"],"summary":"A light, simple tool for sending emails with minimal dependencies.","text_prefix":false,"timestamp":1627629441,"version":"0.9.3"},"r-embed":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://embed.tidymodels.org, https://github.com/tidymodels/embed","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/embed/embed_1.2.2.tar.gz","https://cran.r-project.org/src/contrib/embed_1.2.2.tar.gz"],"subdirs":["noarch"],"summary":"Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <arXiv:1611.09477> or nonlinear models <arXiv:1604.06737> can be used.  There are also functions for dimension reduction and other approaches.","text_prefix":false,"timestamp":1654758521,"version":"1.2.2"},"r-emc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=EMC","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/EMC/EMC_1.3.tar.gz","https://cran.r-project.org/src/contrib/EMC_1.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement","text_prefix":false,"timestamp":1758252656,"version":"1.3"},"r-emcluster":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/snoweye/EMCluster","license":"MPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/EMCluster/EMCluster_0.2-18.tar.gz","https://cloud.r-project.org/src/contrib/EMCluster_0.2-18.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.","text_prefix":false,"timestamp":1552833671,"version":"0.2_18"},"r-emdbook":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.math.mcmaster.ca/bolker/emdbook","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emdbook/emdbook_1.3.14.tar.gz","https://cran.r-project.org/src/contrib/emdbook_1.3.14.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Auxiliary functions and data sets for \"Ecological Models and Data\", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).","text_prefix":false,"timestamp":0,"version":"1.3.14"},"r-emdist":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.rforge.net/emd","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emdist/emdist_0.3-3.tar.gz","https://cran.r-project.org/src/contrib/emdist_0.3-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Package providing calculation of Earth Mover's Distance (EMD).","text_prefix":false,"timestamp":1588817314,"version":"0.3_3"},"r-eml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/EML","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/EML/EML_2.0.7.tar.gz","https://cran.r-project.org/src/contrib/EML_2.0.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Work with Ecological Metadata Language ('EML') files.  'EML' is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), <doi:10.1146/annurev.ecolsys.37.091305.110031>.","text_prefix":false,"timestamp":1539297384,"version":"2.0.7"},"r-emlassemblyline":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/EDIorg/EMLassemblyline","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/EDIorg/EMLassemblyline/archive/refs/tags/v3.6.0.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"For scientists and data managers to create high quality EML metadata for dataset publication. EMLassemblyline is optimized for automating recurring publications (timeseries or data derived from timeseries sources) but works well for \"one-off\" publications, especially through the MetaShARK interface. EMLassemblyline prioritizes automated metadata extraction from data objects to minimize required human effort and encourages EML best practices to make publications Findable, Accessible, Interoperable, and Reusable.","text_prefix":false,"timestamp":1758904356,"version":"3.6.0"},"r-emld":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/emld","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emld/emld_0.5.3.tar.gz","https://cran.r-project.org/src/contrib/emld_0.5.3.tar.gz"],"subdirs":["noarch"],"summary":"This is a utility for transforming Ecological Metadata Language ('EML') files into 'JSON-LD' and back into 'EML.'  Doing so creates a list-based representation of 'EML' in R, so that 'EML' data can easily be manipulated using standard 'R' tools. This makes this package an effective backend for other 'R'-based tools  working with 'EML.' By abstracting away the complexity of 'XML' Schema, developers can build around native 'R' list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the 'JSON-LD'  representation enables the use of developer-friendly 'JSON' parsing and serialization that may facilitate the use of 'EML' in contexts outside of 'R,' as well as the informatics-friendly serializations such as 'RDF' and 'SPARQL' queries.","text_prefix":false,"timestamp":1594249113,"version":"0.5.3"},"r-emmeans":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rvlenth/emmeans","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emmeans/emmeans_2.0.4.tar.gz","https://cran.r-project.org/src/contrib/emmeans_2.0.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Obtain estimated marginal means (EMMs) for many linear, generalized  linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term \"estimated marginal means\" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means  in the linear model: An alternative to least squares means, The American  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.","text_prefix":false,"timestamp":0,"version":"2.0.4"},"r-emoa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=emoa","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emoa/emoa_0.5-3.tar.gz","https://cran.r-project.org/src/contrib/emoa_0.5-3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Collection of building blocks for the design and analysis of evolutionary multiobjective optimization algorithms.","text_prefix":false,"timestamp":1758712020,"version":"0.5_3"},"r-emojifont":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://guangchuangyu.github.io/emojifont","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emojifont/emojifont_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/emojifont_0.6.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An implementation of using emoji and fontawesome for using in both base and 'ggplot2' graphics.","text_prefix":false,"timestamp":1588816815,"version":"0.6.0"},"r-emstreer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/allanvc/emstreeR","home":"https://CRAN.R-project.org/package=emstreeR","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emstreeR/emstreeR_3.2.0.tar.gz","https://cran.r-project.org/src/contrib/emstreeR_3.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Fast and easily computes an Euclidean Minimum Spanning Tree (EMST) from data, relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013). 'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>), which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides functions and an S3 method for readily plotting Minimum Spanning Trees (MST) using either the style of the 'base', 'scatterplot3d', or 'ggplot2' libraries.","text_prefix":false,"timestamp":1785244791,"version":"3.2.0"},"r-emt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=EMT","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/EMT/EMT_1.3.2.tar.gz","https://cran.r-project.org/src/contrib/EMT_1.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The package provides functions to carry out a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. A p-value can be calculated using different distance measures between observed and expected frequencies. A Monte Carlo method is provided to make the package capable of solving high-dimensional problems.","text_prefix":false,"timestamp":1539221505,"version":"1.3.2"},"r-emulator":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RobinHankin/emulator","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/emulator/emulator_1.2-24.tar.gz","https://cran.r-project.org/src/contrib/emulator_1.2-24.tar.gz"],"subdirs":["noarch"],"summary":"Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output.  This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point.  The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class.  The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model.  The package includes functionality to evaluate quadratic forms efficiently.","text_prefix":false,"timestamp":1757913509,"version":"1.2_24"},"r-enc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/krlmlr/enc","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/enc/enc_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/enc_0.2.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements an S3 class for storing 'UTF-8' strings, based on regular character vectors. Also contains routines to portably read and write 'UTF-8' encoded text files, to convert all strings in an object to 'UTF-8', and to create character vectors with various encodings.","text_prefix":false,"timestamp":1563566504,"version":"0.2.2"},"r-encryptedrmd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dirkschumacher/encryptedRmd","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/encryptedRmd/encryptedRmd_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/encryptedRmd_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Create encrypted html files that are fully self contained and do not require any additional software. Using the package you can encrypt arbitrary html files and also directly create encrypted 'rmarkdown' html reports.","text_prefix":false,"timestamp":1758491248,"version":"0.2.1"},"r-energy":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/mariarizzo/energy","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/energy/energy_1.7-12.tar.gz","https://cran.r-project.org/src/contrib/energy_1.7-12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"E-statistics (energy) tests and statistics for multivariate and univariate inference, including distance correlation, one-sample, two-sample, and multi-sample tests for comparing multivariate distributions, are implemented. Measuring and testing multivariate independence based on distance correlation, partial distance correlation, multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy  distance, testing for multivariate normality, distance components (disco) for non-parametric  analysis of structured data, and other energy statistics/methods are implemented.","text_prefix":false,"timestamp":1567887287,"version":"1.7_12"},"r-english":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=english","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/english/english_1.2-6.tar.gz","https://cran.r-project.org/src/contrib/english_1.2-6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Allow numbers to be presented in an English language version, one, two, three, ...  Ordinals are also available, first, second, third, ...","text_prefix":false,"timestamp":1524264098,"version":"1.2_6"},"r-enmeval":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ENMeval","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ENMeval/ENMeval_2.0.5.2.tar.gz","https://cran.r-project.org/src/contrib/ENMeval_2.0.5.2.tar.gz"],"subdirs":["noarch"],"summary":"Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics.  Current version only implements ENMs with Maxent (Phillips et al. 2006) or maxnet (Phillips et al. 2017).","text_prefix":false,"timestamp":1616413308,"version":"2.0.5.2"},"r-enrichr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=enrichR","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/enrichR/enrichR_3.4.tar.gz","https://cran.r-project.org/src/contrib/enrichR_3.4.tar.gz"],"subdirs":["noarch"],"summary":"Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See (<http://amp.pharm.mssm.edu/Enrichr/>) for further details.","text_prefix":false,"timestamp":1622082437,"version":"3.4"},"r-enrichwith":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ikosmidis/enrichwith","license":"GPL-2 | GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/enrichwith/enrichwith_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/enrichwith_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides the \"enrich\" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm', 'glm' and 'betareg'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.","text_prefix":false,"timestamp":1777493746,"version":"0.5.0"},"r-enshuman":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=enshuman","license":"LGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/enshuman/enshuman_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/enshuman_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Gene information from 'Ensembl' genome builds 'GRCh38.p14' and 'GRCh37.p13' to use. with the 'topr' package. The datasets were originally downloaded from <https://ftp.ensembl.org/pub/current/gtf/homo_sapiens/Homo_sapiens.GRCh38.111.gtf.gz> and <https://ftp.ensembl.org/pub/grch37/current/gtf/homo_sapiens/Homo_sapiens.GRCh37.87.gtf.gz> and converted into the format required by the 'topr' package. See <https://github.com/totajuliusd/topr?tab=readme-ov-file#how-to-use-topr-with-other-species-than-human> to see the required format.","text_prefix":false,"timestamp":1758619508,"version":"1.0.0"},"r-ensmodelvis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/domijan/ensModelVis","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ensModelVis/ensModelVis_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/ensModelVis_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Displays for model fits of multiple models and their ensembles. For classification models, the plots are heatmaps, for regression, scatterplots.","text_prefix":false,"timestamp":1768970023,"version":"0.2.0"},"r-ensr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/dewittpe/ensr","doc_url":"https://cran.r-project.org/web/packages/ensr/ensr.pdf","home":"https://github.com/dewittpe/ensr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ensr/ensr_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/ensr_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Elastic net regression models are controlled by two parameters, lambda, a measure of shrinkage, and alpha, a metric defining the model's location on the spectrum between ridge and lasso regression. glmnet provides tools for selecting lambda via cross validation but no automated methods for selection of alpha.  Elastic Net SearcheR automates the simultaneous selection of both lambda and alpha. Developed, in part, with support by NICHD R03 HD094912.","text_prefix":false,"timestamp":1758633933,"version":"0.1.0"},"r-ensurer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/smbache/ensurer","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ensurer/ensurer_1.1.tar.gz","https://cran.r-project.org/src/contrib/ensurer_1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Add simple runtime contracts to R values. These ensure that values fulfil certain conditions and will raise appropriate errors if they do not.","text_prefix":false,"timestamp":1540839709,"version":"1.1"},"r-entropy":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://strimmerlab.org/software/entropy/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/entropy/entropy_1.3.2.tar.gz","https://cran.r-project.org/src/contrib/entropy_1.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This package implements various estimators of entropy, such as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood  and the Millow-Madow estimator, various Bayesian estimators, and the  Chao-Shen estimator.  It also offers an R interface to the NSB estimator. Furthermore, it provides functions for estimating Kullback-Leibler divergence, chi-squared, mutual information, and chi-squared statistic of independence. In addition there are functions for discretizing continuous random variables.","text_prefix":false,"timestamp":1539213365,"version":"1.3.2"},"r-entropyexplorer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=EntropyExplorer","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/EntropyExplorer/EntropyExplorer_1.1.tar.gz","https://cran.r-project.org/src/contrib/EntropyExplorer_1.1.tar.gz"],"subdirs":["noarch"],"summary":"Rows of two matrices are compared for Shannon entropy, coefficient of variation, and expression. P-values can be requested for all metrics.","text_prefix":false,"timestamp":1758600566,"version":"1.1"},"r-envipat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.envipat.eawag.ch/, http://pubs.acs.org/doi/abs/10.1021/acs.analchem.5b00941","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/enviPat/enviPat_2.8.tar.gz","https://cran.r-project.org/src/contrib/enviPat_2.8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fast and very memory-efficient calculation of isotope patterns, subsequent convolution to theoretical envelopes (profiles) plus valley detection and centroidization or intensoid calculation. Batch processing, resolution interpolation, wrapper, adduct calculations and molecular formula parsing. Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) <doi:10.1021/acs.analchem.5b00941>.","text_prefix":false,"timestamp":1665081907,"version":"2.8"},"r-envstats":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/alexkowa/EnvStats","home":"https://alexkowa.github.io/EnvStats/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/EnvStats/EnvStats_3.1.0.tar.gz","https://cran.r-project.org/src/contrib/EnvStats_3.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Graphical and statistical analyses of environmental data, with  focus on analyzing chemical concentrations and physical parameters, usually in  the context of mandated environmental monitoring.  Major environmental  statistical methods found in the literature and regulatory guidance documents,  with extensive help that explains what these methods do, how to use them,  and where to find them in the literature.  Numerous built-in data sets from  regulatory guidance documents and environmental statistics literature.  Includes  scripts reproducing analyses presented in the book \"EnvStats: An R Package for  Environmental Statistics\" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4,  <http://www.springer.com/book/9781461484554>).","text_prefix":false,"timestamp":1532461961,"version":"3.1.0"},"r-epi":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://BendixCarstensen.com/Epi/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Epi/Epi_2.66.tar.gz","https://cran.r-project.org/src/contrib/Epi_2.66.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data, in particular representation, manipulation and simulation of multistate  data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages.  Also contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.","text_prefix":false,"timestamp":1649866949,"version":"2.66"},"r-epir":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://fvas.unimelb.edu.au/research/groups/veterinary-epidemiology-melbourne","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/epiR/epiR_2.0.99.tar.gz","https://cloud.r-project.org/src/contrib/epiR_2.0.99.tar.gz"],"subdirs":["noarch"],"summary":"Tools for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.","text_prefix":false,"timestamp":1641582350,"version":"2.0.99"},"r-epitools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=epitools","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/epitools/epitools_0.5-10.1.tar.gz","https://cran.r-project.org/src/contrib/epitools_0.5-10.1.tar.gz"],"subdirs":["noarch"],"summary":"Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.","text_prefix":false,"timestamp":1758565889,"version":"0.5_10.1"},"r-equalityml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://equalityai.com","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://github.com/EqualityAI/EqualityML/archive/refs/tags/v0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Algorithms for evaluating fairness metrics and mitigating unfairness in supervised machine learning","text_prefix":false,"timestamp":1758636996,"version":"0.2.1"},"r-ergm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://statnet.org","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ergm/ergm_4.12.0.tar.gz","https://cran.r-project.org/src/contrib/ergm_4.12.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 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In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes.  Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping.  Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included.  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It contains also functions for simplifying some clustering analysis steps and provides 'ggplot2' - based elegant data visualization.","text_prefix":false,"timestamp":1539315409,"version":"2.2.0"},"r-factominer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/husson/FactoMineR","home":"http://factominer.free.fr","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/FactoMineR/FactoMineR_2.17.tar.gz","https://cloud.r-project.org/src/contrib/FactoMineR_2.17.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Exploratory data analysis methods to summarize, visualize and describe datasets. 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Pages (2017).","text_prefix":false,"timestamp":1666020137,"version":"2.17"},"r-factormerger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/MI2DataLab/factorMerger","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/factorMerger/factorMerger_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/factorMerger_0.4.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The Merging Path Plot is a methodology for adaptive fusing of k-groups  with likelihood-based model selection. This package contains tools for  exploration and visualization of k-group dissimilarities.  Comparison of k-groups is one of the most important issues in exploratory analyses and it has zillions of applications.  The traditional approach is to use pairwise post hoc tests in order to verify which groups differ significantly. 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Included are the families of Archemedean, Elliptical, Extreme Value, and Empirical Copulae.","text_prefix":false,"timestamp":1771757156,"version":"4052.86"},"r-fcps":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.deepbionics.org/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/FCPS/FCPS_1.4.3.tar.gz","https://cloud.r-project.org/src/contrib/FCPS_1.4.3.tar.gz"],"subdirs":["noarch"],"summary":"Over sixty clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. 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This  method may be useful for high-throughput biological data (micro-array, sequencing, ...). Probabilities are associated with genes or probes in the data set and there is no problem of multiple tests when using this method. For array-based comparative genomic hybridization data, segmentation results are obtained by merging the significant probes detected.","text_prefix":false,"timestamp":1550708067,"version":"1.6_6"},"r-fd":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=FD","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/FD/FD_1.0-12.6.tar.gz","https://cloud.r-project.org/src/contrib/FD_1.0-12.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"FD is a package to compute different multidimensional FD indices. 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O., Hooker, Giles, and Graves, Spencer (2009) Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book.  Matlab versions of the code and sample analyses are no longer distributed through CRAN, as they were when the book was published.  For those, ftp from <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/> There you find a set of .zip files containing the functions and sample analyses, as well as two .txt files giving instructions for installation and some additional information. The changes from Version 2.4.1 are fixes of bugs in density.fd and removal of functions create.polynomial.basis, polynompen, and polynomial. 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In addition, the package  contains functions for non-parametric density estimation (Grenander estimator),  for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM),  for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold.","text_prefix":false,"timestamp":1588560297,"version":"1.2.18"},"r-fds":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fds","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fds/fds_1.9.tar.gz","https://cran.r-project.org/src/contrib/fds_1.9.tar.gz"],"subdirs":["noarch"],"summary":"Functional data sets.","text_prefix":false,"timestamp":1778142431,"version":"1.9"},"r-feasts":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://feasts.tidyverts.org/, https://github.com/tidyverts/feasts/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/feasts/feasts_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/feasts_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. 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It is designed for causal panel analysis with binary treatments under the parallel trends assumption. The package supports scenarios where treatments can switch on and off and allows for limited carryover effects. It includes several imputation estimators, such as Gsynth (Xu 2017), linear factor models, and the matrix completion method. Detailed methodology is described in Liu, Wang, and Xu (2024) <doi:10.48550/arXiv.2107.00856> and Chiu et al. (2025) <doi:10.48550/arXiv.2309.15983>. Optionally integrates with the \"HonestDiDFEct\" package for sensitivity analyses compatible with imputation estimators. \"HonestDiDFEct\" is not on CRAN but can be obtained from <https://github.com/lzy318/HonestDiDFEct>.","text_prefix":false,"timestamp":1783425077,"version":"2.4.5"},"r-fedmatch":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fedmatch","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fedmatch/fedmatch_2.1.0.tar.gz","https://cran.r-project.org/src/contrib/fedmatch_2.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides a flexible set of tools for matching two un-linked data sets. 'fedmatch' allows for three ways to match data: exact matches, fuzzy matches, and multi-variable matches. 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For example ''quad'' allows efficient  storage of genomic data as an ''A'',''T'',''G'',''C'' factor. The unsigned  types support ''circular'' arithmetic. There is also support for  close-to-atomic types ''factor'', ''ordered'', ''POSIXct'', ''Date'' and  custom close-to-atomic types.  ff not only has native C-support for vectors, matrices and arrays  with flexible dimorder (major column-order, major row-order and  generalizations for arrays). There is also a ffdf class not unlike  data.frames and import/export filters for csv files. ff objects store raw data in binary flat files in native encoding, and complement this with metadata stored in R as physical and virtual attributes. ff objects have well-defined hybrid copying semantics,  which gives rise to certain performance improvements through  virtualization. ff objects can be stored and reopened across R  sessions. ff files can be shared by multiple ff R objects  (using different data en/de-coding schemes) in the same process  or from multiple R processes to exploit parallelism. A wide choice of  finalizer options allows to work with ''permanent'' files as well as  creating/removing ''temporary'' ff files completely transparent to the  user. On certain OS/Filesystem combinations, creating the ff files works without notable delay thanks to using sparse file allocation. Several access optimization techniques such as Hybrid Index  Preprocessing and Virtualization are implemented to achieve good  performance even with large datasets, for example virtual matrix  transpose without touching a single byte on disk. Further, to reduce  disk I/O, ''logicals'' and non-standard data types get stored native and  compact on binary flat files i.e. logicals take up exactly 2 bits to  represent TRUE, FALSE and NA.  Beyond basic access functions, the ff package also provides  compatibility functions that facilitate writing code for ff and ram  objects and support for batch processing on ff objects (e.g. as.ram,  as.ff, ffapply). ff interfaces closely with functionality from package  ''bit'': chunked looping, fast bit operations and coercions between  different objects that can store subscript information (''bit'',  ''bitwhich'', ff ''boolean'', ri range index, hi hybrid index). This allows to work interactively with selections of large datasets and quickly  modify selection criteria.  Further high-performance enhancements can be made available upon request.","text_prefix":false,"timestamp":1563499050,"version":"4.5.3"},"r-ffbase":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://github.com/edwindj/ffbase","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ffbase/ffbase_0.13.3.tar.gz","https://cran.r-project.org/src/contrib/ffbase_0.13.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Extends the out of memory vectors of 'ff' with statistical functions and other utilities to ease their usage.","text_prefix":false,"timestamp":1598376112,"version":"0.13.3"},"r-ffield":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=FField","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FField/FField_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/FField_0.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Force field simulation of interaction of set of points. 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This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). 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The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter).  A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview.  The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding  source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation  Grant 1417857 and the National Center for Atmospheric Research. See the Fields URL for a vignette on using this package and some background on spatial statistics.","text_prefix":false,"timestamp":1599092204,"version":"17.3"},"r-filearray":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dipterix/filearray","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/filearray/filearray_0.2.4.tar.gz","https://cloud.r-project.org/src/contrib/filearray_0.2.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Stores large arrays in files to avoid occupying large memories. Implemented with super fast gigabyte-level multi-threaded reading/writing via 'OpenMP'. 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However, it still has a small number of unique, handy file manipulation  functions which can be seen in the vignette.  One example is a function to remove spaces from all file names in a  directory.","text_prefix":false,"timestamp":1543003835,"version":"3.4.0"},"r-fillpattern":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/cmmr/fillpattern","home":"https://cmmr.github.io/fillpattern/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/fillpattern/fillpattern_1.0.4.tar.gz","https://cloud.r-project.org/src/contrib/fillpattern_1.0.4.tar.gz"],"subdirs":["noarch"],"summary":"Adds distinctive yet unobtrusive geometric patterns where solid color fills are normally used. Patterned figures look just as professional when viewed by colorblind readers or when printed in black and white. 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Explore models and export directly to PDF and 'Word' using 'RMarkdown'.","text_prefix":false,"timestamp":1758250609,"version":"1.1.0"},"r-finch":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/finch","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/finch/finch_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/finch_0.4.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple and Archives. Functionality includes reading and parsing all the files in a Darwin Core Archive, including the datasets and metadata; read and parse simple Darwin Core files; and validation of Darwin Core Archives.","text_prefix":false,"timestamp":1539354749,"version":"0.4.0"},"r-findpython":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/trevorld/findpython","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/findpython/findpython_1.0.9.tar.gz","https://cran.r-project.org/src/contrib/findpython_1.0.9.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Package designed to find an acceptable python binary.","text_prefix":false,"timestamp":1540067351,"version":"1.0.9"},"r-finetune":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidymodels/finetune","home":"https://finetune.tidymodels.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/finetune/finetune_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/finetune_1.2.1.tar.gz"],"subdirs":["noarch"],"summary":"The ability to tune models is important. 'finetune' enhances the 'tune' package by providing more specialized methods for finding reasonable values of model tuning parameters.  Two racing methods described by Kuhn (2014) <arXiv:1405.6974> are included. An iterative search method using generalized simulated annealing (Bohachevsky, Johnson and Stein, 1986) <doi:10.1080/00401706.1986.10488128> is also included.","text_prefix":false,"timestamp":1759621436,"version":"1.2.1"},"r-fingerprint":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fingerprint","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fingerprint/fingerprint_3.5.10.tar.gz","https://cran.r-project.org/src/contrib/fingerprint_3.5.10.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions to manipulate binary fingerprints of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint' which is internally represented a vector of integers, such that each element represents the position in the fingerprint that is set to 1. The bitwise logical functions in R are overridden so that they can be used directly with 'fingerprint' objects. A number of distance metrics are also available (many contributed by Michael Fadock). Fingerprints  can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and can also be folded using OR.  Arbitrary fingerprint formats can be handled via line handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.","text_prefix":false,"timestamp":1758127189,"version":"3.5.10"},"r-firedanger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SantanderMetGroup/fireDanger","doc_url":"https://github.com/SantanderMetGroup/fireDanger","home":"https://github.com/SantanderMetGroup/climate4R","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/fireDanger/archive/refs/tags/v1.2.0.tar.gz","subdirs":["noarch"],"summary":"A climate4R package for computing several popular fire danger indices.","text_prefix":false,"timestamp":1758092806,"version":"1.2.0"},"r-fit.models":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fit.models","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fit.models/fit.models_0.64.tar.gz","https://cran.r-project.org/src/contrib/fit.models_0.64.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally.","text_prefix":false,"timestamp":1538442083,"version":"0.64"},"r-fitdistrplus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"MailingList: http://listes.univ-lyon1.fr/wws/info/fitdist-users; https://lbbe.univ-lyon1.fr/fitdistrplus.html, http://riskassessment.r-forge.r-project.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fitdistrplus/fitdistrplus_1.2-6.tar.gz","https://cran.r-project.org/src/contrib/fitdistrplus_1.2-6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available. See e.g. Casella & Berger (2002). Statistical inference. Pacific Grove.","text_prefix":false,"timestamp":1540036895,"version":"1.2_6"},"r-fivethirtyeight":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rudeboybert/fivethirtyeight","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fivethirtyeight/fivethirtyeight_0.6.2.tar.gz","https://cran.r-project.org/src/contrib/fivethirtyeight_0.6.2.tar.gz"],"subdirs":["noarch"],"summary":"Datasets and code published by the data journalism website  'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>.  Note that while we received guidance from editors at 'FiveThirtyEight', this  package is not officially published by 'FiveThirtyEight'.","text_prefix":false,"timestamp":1588805615,"version":"0.6.2"},"r-fixest":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/lrberge/fixest","home":"https://lrberge.github.io/fixest/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fixest/fixest_0.14.2.tar.gz","https://cran.r-project.org/src/contrib/fixest_0.14.2.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://wwwen.uni.lu/content/download/110162/1299525/file/2018_13>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.","text_prefix":false,"timestamp":1632843301,"version":"0.14.2"},"r-flare":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=flare","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flare/flare_1.8.tar.gz","https://cran.r-project.org/src/contrib/flare_1.8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.","text_prefix":false,"timestamp":1588806921,"version":"1.8"},"r-flashclust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=flashClust","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flashClust/flashClust_1.1-4.tar.gz","https://cran.r-project.org/src/contrib/flashClust_1.1-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fast implementation of hierarchical clustering","text_prefix":false,"timestamp":1772597813,"version":"1.1_4"},"r-flatxml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jsugarelli/flatxml/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flatxml/flatxml_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/flatxml_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"On import, the XML information is converted to a dataframe that reflects the hierarchical XML structure. Intuitive functions allow to navigate within this transparent XML data structure (without any knowledge of 'XPath'). 'flatXML' also provides tools to extract data from the XML into a flat dataframe that can be used to perform statistical operations. It also supports converting dataframes to XML.","text_prefix":false,"timestamp":1758610569,"version":"0.1.1"},"r-flexclust":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=flexclust","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flexclust/flexclust_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/flexclust_1.5.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.","text_prefix":false,"timestamp":1542701234,"version":"1.5.0"},"r-flexdashboard":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://rmarkdown.rstudio.com/flexdashboard","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flexdashboard/flexdashboard_0.6.3.tar.gz","https://cran.r-project.org/src/contrib/flexdashboard_0.6.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Format for converting an R Markdown document to a grid oriented dashboard. The dashboard flexibly adapts the size of it's components to the containing web page.","text_prefix":false,"timestamp":1540064190,"version":"0.6.3"},"r-flexmix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=flexmix","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flexmix/flexmix_2.3-20.tar.gz","https://cran.r-project.org/src/contrib/flexmix_2.3-20.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.","text_prefix":false,"timestamp":0,"version":"2.3_20"},"r-flexoki":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/christopherkenny/flexoki","home":"https://christophertkenny.com/flexoki/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flexoki/flexoki_0.0.2.tar.gz","https://cran.r-project.org/src/contrib/flexoki_0.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides color palettes designed to be reminiscent of text on paper. The color schemes were taken from <https://stephango.com/flexoki>. Includes discrete, continuous, and binned scales that are not necessarily color-blind friendly. Simple scale and theme functions are available for use with 'ggplot2'.","text_prefix":false,"timestamp":1778250357,"version":"0.0.2"},"r-flexparamcurve":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pennstate.academia.edu:443/SteveOswald","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/FlexParamCurve/FlexParamCurve_1.5-8.tar.gz","https://cloud.r-project.org/src/contrib/FlexParamCurve_1.5-8.tar.gz"],"subdirs":["noarch"],"summary":"Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.","text_prefix":false,"timestamp":1788349571,"version":"1.5_8"},"r-flexplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dustinfife/flexplot","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://github.com/dustinfife/flexplot/archive/refs/tags/v0.7.4.tar.gz"],"subdirs":["noarch"],"summary":"A a graphically-based set of tools for doing data analysis.","text_prefix":false,"timestamp":1758314213,"version":"0.7.4"},"r-flexsurv":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/chjackson/flexsurv-dev","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flexsurv/flexsurv_2.3.2.tar.gz","https://cran.r-project.org/src/contrib/flexsurv_2.3.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions.  Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. There are also tools for fitting and predicting from fully parametric multi-state models.","text_prefix":false,"timestamp":1665887768,"version":"2.3.2"},"r-flextable":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/davidgohel/flextable","doc_url":"https://ardata-fr.github.io/flextable-book/","home":"https://davidgohel.github.io/flextable","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/flextable/flextable_0.10.3.tar.gz","https://cloud.r-project.org/src/contrib/flextable_0.10.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Use a grammar for creating and customizing pretty tables. The following formats are supported: 'HTML', 'PDF', 'RTF', 'Microsoft Word', 'Microsoft PowerPoint' and R 'Grid Graphics'. 'R Markdown', 'Quarto' and the package 'officer' can be used to produce the result files. The syntax is the same for the user regardless of the type of output to be produced. A set of functions allows the creation, definition of cell arrangement, addition of headers or footers, formatting and definition of cell content with text and or images. The package also offers a set of high-level functions that allow tabular reporting of statistical models and the creation of complex cross tabulations.","text_prefix":false,"timestamp":1538316455,"version":"0.10.3"},"r-float":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/wrathematics/float","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/float/float_0.3-3.tar.gz","https://cran.r-project.org/src/contrib/float_0.3-3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"R comes with a suite of utilities for linear algebra with \"numeric\" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task.  This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their \"numeric\"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off.  The internal representation is an S4 class, which allows us to keep the syntax identical to that of base R's. Interaction between floats and base types for binary operators is generally possible; in these cases, type promotion always defaults to the higher precision.  The package ships with copies of the single precision 'BLAS' and 'LAPACK', which are automatically built in the event they are not available on the system.","text_prefix":false,"timestamp":1632155164,"version":"0.3_3"},"r-flock":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=flock","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flock/flock_0.7.tar.gz","https://cran.r-project.org/src/contrib/flock_0.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Implements synchronization between R processes (spawned by using the \"parallel\" package for instance) using file locks. Supports both exclusive and shared locking.","text_prefix":false,"timestamp":1757762435,"version":"0.7"},"r-flow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/moodymudskipper/flow","home":"https://moodymudskipper.github.io/flow/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flow/flow_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/flow_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Visualize as flow diagrams the logic of functions, expressions or scripts in a static way or when running a call, visualize the dependencies between functions or between modules in a shiny app, and more.","text_prefix":false,"timestamp":1758616200,"version":"0.2.0"},"r-flowchart":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/bruigtp/flowchart","home":"https://bruigtp.github.io/flowchart/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flowchart/flowchart_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/flowchart_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Creates participant flow diagrams directly from a dataframe. Representing the flow of participants through each stage of a study, especially in clinical trials, is essential to assess the generalisability and validity of the results. This package provides a set of functions that can be combined with a pipe operator to create all kinds of flowcharts from a data frame in an easy way.","text_prefix":false,"timestamp":1782290318,"version":"1.0.1"},"r-flowml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Boehringer-Ingelheim/flowml","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flowml/flowml_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/flowml_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Provides functionality to perform machine-learning-based modeling in a computation pipeline. Its functions contain the basic steps of machine-learning-based knowledge discovery workflows, including model training and optimization, model evaluation, and model testing. To perform these tasks, the package builds heavily on existing machine-learning packages, such as 'caret' <https://github.com/topepo/caret/> and associated packages. The package can train multiple models, optimize model hyperparameters by performing a grid search or a random search, and evaluates model performance by different metrics. Models can be validated either on a test data set, or in case of a small sample size by k-fold cross validation or repeated bootstrapping. It also allows for 0-Hypotheses generation by performing permutation experiments. Additionally, it offers methods of model interpretation and item categorization to identify the most informative features from a high dimensional data space. The functions of this package can easily be integrated into computation pipelines (e.g. 'nextflow' <https://www.nextflow.io/>) and hereby improve scalability, standardization, and re-producibility in the context of machine-learning.","text_prefix":false,"timestamp":1758627130,"version":"0.1.3"},"r-flownet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://sebkrantz.github.io/flownet/, https://github.com/SebKrantz/flownet","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flownet/flownet_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/flownet_0.3.0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"High-performance tools for transport modeling - network processing, route enumeration, and traffic assignment in R. The package implements the Path-Sized Logit model for traffic assignment - Ben-Akiva and Bierlaire (1999) <doi:10.1007/978-1-4615-5203-1_2> - an efficient route enumeration algorithm, and provides powerful utility functions for (multimodal) network generation, consolidation/contraction, and/or simplification. The user is expected to provide a transport network (either a graph or collection of linestrings) and an origin-destination (OD) matrix of trade/traffic flows. Maintained by transport consultants at CPCS (cpcs.ca).","text_prefix":false,"timestamp":1780306073,"version":"0.3.0"},"r-flowr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/flow-r/flowr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flowr/flowr_0.9.11.tar.gz","https://cran.r-project.org/src/contrib/flowr_0.9.11.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This framework allows you to design and implement complex pipelines, and deploy them on your institution's computing cluster. This has been built keeping in mind the needs of bioinformatics workflows. However, it is easily extendable to any field where a series of steps (shell commands) are to be executed in a (work)flow.","text_prefix":false,"timestamp":0,"version":"0.9.11"},"r-flux":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=flux","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/flux/flux_0.3-0.1.tar.gz","https://cran.r-project.org/src/contrib/flux_0.3-0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions for the calculation of greenhouse gas flux rates  from closed chamber concentration measurements. The package follows  a modular concept: Fluxes can be calculated in just two simple steps  or in several steps if more control in details is wanted. Additionally  plot and preparation functions as well as functions for modelling gpp and reco are provided.","text_prefix":false,"timestamp":0,"version":"0.3_0.1"},"r-fma":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pkg.robjhyndman.com/fma/, https://github.com/robjhyndman/fma","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fma/fma_2.5.tar.gz","https://cran.r-project.org/src/contrib/fma_2.5.tar.gz"],"subdirs":["noarch"],"summary":"All data sets from \"Forecasting: methods and applications\" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.","text_prefix":false,"timestamp":1563558194,"version":"2.5"},"r-fme":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://fme.r-forge.r-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FME/FME_1.3.6.4.tar.gz","https://cran.r-project.org/src/contrib/FME_1.3.6.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides functions to help in fitting models to data, to perform Monte Carlo, sensitivity and identifiability analysis. It is intended to work with models be written as a set of differential equations that are solved either by an integration routine from package 'deSolve', or a steady-state solver from package 'rootSolve'. However, the methods can also be used with other types of functions.","text_prefix":false,"timestamp":1622806750,"version":"1.3.6.4"},"r-fmesher":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/inlabru-org/fmesher","home":"https://inlabru-org.github.io/fmesher/","license":"MPL-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fmesher/fmesher_0.8.0.tar.gz","https://cran.r-project.org/src/contrib/fmesher_0.8.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Generate planar and spherical triangle meshes, compute finite element calculations for 1- and 2-dimensional flat and curved manifolds with associated basis function spaces, methods for lines and polygons, and transparent handling of coordinate reference systems and coordinate transformation, including 'sf' and 'sp' geometries. The core 'fmesher' library code was originally part of the 'INLA' package, and implements parts of \"Triangulations and Applications\" by Hjelle and Daehlen (2006) <doi:10.1007/3-540-33261-8>.","text_prefix":false,"timestamp":1782261277,"version":"0.8.0"},"r-fmsb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://minato.sip21c.org/msb/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/fmsb/fmsb_0.7.8.tar.gz","https://cloud.r-project.org/src/contrib/fmsb_0.7.8.tar.gz"],"subdirs":["noarch"],"summary":"Several utility functions for the book entitled  \"Practices of Medical and Health Data Analysis using R\" (Pearson Education Japan, 2007) with Japanese demographic data and some demographic analysis related functions.","text_prefix":false,"timestamp":1639396011,"version":"0.7.8"},"r-fmstable":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=FMStable","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FMStable/FMStable_0.1-4.tar.gz","https://cran.r-project.org/src/contrib/FMStable_0.1-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"This package implements some basic procedures for dealing with log maximally skew stable distributions, which are also called finite moment log stable distributions.","text_prefix":false,"timestamp":1621753511,"version":"0.1_4"},"r-fmultivar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.rmetrics.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fMultivar/fMultivar_4031.84.tar.gz","https://cran.r-project.org/src/contrib/fMultivar_4031.84.tar.gz"],"subdirs":["noarch"],"summary":"Provides a  collection of functions to manage, to investigate and to analyze bivariate and multivariate data sets of financial returns.","text_prefix":false,"timestamp":1622111589,"version":"4031.84"},"r-fnn":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=FNN","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FNN/FNN_1.1.4.1.tar.gz","https://cran.r-project.org/src/contrib/FNN_1.1.4.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications including KNN classification, regression and information measures are implemented.","text_prefix":false,"timestamp":1621628771,"version":"1.1.4.1"},"r-foci":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=FOCI","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FOCI/FOCI_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/FOCI_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Feature Ordering by Conditional Independence (FOCI) is a variable selection algorithm based on the measure of conditional dependence. For more information, see the paper: Azadkia and Chatterjee (2019),\"A simple measure of conditional dependence\" <arXiv:1910.12327>.","text_prefix":false,"timestamp":1758683965,"version":"0.1.3"},"r-fontawesome":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/fontawesome","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fontawesome/fontawesome_0.5.3.tar.gz","https://cran.r-project.org/src/contrib/fontawesome_0.5.3.tar.gz"],"subdirs":["noarch"],"summary":"Easily and flexibly insert 'Font Awesome' icons into 'R Markdown' documents and 'Shiny' apps. These icons can be inserted into HTML content through inline 'SVG' tags or 'i' tags. There is also a utility function for exporting 'Font Awesome' icons as 'PNG' images for those situations where raster graphics are needed.","text_prefix":false,"timestamp":1632506694,"version":"0.5.3"},"r-fontbitstreamvera":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fontBitstreamVera","license":"Bitstream Vera Fonts","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fontBitstreamVera/fontBitstreamVera_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/fontBitstreamVera_0.1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides fonts licensed under the 'Bitstream Vera Fonts' license for the 'fontquiver' package.","text_prefix":false,"timestamp":1538341074,"version":"0.1.1"},"r-fontliberation":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fontLiberation","license":"OFL-1.1","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fontLiberation/fontLiberation_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/fontLiberation_0.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A placeholder for the Liberation fontset intended for the `fontquiver` package. This fontset covers the 12 combinations of families (sans, serif, mono) and faces (plain, bold, italic, bold italic) supported in R graphics devices.","text_prefix":false,"timestamp":1538340784,"version":"0.1.0"},"r-fontquiver":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fontquiver","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fontquiver/fontquiver_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/fontquiver_0.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a set of fonts with permissive licences. This is useful when you want to avoid system fonts to make sure your outputs are reproducible.","text_prefix":false,"timestamp":1538343039,"version":"0.2.1"},"r-forcats":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://forcats.tidyverse.org, https://github.com/tidyverse/forcats","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/forcats/forcats_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/forcats_1.0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Helpers for reordering factor levels (including moving specified levels to front, ordering by first appearance, reversing, and randomly shuffling), and tools for modifying factor levels (including collapsing rare levels into other, 'anonymising', and manually 'recoding').","text_prefix":false,"timestamp":0,"version":"1.0.1"},"r-foreach":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RevolutionAnalytics/foreach","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/foreach/foreach_1.5.2.tar.gz","https://cran.r-project.org/src/contrib/foreach_1.5.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Support for the foreach looping construct.  Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter.  This package in particular is intended to be used for its return value, rather than for its side effects.  In that sense, it is similar to the standard lapply function, but doesn't require the evaluation of a function.  Using foreach without side effects also facilitates executing the loop in parallel.","text_prefix":false,"timestamp":0,"version":"1.5.2"},"r-forecast":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://pkg.robjhyndman.com/forecast, https://github.com/robjhyndman/forecast","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/forecast/forecast_9.0.2.tar.gz","https://cran.r-project.org/src/contrib/forecast_9.0.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.","text_prefix":false,"timestamp":1547959969,"version":"9.0.2"},"r-forecasthybrid":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://gitlab.com/dashaub/forecastHybrid, https://github.com/ellisp/forecastHybrid","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/forecastHybrid/forecastHybrid_5.1.21.tar.gz","https://cran.r-project.org/src/contrib/forecastHybrid_5.1.21.tar.gz"],"subdirs":["noarch"],"summary":"Convenient functions for ensemble forecasts in R combining approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy.","text_prefix":false,"timestamp":1768495843,"version":"5.1.21"},"r-foreign":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=foreign","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/foreign/foreign_0.8-91.tar.gz","https://cran.r-project.org/src/contrib/foreign_0.8-91.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.","text_prefix":false,"timestamp":1590284820,"version":"0.8_91"},"r-forestfloor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://forestFloor.dk","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/forestFloor/forestFloor_1.11.1.tar.gz","https://cran.r-project.org/src/contrib/forestFloor_1.11.1.tar.gz"],"subdirs":["linux-64","win-64"],"summary":"Form visualizations of high dimensional mapping structures of random forests and feature contributions.","text_prefix":false,"timestamp":1773980029,"version":"1.11.1"},"r-forestly":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=forestly","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/forestly/forestly_0.1.6.tar.gz","https://cloud.r-project.org/src/contrib/forestly_0.1.6.tar.gz"],"subdirs":["noarch"],"summary":"Interactive forest plot for clinical trial safety analysis using 'metalite', 'reactable', 'plotly', and Analysis Data Model (ADaM) datasets. Includes functionality for adverse event filtering, incidence-based group filtering, hover-over reveals, and search and sort operations. The workflow allows for metadata construction, data preparation, output formatting, and interactive plot generation.","text_prefix":false,"timestamp":1787906291,"version":"0.1.6"},"r-forestmodel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=forestmodel","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/forestmodel/forestmodel_0.6.2.tar.gz","https://cran.r-project.org/src/contrib/forestmodel_0.6.2.tar.gz"],"subdirs":["noarch"],"summary":"Produces forest plots using 'ggplot2' from models produced by functions such as stats::lm(), stats::glm() and survival::coxph().","text_prefix":false,"timestamp":1757897566,"version":"0.6.2"},"r-forestplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://gforge.se/packages/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/forestplot/forestplot_3.2.0.tar.gz","https://cran.r-project.org/src/contrib/forestplot_3.2.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"A forest plot that allows for multiple confidence intervals per row, custom fonts for each text element, custom confidence intervals, text mixed with expressions, and more. The aim is to extend the use of forest plots beyond meta-analyses. This is a more general version of the original 'rmeta' package's forestplot() function and relies heavily on the 'grid' package.","text_prefix":false,"timestamp":0,"version":"3.2.0"},"r-forge":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=forge","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/forge/forge_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/forge_0.2.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking.","text_prefix":false,"timestamp":1540828405,"version":"0.2.0"},"r-formatr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/yihui/formatR","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/formatR/formatR_1.14.tar.gz","https://cran.r-project.org/src/contrib/formatR_1.14.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a function tidy_source() to format R source code. Spaces and indent will be added to the code automatically, and comments will be preserved under certain conditions, so that R code will be more human-readable and tidy. There is also a Shiny app as a user interface in this package (see tidy_app()).","text_prefix":false,"timestamp":0,"version":"1.14"},"r-formattable":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://renkun.me/formattable, https://github.com/renkun-ken/formattable","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/formattable/formattable_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/formattable_0.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides functions to create formattable vectors and data frames. 'Formattable' vectors are printed with text formatting, and formattable data frames are printed with multiple types of formatting in HTML to improve the readability of data presented in tabular form rendered in web pages.","text_prefix":false,"timestamp":0,"version":"0.2.1"},"r-formatters":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/insightsengineering/formatters","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/formatters/formatters_0.5.13.tar.gz","https://cloud.r-project.org/src/contrib/formatters_0.5.13.tar.gz"],"subdirs":["noarch"],"summary":"We provide a framework for rendering complex tables to ASCII, and a set of formatters for transforming values or sets of values into ASCII-ready display strings.","text_prefix":false,"timestamp":1726318999,"version":"0.5.13"},"r-formula":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://zeileis.codeberg.page/Formula/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/Formula/Formula_1.2-6.tar.gz","https://cloud.r-project.org/src/contrib/Formula_1.2-6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Infrastructure for extended formulas with multiple parts on the right-hand side and/or multiple responses on the left-hand side (see <DOI:10.18637/jss.v034.i01>).","text_prefix":false,"timestamp":1537580430,"version":"1.2_6"},"r-formula.tools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/decisionpatterns/formula.tools","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/formula.tools/formula.tools_1.7.1.tar.gz","https://cran.r-project.org/src/contrib/formula.tools_1.7.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"These utilities facilitate the programmatic manipulations of formulas, expressions, calls, assignments and other R language objects.  These objects all share the same structure: a left-hand side, operator and  right-hand side. This packages provides methods for accessing and  modifying this structures as well as extracting and replacing names and  symbols from these objects.","text_prefix":false,"timestamp":1535648853,"version":"1.7.1"},"r-fossil":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://matthewvavrek.com/programs-and-code/fossil/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fossil/fossil_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/fossil_0.4.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A set of analytical tools useful in analysing ecological and geographical data sets, both ancient and modern. The package includes functions for estimating species richness (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta diversity, species area curves and geographic distances and areas.","text_prefix":false,"timestamp":1758595849,"version":"0.4.0"},"r-fourpno":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tmsalab/fourPNO","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fourPNO/fourPNO_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/fourPNO_1.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.","text_prefix":false,"timestamp":1758726791,"version":"1.1.0"},"r-fpc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.unibo.it/sitoweb/christian.hennig/en/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/fpc/fpc_2.2-15.tar.gz","https://cloud.r-project.org/src/contrib/fpc_2.2-15.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by  merging Gaussian mixture components. Symmetric  and asymmetric discriminant projections for visualisation of the  separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index.  Standardisation of cluster validation statistics by random clusterings and  comparison between many clustering methods and numbers of clusters based on this.   Cluster-wise cluster stability assessment. Methods for estimation of  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's  prediction strength, Fang and Wang's bootstrap stability.  Gaussian/multinomial mixture fitting for mixed  continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many  clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc.","text_prefix":false,"timestamp":0,"version":"2.2_15"},"r-fportfolio":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://r-forge.r-project.org/projects/rmetrics/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fPortfolio/fPortfolio_4023.84.tar.gz","https://cran.r-project.org/src/contrib/fPortfolio_4023.84.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions to optimize portfolios and to analyze them from different points of view.","text_prefix":false,"timestamp":1758747299,"version":"4023.84"},"r-fpp2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pkg.robjhyndman.com/fpp2-package, https://github.com/robjhyndman/fpp2-package, https://OTexts.org/fpp2/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fpp2/fpp2_2.5.1.tar.gz","https://cran.r-project.org/src/contrib/fpp2_2.5.1.tar.gz"],"subdirs":["noarch"],"summary":"All data sets required for the examples and exercises  in the book \"Forecasting: principles and practice\"  by Rob J Hyndman and George Athanasopoulos <https://OTexts.org/fpp2/>.  All packages required to run the examples are also loaded.","text_prefix":false,"timestamp":1665792532,"version":"2.5.1"},"r-fpp3":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/robjhyndman/fpp3package","doc_url":"https://pkg.robjhyndman.com/fpp3package/","home":"https://OTexts.com/fpp3/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fpp3/fpp3_1.0.3.tar.gz","https://cran.r-project.org/src/contrib/fpp3_1.0.3.tar.gz"],"subdirs":["noarch"],"summary":"All data sets required for the examples and exercises in the book \"Forecasting: principles and practice\" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded. Additional data sets not used in the book are also included.","text_prefix":false,"timestamp":1770974652,"version":"1.0.3"},"r-frab":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RobinHankin/frab","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/frab/frab_0.0-6.tar.gz","https://cran.r-project.org/src/contrib/frab_0.0-6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Methods to \"add\" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3).  Uses 'disordR' discipline (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>).  Extraction and replacement methods are provided.  The underlying mathematical structure is the Free Abelian group, hence the name.  To cite in publications please use Hankin (2023) <doi:10.48550/arXiv.2307.13184>.","text_prefix":false,"timestamp":1758094168,"version":"0.0_6"},"r-fracdiff":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/mmaechler/fracdiff","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fracdiff/fracdiff_1.5-4.tar.gz","https://cran.r-project.org/src/contrib/fracdiff_1.5-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); including inference and basic methods.  Some alternative algorithms to estimate \"H\".","text_prefix":false,"timestamp":1577147484,"version":"1.5_4"},"r-fractional":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fractional","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fractional/fractional_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/fractional_0.1.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The main function of this package allows numerical vector objects to be displayed with their values in vulgar fractional form.  This is convenient if patterns can then be more easily detected.  In some cases replacing the components of a numeric vector by a rational approximation can also be expected to remove some component of round-off error.  The main functions form a re-implementation of the functions 'fractions' and 'rational' of the MASS package, but using a radically improved programming strategy.","text_prefix":false,"timestamp":1773517127,"version":"0.1.3"},"r-framecleaner":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://harrison4192.github.io/framecleaner/, https://github.com/Harrison4192/framecleaner","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/framecleaner/framecleaner_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/framecleaner_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides a friendly interface for modifying data frames with a sequence of piped commands built upon the 'tidyverse' Wickham et al., (2019) <doi:10.21105/joss.01686> . The majority of commands wrap 'dplyr' mutate statements in a convenient way to concisely solve common issues that arise when tidying small to medium data sets. Includes smart defaults and allows flexible selection of columns via 'tidyselect'.","text_prefix":false,"timestamp":1758419761,"version":"0.2.1"},"r-fredr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sboysel/fredr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fredr/fredr_2.1.0.tar.gz","https://cran.r-project.org/src/contrib/fredr_2.1.0.tar.gz"],"subdirs":["noarch"],"summary":"An R client for the 'Federal Reserve Economic Data' ('FRED') API <https://research.stlouisfed.org/docs/api/>.  Functions to retrieve economic time series and other data from 'FRED'.","text_prefix":false,"timestamp":1758715081,"version":"2.1.0"},"r-freesurferformats":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dfsp-spirit/freesurferformats","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/freesurferformats/freesurferformats_1.0.2.tar.gz","https://cloud.r-project.org/src/contrib/freesurferformats_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides functions to read and write neuroimaging data in various file formats, with a focus on 'FreeSurfer' <http://freesurfer.net/> formats. This includes, but is not limited to, the following file formats: 1) MGH/MGZ format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) 'FreeSurfer' morphometry data files in binary 'curv' format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas. 4) Surface file format. Contains a brain surface mesh, given by a list of vertices and a list of faces.","text_prefix":false,"timestamp":1789500103,"version":"1.0.2"},"r-freetypeharfbuzz":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=freetypeharfbuzz","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/freetypeharfbuzz/freetypeharfbuzz_0.2.6.tar.gz","https://cran.r-project.org/src/contrib/freetypeharfbuzz_0.2.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Unlike other tools that dynamically link to the 'Cairo' stack, 'freetypeharfbuzz' is statically linked to specific versions of the 'FreeType' and 'harfbuzz' libraries (2.9 and 1.7.6 respectively). This ensures deterministic computation of text box extents for situations where reproducible results are crucial (for instance unit tests of graphics).","text_prefix":false,"timestamp":1588617120,"version":"0.2.6"},"r-freqprof":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/AIBRT/FreqProf","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FreqProf/FreqProf_0.0.1.tar.gz","https://cran.r-project.org/src/contrib/FreqProf_0.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Tools for generating an informative type of line graph, the frequency profile, which allows single behaviors, multiple behaviors, or the specific behavioral patterns of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.","text_prefix":false,"timestamp":1758739519,"version":"0.0.1"},"r-fresh":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dreamRs/fresh","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fresh/fresh_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/fresh_0.2.2.tar.gz"],"subdirs":["noarch"],"summary":"Customize 'Bootstrap' and 'Bootswatch' themes, like colors, fonts, grid layout, to use in 'Shiny' applications, 'rmarkdown' documents and 'flexdashboard'.","text_prefix":false,"timestamp":1757963505,"version":"0.2.2"},"r-frictionless":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/frictionlessdata/frictionless-r, https://docs.ropensci.org/frictionless/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/frictionless/frictionless_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/frictionless_1.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Read and write Frictionless Data Packages. A 'Data Package' (<https://specs.frictionlessdata.io/data-package/>) is a simple container format and standard to describe and package a collection of (tabular) data. It is typically used to publish FAIR (<https://www.go-fair.org/fair-principles/>) and open datasets.","text_prefix":false,"timestamp":1665792348,"version":"1.3.0"},"r-fritools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://gitlab.com/fvafrcu/fritools","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fritools/fritools_4.6.0.tar.gz","https://cran.r-project.org/src/contrib/fritools_4.6.0.tar.gz"],"subdirs":["noarch"],"summary":"Miscellaneous utilities, tools and helper functions for finding and searching files on disk, searching for and removing R objects from the workspace. Does not import or depend on any third party package, but on core R only (i.e. it may depend on packages with priority 'base').","text_prefix":false,"timestamp":1771409531,"version":"4.6.0"},"r-fs":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-lib/fs","home":"http://fs.r-lib.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fs/fs_2.1.0.tar.gz","https://cran.r-project.org/src/contrib/fs_2.1.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A cross-platform interface to file system operations, built on top of the 'libuv' C library.","text_prefix":false,"timestamp":1553188123,"version":"2.1.0"},"r-fsa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/droglenc/FSA","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FSA/FSA_0.10.1.tar.gz","https://cran.r-project.org/src/contrib/FSA_0.10.1.tar.gz"],"subdirs":["noarch"],"summary":"A variety of simple fish stock assessment methods. Detailed vignettes are available on the fishR website <http://derekogle.com/fishR/>.","text_prefix":false,"timestamp":1632829842,"version":"0.10.1"},"r-fscache":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://gitlab.com/cnrgh/databases/r-fscache","license":"AGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fscache/fscache_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/fscache_1.0.5.tar.gz"],"subdirs":["noarch"],"summary":"Manages a file system cache. Regular files can be moved or copied to the cache folder. Sub-folders can be created in order to organize the files. Files can be located inside the cache using a glob function. Text contents can be easily stored in and retrieved from the cache using dedicated functions. It can be used for an application or a package, as a global cache, or as a per-user cache, in which case the standard OS user cache folder will be used (e.g.: on Linux $HOME/.cache/R/my_app_or_pkg_cache_folder).","text_prefix":false,"timestamp":1772606290,"version":"1.0.5"},"r-fselector":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=FSelector","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FSelector/FSelector_0.34.tar.gz","https://cran.r-project.org/src/contrib/FSelector_0.34.tar.gz"],"subdirs":["noarch"],"summary":"Functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible.","text_prefix":false,"timestamp":1610052473,"version":"0.34"},"r-fst":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://fstpackage.github.io","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fst/fst_0.9.8.tar.gz","https://cran.r-project.org/src/contrib/fst_0.9.8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-32","win-64"],"summary":"Multithreaded serialization of compressed data frames using the 'fst' format. The 'fst' format allows for random access of stored data and compression with the LZ4 and ZSTD compressors created by Yann Collet. The ZSTD compression library is owned by Facebook Inc.","text_prefix":false,"timestamp":1602333723,"version":"0.9.8"},"r-fstcore":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.fstpackage.org/fstcore/","license":"MPL-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fstcore/fstcore_0.10.0.tar.gz","https://cran.r-project.org/src/contrib/fstcore_0.10.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The 'fstlib' library provides multithreaded serialization of compressed data frames using the 'fst' format. The 'fst' format allows for random access of stored data and compression with the 'LZ4' and 'ZSTD' compressors.","text_prefix":false,"timestamp":1645710286,"version":"0.10.0"},"r-ftrading":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.rmetrics.org","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fTrading/fTrading_3042.79.tar.gz","https://cran.r-project.org/src/contrib/fTrading_3042.79.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions for trading and rebalancing financial instruments. It implements various technical indicators to analyse time series such as moving averages or stochastic oscillators.","text_prefix":false,"timestamp":1758289891,"version":"3042.79"},"r-fts":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fts","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fts/fts_0.9.9.2.tar.gz","https://cran.r-project.org/src/contrib/fts_0.9.9.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"fast operations for time series objects","text_prefix":false,"timestamp":1533146138,"version":"0.9.9.2"},"r-functional":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/klutometis/R-functional","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/functional/functional_0.7.tar.gz","https://cran.r-project.org/src/contrib/functional_0.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a small collection of higher-order functions for functional-style programming in R, including 'Curry' (partial application), 'CurryL' (lazy partial application), 'Compose' (function composition, including a multi-argument variant), 'Identity', 'Negate' and 'Swap'.","text_prefix":false,"timestamp":0,"version":"0.7"},"r-functiondepends":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=functiondepends","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/functiondepends/functiondepends_0.2.3.tar.gz","https://cran.r-project.org/src/contrib/functiondepends_0.2.3.tar.gz"],"subdirs":["noarch"],"summary":"Find functions in an unstructured directory and explore their dependencies. Sourcing of R source files is performed without side-effects: from R scripts that have executable code and function definitions only functions are sourced.","text_prefix":false,"timestamp":1758402209,"version":"0.2.3"},"r-funr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sahilseth/funr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/funr/funr_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/funr_0.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A small utility which wraps Rscript and provides access to all R functions from the shell.","text_prefix":false,"timestamp":0,"version":"0.3.2"},"r-funrar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://rekyt.github.io/funrar/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/funrar/funrar_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/funrar_1.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Computes functional rarity indices as proposed by Violle et al. (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. 'funrar' is presented in Greni\u00e9 et al. (2017) <doi:10.1111/ddi.12629>.","text_prefix":false,"timestamp":1583422378,"version":"1.5.0"},"r-funspotr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://brshallo.github.io/funspotr/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/funspotr/funspotr_0.0.5.tar.gz","https://cran.r-project.org/src/contrib/funspotr_0.0.5.tar.gz"],"subdirs":["noarch"],"summary":"Helpers for parsing out the R functions and packages used in R scripts and notebooks.","text_prefix":false,"timestamp":1769703629,"version":"0.0.5"},"r-furrr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/DavisVaughan/furrr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/furrr/furrr_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/furrr_0.4.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implementations of the family of map() functions from 'purrr' that can be resolved using any 'future'-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.","text_prefix":false,"timestamp":1538455788,"version":"0.4.0"},"r-fusen":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/Thinkr-open/fusen","home":"https://thinkr-open.github.io/fusen/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fusen/fusen_0.7.2.tar.gz","https://cran.r-project.org/src/contrib/fusen_0.7.2.tar.gz"],"subdirs":["noarch"],"summary":"Use Rmarkdown First method to build your package. Start your package with documentation, functions, examples and tests in the same unique file. Everything can be set from the Rmarkdown template file provided in your project, then inflated as a package. Inflating the template copies the relevant chunks and sections in the appropriate files required for package development.","text_prefix":false,"timestamp":1758636032,"version":"0.7.2"},"r-futile.logger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=futile.logger","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/futile.logger/futile.logger_1.4.9.tar.gz","https://cran.r-project.org/src/contrib/futile.logger_1.4.9.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a simple yet powerful logging utility. Based loosely on log4j, futile.logger takes advantage of R idioms to make logging a convenient and easy to use replacement for cat and print statements.","text_prefix":false,"timestamp":1537203656,"version":"1.4.9"},"r-futile.options":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=futile.options","license":"LGPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/futile.options/futile.options_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/futile.options_1.0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A scoped options management framework. Used in other packages.","text_prefix":false,"timestamp":1537098720,"version":"1.0.1"},"r-future":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/HenrikBengtsson/future","home":"https://future.futureverse.org/","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/future/future_1.80.0.tar.gz","https://cloud.r-project.org/src/contrib/future_1.80.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures.  The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`. This package implements sequential, multicore, multisession, and cluster futures.  With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. 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These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.","text_prefix":false,"timestamp":0,"version":"1.21.0"},"r-future.batchtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/future.batchtools","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/future.batchtools/future.batchtools_0.30.0.tar.gz","https://cloud.r-project.org/src/contrib/future.batchtools_0.30.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implementation of the Future API on top of the 'batchtools' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.","text_prefix":false,"timestamp":1539784161,"version":"0.30.0"},"r-future.callr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/HenrikBengtsson/future.callr","home":"https://future.callr.futureverse.org","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/future.callr/future.callr_1.0.1.tar.gz","https://cloud.r-project.org/src/contrib/future.callr_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Implementation of the Future API on top of the 'callr' package.  This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine.  Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession') and socket connections, the 'callr' backend provided here can run more than 125 parallel R processes.","text_prefix":false,"timestamp":1791742978,"version":"1.0.1"},"r-fuzzyjoin":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=fuzzyjoin","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/fuzzyjoin/fuzzyjoin_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/fuzzyjoin_0.1.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Join tables together based not on whether columns match exactly, but whether they are similar by some comparison. Implementations include string distance and regular expression matching.","text_prefix":false,"timestamp":0,"version":"0.1.8"},"r-fwdselect":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://cran.r-project.org/package=FWDselect","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/FWDselect/FWDselect_2.1.1.tar.gz","https://cran.r-project.org/src/contrib/FWDselect_2.1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric).","text_prefix":false,"timestamp":1539723564,"version":"2.1.1"},"r-ga":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/luca-scr/GA/","home":"https://luca-scr.github.io/GA/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GA/GA_3.2.5.tar.gz","https://cran.r-project.org/src/contrib/GA_3.2.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.","text_prefix":false,"timestamp":1665788006,"version":"3.2.5"},"r-galaxyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/JulFrey/GalaxyR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GalaxyR/GalaxyR_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/GalaxyR_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"On 'Galaxy' platforms like 'Galaxy Europe' <https://usegalaxy.eu>, many tools and workflows can run directly on a high-performance computer. 'GalaxyR' connects R with 'Galaxy' platforms API <https://usegalaxy.eu/api/docs> and allows credential management, uploading data, invoking workflows or tools, checking their status, and downloading results.","text_prefix":false,"timestamp":1783412004,"version":"0.1.2"},"r-gam":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gam","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gam/gam_1.22-7.tar.gz","https://cran.r-project.org/src/contrib/gam_1.22-7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions for fitting and working with generalized additive models, as described in chapter 7 of \"Statistical Models in S\" (Chambers and Hastie (eds), 1991), and \"Generalized Additive Models\" (Hastie and Tibshirani, 1990).","text_prefix":false,"timestamp":1543167523,"version":"1.22_7"},"r-gamboostlss":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"For source code, development versions and issue tracker see https://github.com/boost-R/gamboostLSS","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gamboostLSS/gamboostLSS_2.2-0.tar.gz","https://cran.r-project.org/src/contrib/gamboostLSS_2.2-0.tar.gz"],"subdirs":["noarch"],"summary":"Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.","text_prefix":false,"timestamp":1622117638,"version":"2.2_0"},"r-gameofthrones":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/aljrico/gameofthrones","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gameofthrones/gameofthrones_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/gameofthrones_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Implementation of the characteristic palettes from the TV show 'Game of Thrones'.","text_prefix":false,"timestamp":1563555593,"version":"1.0.2"},"r-gamlss":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.gamlss.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gamlss/gamlss_5.5-0.tar.gz","https://cran.r-project.org/src/contrib/gamlss_5.5-0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.","text_prefix":false,"timestamp":1594651826,"version":"5.5_0"},"r-gamlss.data":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.gamlss.org/","license":"GPL-2 | GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gamlss.data/gamlss.data_6.0-7.tar.gz","https://cran.r-project.org/src/contrib/gamlss.data_6.0-7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Data used as examples to demonstrate GAMLSS models.","text_prefix":false,"timestamp":0,"version":"6.0_7"},"r-gamlss.dist":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.gamlss.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/gamlss.dist/gamlss.dist_6.1-11.tar.gz","https://cloud.r-project.org/src/contrib/gamlss.dist_6.1-11.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A set of distributions  which can be used  for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed  distributions.  Extra distributions can be created, by transforming, any continuous distribution defined on the real line,  to  a distribution defined on ranges 0 to infinity  or  0 to 1,  by using a ''log'' or a ''logit' transformation respectively.","text_prefix":false,"timestamp":1594651820,"version":"6.1_11"},"r-gamm4":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gamm4","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gamm4/gamm4_0.3-0.tar.gz","https://cran.r-project.org/src/contrib/gamm4_0.3-0.tar.gz"],"subdirs":["noarch"],"summary":"Estimate generalized additive mixed models via a version of function gamm() from 'mgcv', using 'lme4' for estimation.","text_prefix":false,"timestamp":1563555555,"version":"0.3_0"},"r-gamsel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Using overlap grouped-lasso penalties, gamsel selects whether a term in a gam is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.","doc_url":"https://arxiv.org/abs/1506.03850","home":"https://arxiv.org/abs/1506.03850","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://cran.r-project.org/src/contrib/gamsel_1.8-5.tar.gz","subdirs":["linux-64","osx-64"],"summary":"Fit Regularization Path for Generalized Additive Models","text_prefix":false,"timestamp":1777300254,"version":"1.8.5"},"r-gap":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jinghuazhao/R","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gap/gap_1.15.2.tar.gz","https://cran.r-project.org/src/contrib/gap_1.15.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"It is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates.","text_prefix":false,"timestamp":1666369898,"version":"1.15.2"},"r-gap.datasets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://jinghuazhao.github.io/R/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gap.datasets/gap.datasets_0.0.6.tar.gz","https://cran.r-project.org/src/contrib/gap.datasets_0.0.6.tar.gz"],"subdirs":["noarch"],"summary":"Datasets associated with the 'gap' package. Currently, it includes an example data for regional association plot (CDKN), an example data for a genomewide association meta-analysis (OPG), data in studies of Parkinson's diease (PD), ALHD2 markers and alcoholism (aldh2), APOE/APOC1 markers and Schizophrenia (apoeapoc), cystic fibrosis (cf), a Olink/INF panel (inf1), Manhattan plots with (hr1420, mhtdata) and without (w4) gene annotations.","text_prefix":false,"timestamp":1757849771,"version":"0.0.6"},"r-gapminder":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jennybc/gapminder, http://www.gapminder.org/data/, https://doi.org/10.5281/zenodo.594018","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gapminder/gapminder_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/gapminder_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"An excerpt of the data available at Gapminder.org. For each of 142 countries, the package provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007.","text_prefix":false,"timestamp":1665581597,"version":"1.0.1"},"r-gargle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/r-lib/gargle","home":"https://gargle.r-lib.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gargle/gargle_1.6.1.tar.gz","https://cran.r-project.org/src/contrib/gargle_1.6.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides utilities for working with Google APIs <https://developers.google.com/apis-explorer>.  This includes functions and classes for handling common credential types and for preparing, executing, and processing HTTP requests.","text_prefix":false,"timestamp":1588749740,"version":"1.6.1"},"r-gaston":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gaston","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gaston/gaston_1.6.tar.gz","https://cran.r-project.org/src/contrib/gaston_1.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al <doi:10.1159/000488519>.","text_prefix":false,"timestamp":1595281819,"version":"1.6"},"r-gaupro":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/CollinErickson/GauPro","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GauPro/GauPro_0.2.17.tar.gz","https://cran.r-project.org/src/contrib/GauPro_0.2.17.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.","text_prefix":false,"timestamp":1781612147,"version":"0.2.17"},"r-gbj":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=GBJ","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GBJ/GBJ_0.5.4.tar.gz","https://cran.r-project.org/src/contrib/GBJ_0.5.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests. The GBJ has been shown to outperform other tests in genetic association studies when signals are correlated and moderately sparse. Please see the vignette for a quickstart guide or the paper at <doi:10.1080/01621459.2019.1660170> for full details.","text_prefix":false,"timestamp":1758232472,"version":"0.5.4"},"r-gbm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/gbm-developers/gbm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gbm/gbm_2.3.1.tar.gz","https://cran.r-project.org/src/contrib/gbm_2.3.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"An implementation of extensions to Freund and Schapire's AdaBoost  algorithm and Friedman's gradient boosting machine. Includes regression  methods for least squares, absolute loss, t-distribution loss, quantile  regression, logistic, multinomial logistic, Poisson, Cox proportional hazards  partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and  Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.","text_prefix":false,"timestamp":1621980966,"version":"2.3.1"},"r-gbrd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gbRd","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gbRd/gbRd_0.4.12.tar.gz","https://cran.r-project.org/src/contrib/gbRd_0.4.12.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides utilities for processing Rd objects and files. Extract argument descriptions and other parts of the help pages of functions.","text_prefix":false,"timestamp":1540829000,"version":"0.4.12"},"r-gbutils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/GeoBosh/gbutils","home":"https://geobosh.github.io/gbutils/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gbutils/gbutils_0.5.1.tar.gz","https://cran.r-project.org/src/contrib/gbutils_0.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages.","text_prefix":false,"timestamp":1766291352,"version":"0.5.1"},"r-gclus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gclus","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gclus/gclus_1.3.3.tar.gz","https://cran.r-project.org/src/contrib/gclus_1.3.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Orders panels in scatterplot matrices and parallel coordinate displays by some merit index. Package contains various indices of merit, ordering functions, and enhanced versions of pairs and parcoord which color panels according to their merit level.","text_prefix":false,"timestamp":1539003841,"version":"1.3.3"},"r-gdalcubes":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/appelmar/gdalcubes_R","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/gdalcubes/gdalcubes_0.7.4.tar.gz","https://cloud.r-project.org/src/contrib/gdalcubes_0.7.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, and plotting.  The package implements lazy evaluation and multithreading. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) <doi:10.3390/data4030092> for further details.","text_prefix":false,"timestamp":1635438326,"version":"0.7.4"},"r-gdalraster":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/USDAForestService/gdalraster","doc_url":"https://usdaforestservice.github.io/gdalraster/","home":"https://github.com/USDAForestService/gdalraster","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/gdalraster/gdalraster_2.7.0.tar.gz","https://cloud.r-project.org/src/contrib/gdalraster_2.7.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64"],"summary":"API bindings to the Geospatial Data Abstraction Library ('GDAL', <https://gdal.org>). Implements the 'GDAL' Raster and Vector Data Models. Bindings are implemented with 'Rcpp' modules. Exposed C++ classes and stand-alone functions wrap much of the 'GDAL' API and provide additional functionality. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Class 'GDALRaster' encapsulates a 'GDALDataset' and its raster band objects. Class 'GDALVector' encapsulates an 'OGRLayer' and the 'GDALDataset' that contains it. Initial bindings are provided to the unified 'gdal' command line interface added in 'GDAL' 3.11. C++ stand-alone functions provide bindings to most 'GDAL' \"traditional\" raster and vector utilities, including 'OGR' facilities for vector geoprocessing, several algorithms, as well as the Geometry API ('GEOS' via 'GDAL' headers), the Spatial Reference Systems API, and methods for coordinate transformation. Bindings to the Virtual Systems Interface ('VSI') API implement standard file system operations abstracted for URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR', in-memory files, as well as regular local file systems. This provides a single interface for operating on file system objects that works the same for any storage backend. A custom raster calculator evaluates a user-defined R expression on a layer or stack of layers, with pixel x/y available as variables in the expression. Raster 'combine()' identifies and counts unique pixel combinations across multiple input layers, with optional raster output of the pixel-level combination IDs. Basic plotting capability is provided for raster and vector display. 'gdalraster' leans toward minimalism and the use of simple, lightweight objects for holding raw data. Currently, only minimal S3 class interfaces have been implemented for selected R objects that contain spatial data. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API.","text_prefix":false,"timestamp":1790180452,"version":"2.7.0"},"r-gdalutilities":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/JoshOBrien/gdalUtilities/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gdalUtilities/gdalUtilities_1.2.5.tar.gz","https://cran.r-project.org/src/contrib/gdalUtilities_1.2.5.tar.gz"],"subdirs":["noarch"],"summary":"R's 'sf' package ships with self-contained 'GDAL' executables, including a bare bones interface to several 'GDAL'-related utility programs collectively known as the 'GDAL utilities'. For each of those utilities, this package provides an R wrapper whose formal arguments closely mirror those of the 'GDAL' command line interface. The utilities operate on data stored in files and typically write their output to other files. Therefore, to process data stored in any of R's more common spatial formats (i.e. those supported by the 'sp', 'sf', and 'raster' packages), first write them to disk, then process them with the package's wrapper functions before reading the outputted results back into R. GDAL function arguments introduced in GDAL version 3.2.1 or earlier are supported.","text_prefix":false,"timestamp":1635272120,"version":"1.2.5"},"r-gdalutils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gdalUtils","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gdalUtils/gdalUtils_2.0.3.2.tar.gz","https://cran.r-project.org/src/contrib/gdalUtils_2.0.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Wrappers for the Geospatial Data Abstraction Library (GDAL) Utilities.","text_prefix":false,"timestamp":0,"version":"2.0.3.2"},"r-gdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gdata","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gdata/gdata_3.0.1.tar.gz","https://cran.r-project.org/src/contrib/gdata_3.0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Various R programming tools for data manipulation, including: - medical unit conversions ('ConvertMedUnits', 'MedUnits'), - combining objects ('bindData', 'cbindX', 'combine', 'interleave'), - character vector operations ('centerText', 'startsWith', 'trim'), - factor manipulation ('levels', 'reorder.factor', 'mapLevels'), - obtaining information about R objects ('object.size', 'elem', 'env', 'humanReadable', 'is.what', 'll', 'keep', 'ls.funs', 'Args','nPairs', 'nobs'), - manipulating MS-Excel formatted files ('read.xls', 'installXLSXsupport', 'sheetCount', 'xlsFormats'), - generating fixed-width format files ('write.fwf'), - extricating components of date & time objects ('getYear', 'getMonth', 'getDay', 'getHour', 'getMin', 'getSec'), - operations on columns of data frames  ('matchcols', 'rename.vars'), - matrix operations ('unmatrix', 'upperTriangle', 'lowerTriangle'), - operations on vectors ('case', 'unknownToNA', 'duplicated2', 'trimSum'), - operations on data frames ('frameApply', 'wideByFactor'), - value of last evaluated expression ('ans'), and - wrapper for 'sample' that ensures consistent behavior for both scalar and vector arguments ('resample').","text_prefix":false,"timestamp":1536514150,"version":"3.0.1"},"r-gdistance":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gdistance","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gdistance/gdistance_1.6.5.tar.gz","https://cran.r-project.org/src/contrib/gdistance_1.6.5.tar.gz"],"subdirs":["noarch"],"summary":"Calculate distances and routes on geographic grids.","text_prefix":false,"timestamp":1665525671,"version":"1.6.5"},"r-gdtools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/davidgohel/gdtools","home":"https://CRAN.R-project.org/package=gdtools","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gdtools/gdtools_0.5.1.tar.gz","https://cran.r-project.org/src/contrib/gdtools_0.5.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Useful tools for writing vector graphics devices.","text_prefix":false,"timestamp":1533284739,"version":"0.5.1"},"r-gee":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gee","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gee/gee_4.13-30.tar.gz","https://cran.r-project.org/src/contrib/gee_4.13-30.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Generalized Estimation Equation solver.","text_prefix":false,"timestamp":1665788016,"version":"4.13_30"},"r-geeasy":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=geeasy","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/geeasy/geeasy_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/geeasy_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Estimation of generalized linear models with correlated/clustered observations by use of generalized estimating equations (GEE). See e.g. Halekoh and H\u00f8jsgaard, (2005, <doi:10.18637/jss.v015.i02>), for details. Several types of clustering are supported, including exchangeable variance structures, AR1 structures, M-dependent, user-specified variance structures and more. The model fitting computations are performed using modified code from the 'geeM' package, while the interface and output objects have been written to resemble the 'geepack' package. The package also contains additional tools for working with and inspecting results from the 'geepack' package, e.g. a 'confint' method for 'geeglm' objects from 'geepack'.","text_prefix":false,"timestamp":1770885420,"version":"0.1.3"},"r-geem":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=geeM","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/geeM/geeM_0.10.1.tar.gz","https://cran.r-project.org/src/contrib/geeM_0.10.1.tar.gz"],"subdirs":["noarch"],"summary":"GEE estimation of the parameters in mean structures with possible correlation between the outcomes. User-specified mean link and variance functions are allowed, along with observation weighting. The 'M' in the name 'geeM' is meant to emphasize the use of the Matrix package, which allows for an implementation based fully in R.","text_prefix":false,"timestamp":1757877533,"version":"0.10.1"},"r-geepack":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=geepack","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/geepack/geepack_1.3.13.tar.gz","https://cran.r-project.org/src/contrib/geepack_1.3.13.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses.","text_prefix":false,"timestamp":1563498077,"version":"1.3.13"},"r-geigen":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=geigen","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/geigen/geigen_2.4.tar.gz","https://cran.r-project.org/src/contrib/geigen_2.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions to compute generalized eigenvalues and eigenvectors, the generalized Schur decomposition and the generalized Singular Value Decomposition of a matrix pair, using Lapack routines.","text_prefix":false,"timestamp":0,"version":"2.4"},"r-geiger":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.webpages.uidaho.edu/~lukeh/software.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/geiger/geiger_2.0.12.tar.gz","https://cloud.r-project.org/src/contrib/geiger_2.0.12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Methods for fitting macroevolutionary models to phylogenetic trees.","text_prefix":false,"timestamp":1788240170,"version":"2.0.12"},"r-genabel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GenABEL/GenABEL_1.8-0.tar.gz","https://cran.r-project.org/src/contrib/GenABEL_1.8-0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"a package for genome-wide association analysis between  quantitative or binary traits and single-nucleotide polymorphisms (SNPs).","text_prefix":false,"timestamp":1665931090,"version":"1.8_0"},"r-genabel.data":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.genabel.org, http://forum.genabel.org, http://genabel.r-forge.r-project.org/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GenABEL.data/GenABEL.data_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/GenABEL.data_1.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"GenABEL.data package consists of a data set used by GenABEL functions","text_prefix":false,"timestamp":1539919274,"version":"1.0.0"},"r-genalg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/egonw/genalg","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genalg/genalg_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/genalg_0.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"R based genetic algorithm for binary and floating point chromosomes.","text_prefix":false,"timestamp":1539633933,"version":"0.2.1"},"r-gender":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/gender/, https://github.com/ropensci/gender","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gender/gender_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/gender_0.6.0.tar.gz"],"subdirs":["noarch"],"summary":"Infers state-recorded gender categories from first names and dates of birth using historical datasets. By using these datasets instead of lists of male and female names, this package is able to more accurately infer the gender of a name, and it is able to report the probability that a name was male or female. GUIDELINES: This method must be used cautiously and responsibly. Please be sure to see the guidelines and warnings about usage in the 'README' or the package documentation. See Blevins and Mullen (2015) <http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.","text_prefix":false,"timestamp":1622035879,"version":"0.6.0"},"r-genemodel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/greymonroe/genemodel","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genemodel/genemodel_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/genemodel_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.","text_prefix":false,"timestamp":1758379317,"version":"1.1.0"},"r-genenet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://strimmerlab.org/software/genenet/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GeneNet/GeneNet_1.2.17.tar.gz","https://cran.r-project.org/src/contrib/GeneNet_1.2.17.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and  Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).","text_prefix":false,"timestamp":1540037937,"version":"1.2.17"},"r-generalizedhyperbolic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://r-forge.r-project.org/projects/rmetrics/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GeneralizedHyperbolic/GeneralizedHyperbolic_0.8-7.tar.gz","https://cran.r-project.org/src/contrib/GeneralizedHyperbolic_0.8-7.tar.gz"],"subdirs":["noarch"],"summary":"Functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, normal inverse Gaussian distribution and generalized inverse Gaussian distribution, including fitting of these distributions to data. Linear models with hyperbolic errors may be fitted using hyperblmFit.","text_prefix":false,"timestamp":1757860376,"version":"0.8_7"},"r-generics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/generics","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/generics/generics_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/generics_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"In order to reduce potential package dependencies and conflicts,  generics provides a number of commonly used S3 generics.","text_prefix":false,"timestamp":1588390025,"version":"0.1.4"},"r-geneset":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/GangLiLab/geneset","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/geneset/geneset_0.2.8.tar.gz","https://cran.r-project.org/src/contrib/geneset_0.2.8.tar.gz"],"subdirs":["noarch"],"summary":"Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), 'KEGG' (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), 'WikiPathway' (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), 'MsigDb' (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), 'Reactome' (David et al. (2011) <doi:10.1093/nar/gkq1018>), 'MeSH' (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), 'DisGeNET' (Janet et al. (2017) <doi:10.1093/nar/gkw943>), 'Disease Ontology' (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), 'Network of Cancer Genes' (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and 'COVID-19' (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>). Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of terms ID and term description.","text_prefix":false,"timestamp":1785876868,"version":"0.2.8"},"r-genetics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=genetics","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genetics/genetics_1.3.8.1.3.tar.gz","https://cran.r-project.org/src/contrib/genetics_1.3.8.1.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Classes and methods for handling genetic data. Includes classes to represent genotypes and haplotypes at single markers up to multiple markers on multiple chromosomes. Function include allele frequencies, flagging homo/heterozygotes, flagging carriers of certain alleles, estimating and testing for Hardy-Weinberg disequilibrium, estimating and testing for linkage disequilibrium, ...","text_prefix":false,"timestamp":1540057776,"version":"1.3.8.1.3"},"r-genie":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.gagolewski.com/software/genie/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genie/genie_1.0.7.tar.gz","https://cran.r-project.org/src/contrib/genie_1.0.7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A new hierarchical clustering linkage criterion: the Genie algorithm links two clusters in such a way that a chosen economic inequity measure (e.g., the Gini index) of the cluster sizes does not increase drastically above a given threshold. Benchmarks indicate a high practical usefulness of the introduced method: it most often outperforms the Ward or average linkage in terms of the clustering quality while retaining the single linkage speed, see (Gagolewski et al. 2016a <DOI:10.1016/j.ins.2016.05.003>, 2016b <DOI:10.1007/978-3-319-45656-0_16>) for more details.","text_prefix":false,"timestamp":1588801446,"version":"1.0.7"},"r-geniebpc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://genie-bpc.github.io/genieBPC/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genieBPC/genieBPC_2.2.0.tar.gz","https://cran.r-project.org/src/contrib/genieBPC_2.2.0.tar.gz"],"subdirs":["noarch"],"summary":"The American Association Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) BioPharma Collaborative represents a multi-year, multi-institution effort to build a pan-cancer repository of linked clinico-genomic data. The genomic and clinical data are provided in multiple releases (separate releases for each cancer cohort with updates following data corrections), which are stored on the data sharing platform 'Synapse' <https://www.synapse.org/>. The 'genieBPC' package provides a seamless way to obtain the data corresponding to each release from 'Synapse' and to prepare datasets for analysis.","text_prefix":false,"timestamp":1776729278,"version":"2.2.0"},"r-genieclust":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://genieclust.gagolewski.com/","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genieclust/genieclust_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/genieclust_1.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A retake on the Genie algorithm - a robust hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). Now faster and more memory efficient; determining the whole hierarchy for datasets of 10M points in low dimensional Euclidean spaces or 100K points in high-dimensional ones takes only 1-2 minutes. Allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of 'HDBSCAN*' (that is able to detect a predefined number of clusters and hence it does not dependent on the somewhat fragile 'eps' parameter). The package also features an implementation of economic inequity indices (the Gini, Bonferroni index) and external cluster validity measures (partition similarity scores; e.g., the adjusted Rand, Fowlkes-Mallows, adjusted mutual information, pair sets index). See also the 'Python' version of 'genieclust' available on 'PyPI', which supports sparse data, more metrics, and even larger datasets.","text_prefix":false,"timestamp":1596443998,"version":"1.2.0"},"r-genio":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/OchoaLab/genio","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genio/genio_1.1.2.tar.gz","https://cran.r-project.org/src/contrib/genio_1.1.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Implements readers and writers for file formats associated with genetics data.  Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations.  Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files).  There are also make functions for FAM and BIM tables with default values to go with simulated genotype data.","text_prefix":false,"timestamp":1758600254,"version":"1.1.2"},"r-genius":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/josiahparry/genius","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genius/genius_2.2.3.tar.gz","https://cran.r-project.org/src/contrib/genius_2.2.3.tar.gz"],"subdirs":["noarch"],"summary":"Easily access song lyrics in a tidy way.","text_prefix":false,"timestamp":1552482266,"version":"2.2.3"},"r-genkern":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=GenKern","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GenKern/GenKern_1.2-60.tar.gz","https://cran.r-project.org/src/contrib/GenKern_1.2-60.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Computes generalised KDEs","text_prefix":false,"timestamp":1730279467,"version":"1.2_60"},"r-genlasso":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/statsmaths/genlasso","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/genlasso/genlasso_1.6.1.tar.gz","https://cran.r-project.org/src/contrib/genlasso_1.6.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Provides fast algorithms for computing the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. 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Some popular fonts are included and ready to use.","text_prefix":false,"timestamp":1790194787,"version":"0.2.1"},"r-ggalign":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Yunuuuu/ggalign, https://yunuuuu.github.io/ggalign/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggalign/ggalign_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/ggalign_1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"A 'ggplot2' extension providing an integrative framework for composable visualization, enabling the creation of complex multi-plot layouts such as insets, circular arrangements, and multi-panel compositions. Built on the grammar of graphics, it offers tools to align, stack, and nest plots, simplifying the construction of richly annotated figures for high-dimensional data contexts\u2014such as genomics, transcriptomics, and microbiome studies\u2014by making it easy to link related plots, overlay clustering results, or highlight shared patterns.","text_prefix":false,"timestamp":1760884089,"version":"1.2.0"},"r-ggalluvial":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/corybrunson/ggalluvial/","doc_url":"https://corybrunson.github.io/ggalluvial/","home":"https://cloud.r-project.org/package=ggalluvial","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggalluvial/ggalluvial_0.12.6.tar.gz","https://cran.r-project.org/src/contrib/ggalluvial_0.12.6.tar.gz"],"subdirs":["noarch"],"summary":"Alluvial diagrams use x-splines, sometimes augmented with stacked histograms, to visualize multi-dimensional or repeated-measures data with categorical or ordinal variables. They can be viewed as simplified and standardized Sankey diagrams; see Riehmann, Hanfler, and Froehlich (2005) [doi:10.1109/INFVIS.2005.1532152](https://doi.org/10.1109/INFVIS.2005.1532152) and Rosvall and Bergstrom (2010) [doi:10.1371/journal.pone.0008694](https://doi.org/10.1371/journal.pone.0008694). This package provides ggplot2 layers to produce alluvial diagrams from tidy data.","text_prefix":false,"timestamp":1567488852,"version":"0.12.6"},"r-ggally":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ggobi/ggally","home":"https://ggobi.github.io/ggally","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GGally/GGally_2.4.0.tar.gz","https://cran.r-project.org/src/contrib/GGally_2.4.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.","text_prefix":false,"timestamp":1537201401,"version":"2.4.0"},"r-ggalt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hrbrmstr/ggalt","license":"AGPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggalt/ggalt_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/ggalt_0.4.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"A compendium of new geometries, coordinate systems, statistical  transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities,  univariate average shifted histograms, a new map coordinate system based on the  'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of  geom_map(), formatters for \"bytes\", a stat_stepribbon() function, increased 'plotly' compatibility and the 'StateFace' open source font 'ProPublica'. Further new  functionality includes lollipop charts, dumbbell charts, the ability to encircle points and coordinate-system-based text annotations.","text_prefix":false,"timestamp":0,"version":"0.4.0"},"r-gganimate":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/thomasp85/gganimate","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gganimate/gganimate_1.0.11.tar.gz","https://cran.r-project.org/src/contrib/gganimate_1.0.11.tar.gz"],"subdirs":["noarch"],"summary":"The grammar of graphics as implemented in the 'ggplot2' package has been successful in providing a powerful API for creating static  visualisation. In order to extend the API for animated graphics this package provides a completely new set of grammar, fully compatible with 'ggplot2'  for specifying transitions and animations in a flexible and extensible way.","text_prefix":false,"timestamp":1594198620,"version":"1.0.11"},"r-ggbeeswarm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/eclarke/ggbeeswarm","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggbeeswarm/ggbeeswarm_0.7.3.tar.gz","https://cran.r-project.org/src/contrib/ggbeeswarm_0.7.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides two methods of plotting categorical scatter plots such that the arrangement of points within a category reflects the density of data at that region, and avoids over-plotting.","text_prefix":false,"timestamp":1540056235,"version":"0.7.3"},"r-ggblanket":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/davidhodge931/ggblanket/","home":"https://davidhodge931.github.io/ggblanket/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggblanket/ggblanket_21.0.0.tar.gz","https://cran.r-project.org/src/contrib/ggblanket_21.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Wrapper 'ggplot2' functions for publication-quality visualisation. Aligned with 'ggplot2' and 'tidyverse'. Covers much of what 'ggplot2' does.","text_prefix":false,"timestamp":1652771813,"version":"21.0.0"},"r-ggblend":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mjskay/ggblend/","home":"https://mjskay.github.io/ggblend","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggblend/ggblend_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/ggblend_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Algebra of operations for blending, copying, adjusting, and compositing layers in 'ggplot2'. Supports copying and adjusting the aesthetics or parameters of an existing layer, partitioning a layer into multiple pieces for re-composition, applying affine transformations to layers, and combining layers (or partitions of layers) using blend modes (including commutative blend modes, like multiply and darken). Blend mode support is particularly useful for creating plots with overlapping groups where the layer drawing order does not change the output; see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.","text_prefix":false,"timestamp":1759649091,"version":"0.1.2"},"r-ggbreak":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://www.frontiersin.org/articles/10.3389/fgene.2021.774846/full","home":"https://github.com/YuLab-SMU/ggbreak","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ggbreak/ggbreak_0.2.2.tar.gz","https://cloud.r-project.org/src/contrib/ggbreak_0.2.2.tar.gz"],"subdirs":["noarch"],"summary":"An implementation of scale functions for setting axis breaks of a 'gg' plot.","text_prefix":false,"timestamp":1790155828,"version":"0.2.2"},"r-ggbump":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ggbump","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggbump/ggbump_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/ggbump_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"A geom for ggplot to create bump plots. Can be good to use for showing rank over time.","text_prefix":false,"timestamp":1758509285,"version":"0.1.0"},"r-ggchangepoint":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ggchangepoint","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ggchangepoint/ggchangepoint_0.5.0.tar.gz","https://cloud.r-project.org/src/contrib/ggchangepoint_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"R provides fantastic tools for changepoint analysis, but plots generated by the tools do not have the 'ggplot2' style. This tool, however, combines 'changepoint', 'changepoint.np' and 'ecp' together, and uses 'ggplot2' to visualize changepoints.","text_prefix":false,"timestamp":1790390337,"version":"0.5.0"},"r-ggcharts":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/thomas-neitmann/ggcharts","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggcharts/ggcharts_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/ggcharts_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Streamline the creation of common charts by taking care of a lot of data preprocessing and plot customization for the user. Provides a high-level interface to create plots using 'ggplot2'.","text_prefix":false,"timestamp":1589387503,"version":"0.2.1"},"r-ggconf":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/caprice-j/ggconf","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggconf/ggconf_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/ggconf_0.1.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A flexible interface for ggplot2::theme(), potentially saving 50% of your typing.","text_prefix":false,"timestamp":1534102435,"version":"0.1.3"},"r-ggcorrplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.sthda.com/english/wiki/ggcorrplot","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggcorrplot/ggcorrplot_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/ggcorrplot_0.3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The 'ggcorrplot' package can be used to visualize easily a correlation matrix using 'ggplot2'. It provides a solution for reordering the correlation matrix and displays the significance level on the plot. It also includes a function for computing a matrix of correlation p-values.","text_prefix":false,"timestamp":0,"version":"0.3.0"},"r-ggcorset":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ggcorset","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggcorset/ggcorset_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/ggcorset_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Corset plots are a visualization technique used strictly to visualize repeat measures at 2 time points (such as pre- and post- data). The distribution of measurements are visualized at each time point, whilst the trajectories of individual change are visualized by connecting the pre- and post- values linearly. These lines can be coloured to represent the magnitude of change, or other user-defined value. This method of visualization is ideal for showing the heterogeneity of data, including differences by sub-groups. The package relies on 'ggplot2' allowing for easy integration so that users can customize their visualizations as required. Users can create corset plots using data in either wide or long format using the functions gg_corset() or gg_corset_elongated(), respectively.","text_prefix":false,"timestamp":1653987634,"version":"0.5.0"},"r-ggdag":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-causal/ggdag, https://r-causal.github.io/ggdag//","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggdag/ggdag_0.2.13.tar.gz","https://cran.r-project.org/src/contrib/ggdag_0.2.13.tar.gz"],"subdirs":["noarch"],"summary":"Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses the 'DAGitty' web tool (<http://dagitty.net>) for creating and analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.","text_prefix":false,"timestamp":1758745670,"version":"0.2.13"},"r-ggdark":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ggdark","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggdark/ggdark_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/ggdark_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Activate dark mode on your favorite 'ggplot2' theme with dark_mode() or use the dark versions of 'ggplot2' themes, including dark_theme_gray(), dark_theme_minimal(), and others. When a dark theme is applied, all geom color and geom fill defaults are changed to make them visible against a dark background. To restore the defaults to their original values, use invert_geom_defaults().","text_prefix":false,"timestamp":1758695281,"version":"0.2.1"},"r-ggdendro":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/andrie/ggdendro","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggdendro/ggdendro_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/ggdendro_0.2.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This is a set of tools for dendrograms and tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to clearly separate data from the presentation. Unfortunately the plot method for dendrograms plots directly to a plot device without exposing the data. The 'ggdendro' package resolves this by making available functions that extract the dendrogram plot data. The package provides implementations for tree, rpart, as well as diana and agnes cluster diagrams.","text_prefix":false,"timestamp":1538361812,"version":"0.2.0"},"r-ggdensity":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://jamesotto852.github.io/ggdensity/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggdensity/ggdensity_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/ggdensity_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"The 'ggplot2' package provides simple functions for visualizing contours of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators as well as more interpretable visualizations based on highest density regions instead of the traditional height of the estimated density surface.","text_prefix":false,"timestamp":1666652835,"version":"1.0.1"},"r-ggdist":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mjskay/ggdist","home":"http://mjskay.github.io/ggdist","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggdist/ggdist_3.3.3.tar.gz","https://cran.r-project.org/src/contrib/ggdist_3.3.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","osx-arm64","win-64"],"summary":"Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.","text_prefix":false,"timestamp":1757612755,"version":"3.3.3"},"r-ggdistribute":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/iamamutt/ggdistribute","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggdistribute/ggdistribute_1.0.3.tar.gz","https://cran.r-project.org/src/contrib/ggdistribute_1.0.3.tar.gz"],"subdirs":["noarch"],"summary":"The 'ggdistribute' package is an extension for plotting posterior or other types of unimodal distributions that require overlaying information about a distribution's intervals. It makes use of the 'ggproto' system to extend 'ggplot2', providing additional \"geoms\", \"stats\", and \"positions.\" The extensions integrate with existing 'ggplot2' layer elements.","text_prefix":false,"timestamp":1758359599,"version":"1.0.3"},"r-ggeasy":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jonocarroll/ggeasy","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggeasy/ggeasy_0.1.6.tar.gz","https://cran.r-project.org/src/contrib/ggeasy_0.1.6.tar.gz"],"subdirs":["noarch"],"summary":"Provides a series of aliases to commonly used but difficult to remember 'ggplot2' sequences.","text_prefix":false,"timestamp":1665539570,"version":"0.1.6"},"r-ggedit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/yonicd/ggedit","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggedit/ggedit_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/ggedit_0.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Interactively edit 'ggplot2' layer and theme aesthetics definitions.","text_prefix":false,"timestamp":1538504750,"version":"0.4.1"},"r-ggeffects":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://strengejacke.github.io/ggeffects","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggeffects/ggeffects_2.3.2.tar.gz","https://cran.r-project.org/src/contrib/ggeffects_2.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Compute marginal effects from statistical models and returns the  result as tidy data frames. These data frames are ready to use with the  'ggplot2'-package. Marginal effects can be calculated for many different  models. Interaction terms, splines and polynomial terms are also supported.  The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a  generic plot()-method to plot the results using 'ggplot2'.","text_prefix":false,"timestamp":1533982119,"version":"2.3.2"},"r-ggextra":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/daattali/ggExtra","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggExtra/ggExtra_0.11.0.tar.gz","https://cran.r-project.org/src/contrib/ggExtra_0.11.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Collection of functions and layers to enhance 'ggplot2'. The  flagship function is 'ggMarginal()', which can be used to add marginal histograms/boxplots/density plots to 'ggplot2' scatterplots.","text_prefix":false,"timestamp":1538440062,"version":"0.11.0"},"r-ggfittext":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://wilkox.org/ggfittext","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggfittext/ggfittext_0.10.4.tar.gz","https://cran.r-project.org/src/contrib/ggfittext_0.10.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides 'ggplot2' geoms to fit text into a box by growing, shrinking or wrapping the text.","text_prefix":false,"timestamp":1540063709,"version":"0.10.4"},"r-ggflowchart":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://nrennie.github.io/ggflowchart/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggflowchart/ggflowchart_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/ggflowchart_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Flowcharts can be a useful way to visualise complex processes. 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This package is particularly useful for visualizing metadata on a discrete axis, or summary graphics on a continuous axis such as a boxplot or a density distribution.","text_prefix":false,"timestamp":1635093314,"version":"0.4.1"},"r-ggsignif":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/const-ae/ggsignif","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggsignif/ggsignif_0.6.4.tar.gz","https://cran.r-project.org/src/contrib/ggsignif_0.6.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Enrich your 'ggplots' with group-wise comparisons. This package provides an easy way to indicate if two groups are significantly different. Commonly this is shown by a bracket on top connecting the groups of interest which itself is annotated with the level of significance (NS, *, **, ***). 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The suite also includes new statistics to compute proportions, weighted mean and cross-tabulation statistics, as well as new geometries to add alternative background color to a plot.","text_prefix":false,"timestamp":1741619393,"version":"0.14.0"},"r-ggstatsplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/IndrajeetPatil/ggstatsplot","home":"https://indrajeetpatil.github.io/ggstatsplot/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ggstatsplot/ggstatsplot_1.1.1.tar.gz","https://cloud.r-project.org/src/contrib/ggstatsplot_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. 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For further examples and documentation, please proceed to the 'ggtern' website.","text_prefix":false,"timestamp":1541954155,"version":"4.0.0"},"r-ggtext":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://wilkelab.org/ggtext","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ggtext/ggtext_0.2.0.tar.gz","https://cloud.r-project.org/src/contrib/ggtext_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"A 'ggplot2' extension that enables the rendering of complex formatted plot labels (titles, subtitles, facet labels, axis labels, etc.). 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The package also provides plot helper functions to quickly produce commonly used time series graphics, including time plots, season plots, and seasonal sub-series plots.","text_prefix":false,"timestamp":1770729112,"version":"0.2.0"},"r-ggupset":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/const-ae/ggupset","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggupset/ggupset_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/ggupset_0.4.1.tar.gz"],"subdirs":["noarch"],"summary":"Replace the standard x-axis in 'ggplots' with a combination matrix to visualize complex set overlaps. 'UpSet' has introduced a new way to visualize the overlap of sets as an alternative to Venn diagrams.  This package provides a simple way to produce such plots using 'ggplot2'.  In addition it can convert any categorical axis into a combination matrix axis.","text_prefix":false,"timestamp":1585171424,"version":"0.4.1"},"r-ggvenn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ggvenn","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggvenn/ggvenn_0.1.19.tar.gz","https://cran.r-project.org/src/contrib/ggvenn_0.1.19.tar.gz"],"subdirs":["noarch"],"summary":"An easy-to-use way to draw pretty venn diagram by 'ggplot2'.","text_prefix":false,"timestamp":1667638187,"version":"0.1.19"},"r-ggvenndiagram":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gaospecial/ggVennDiagram","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ggVennDiagram/ggVennDiagram_1.5.7.tar.gz","https://cran.r-project.org/src/contrib/ggVennDiagram_1.5.7.tar.gz"],"subdirs":["noarch"],"summary":"Easy-to-use functions to generate 2-7 sets Venn plot in publication quality. 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Moreover, it supports meta-analysis of multiple overlapping studies.","text_prefix":false,"timestamp":1759056458,"version":"0.1.0"},"r-ghql":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/ghql (devel) https://docs.ropensci.org/ghql (docs)","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ghql/ghql_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/ghql_0.1.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A 'GraphQL' client, with an R6 interface for initializing a connection to a 'GraphQL' instance, and methods for constructing queries, including fragments and parameterized queries. 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The main tools are:  (1) 'g:GOSt' - functional enrichment analysis and visualization of gene lists;  (2) 'g:Convert' - gene/protein/transcript identifier conversion across various namespaces; (3) 'g:Orth' - orthology search across species; (4) 'g:SNPense' - mapping SNP rs identifiers to chromosome positions, genes and variant effects This package is an R interface corresponding to the 2019 update of 'g:Profiler' and provides access to 'g:Profiler' for versions 'e94_eg41_p11' and higher. See the package 'gProfileR' for accessing older versions from the 'g:Profiler' toolset.","text_prefix":false,"timestamp":1576679312,"version":"0.2.4"},"r-gpseq":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=GPseq","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://cran.r-project.org/src/contrib/Archive/GPseq/GPseq_0.5.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Some functions for modeling sequence read counts as a generalized poisson model and to use this model for detecting differentially expressed genes in different conditions and differentially spliced exons.","text_prefix":false,"timestamp":0,"version":"0.5"},"r-gptk":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=gptk","license":"BSD_2_clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gptk/gptk_1.08.tar.gz","https://cran.r-project.org/src/contrib/gptk_1.08.tar.gz"],"subdirs":["noarch"],"summary":"The gptk package implements a general-purpose toolkit for Gaussian process regression with a variety of covariance functions (e.g. RBF, Mattern, polynomial, etc). Based on a MATLAB implementation by Neil D. Lawrence. See inst/doc/index.html for more details.","text_prefix":false,"timestamp":1758687153,"version":"1.08"},"r-grab":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/cran/GRAB","home":"https://CRAN.R-project.org/package=GRAB","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/GRAB/GRAB_0.2.5.tar.gz","https://cran.r-project.org/src/contrib/GRAB_0.2.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides a comprehensive suite of genome-wide association study (GWAS) methods specifically designed for biobank-scale data. The package offers computationally efficient and robust association tests for time-to-event traits (e.g., Bi et al., 2020 <doi:10.1016/j.ajhg.2020.06.003>), ordinal categorical traits (e.g., Bi et al., 2021 <doi:10.1016/j.ajhg.2021.03.019>), and longitudinal traits (Xu et al., 2025 <doi:10.1038/s41467-025-56669-1>). Additionally, it includes functions for simulating genotype and phenotype data to support research and method development.","text_prefix":false,"timestamp":1759239809,"version":"0.2.5"},"r-graddescent":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/drizzersilverberg/gradDescentR","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gradDescent/gradDescent_3.0.tar.gz","https://cran.r-project.org/src/contrib/gradDescent_3.0.tar.gz"],"subdirs":["noarch"],"summary":"An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. The variants of gradient descent algorithm are : Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load. Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically. Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average. Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning. Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning. Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning. Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning. RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability. Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning. Stochastic Variance Reduce Gradient (SVRG), which is an optimization SGD-based algorithm to accelerates the process toward converging by reducing the gradient. Semi Stochastic Gradient Descent (SSGD),which is a SGD-based algorithm that combine GD and SGD to accelerates the process toward converging by choosing one of the gradients at a time. Stochastic Recursive Gradient Algorithm (SARAH), which is an optimization algorithm similarly SVRG to accelerates the process toward converging by accumulated stochastic information. Stochastic Recursive Gradient Algorithm+ (SARAHPlus), which is a SARAH practical variant algorithm to accelerates the process toward converging provides a possibility of earlier termination.","text_prefix":false,"timestamp":1758742603,"version":"3.0"},"r-grafify":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ashenoy-cmbi/grafify","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/grafify/grafify_5.1.0.tar.gz","https://cran.r-project.org/src/contrib/grafify_5.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Easily explore data by generating different kinds of graphs with few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways. Choose from colourblind-friendly 12 discreet , 3 continuous (3 palettes) and 3 divergent colour palettes. Simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs. Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans() wrappers). Also includes small datasets for practicing code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify-vignettes.netlify.app/>. Citation: <doi:10.5281/zenodo.5136508>.","text_prefix":false,"timestamp":1666622594,"version":"5.1.0"},"r-graphicalvar":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=graphicalVAR","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/graphicalVAR/graphicalVAR_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/graphicalVAR_0.4.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.","text_prefix":false,"timestamp":1665904338,"version":"0.4.1"},"r-graphlayouts":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/schochastics/graphlayouts","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/graphlayouts/graphlayouts_1.2.5.tar.gz","https://cran.r-project.org/src/contrib/graphlayouts_1.2.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Several new layout algorithms to visualize networks are provided which are not part of 'igraph'.  Most are based on the concept of stress majorization by Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>.  Some more specific algorithms allow to emphasize hidden group structures in networks or focus on specific nodes.","text_prefix":false,"timestamp":1660231090,"version":"1.2.5"},"r-graphql":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://graphql.org (upstream) https://github.com/ropensci/graphql (devel)","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/graphql/graphql_1.5.3.tar.gz","https://cran.r-project.org/src/contrib/graphql_1.5.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Bindings to the 'libgraphqlparser' C++ library. Parses GraphQL syntax and exports the AST in JSON format.","text_prefix":false,"timestamp":1621748859,"version":"1.5.3"},"r-graphtweets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://graphTweets.john-coene.com","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/graphTweets/graphTweets_0.5.3.tar.gz","https://cran.r-project.org/src/contrib/graphTweets_0.5.3.tar.gz"],"subdirs":["noarch"],"summary":"Allows building an edge table from data frame of tweets, also provides function to build nodes and another create a temporal graph.","text_prefix":false,"timestamp":1550562513,"version":"0.5.3"},"r-grates":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/reconverse/grates","home":"https://www.reconverse.org/grates/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/grates/grates_1.8.0.tar.gz","https://cran.r-project.org/src/contrib/grates_1.8.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a coherent interface and implementation for creating grouped date classes. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.","text_prefix":false,"timestamp":1666195657,"version":"1.8.0"},"r-greg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://gforge.se","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Greg/Greg_2.0.3.tar.gz","https://cran.r-project.org/src/contrib/Greg_2.0.3.tar.gz"],"subdirs":["noarch"],"summary":"Methods for manipulating regression models and for describing these in a style adapted for medical journals. Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. In addition to the descriptive methods, there are functions for the robust covariance matrix provided by the 'sandwich' package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for time-splitting a dataset when modeling non-proportional hazards in Cox regressions.","text_prefix":false,"timestamp":1758922661,"version":"2.0.3"},"r-greta":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/greta-dev/greta","home":"https://greta-stats.org","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://greta-dev.r-universe.dev/src/contrib/greta_0.5.0.9000.tar.gz"],"subdirs":["noarch"],"summary":"Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'.  greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it\u2019s easy to extend and build on.  See the website for more information, including tutorials, examples, package documentation, and the greta forum.","text_prefix":false,"timestamp":1766287384,"version":"0.5.0.9000"},"r-greybox":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data.","dev_url":"https://github.com/config-i1/greybox","home":"https://github.com/config-i1/greybox","license":"LGPL-2.1-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/greybox/greybox_2.0.9.tar.gz","https://cloud.r-project.org/src/contrib/greybox_2.0.9.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Toolbox for Model Building and Forecasting","text_prefix":false,"timestamp":1791122305,"version":"2.0.9"},"r-grf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/grf-labs/grf","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/grf/grf_2.6.1.tar.gz","https://cloud.r-project.org/src/contrib/grf_2.6.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables).","text_prefix":false,"timestamp":1546350133,"version":"2.6.1"},"r-gribr":{"activate.d":true,"binary_prefix":true,"deactivate.d":true,"description":"Easily read GRIB data into native R data types.","dev_url":"https://github.com/nawendt/gribr","home":"http://github.com/nawendt/gribr","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/nawendt/gribr/archive/v1.2.7.tar.gz","subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"R interface for GRIB files using ECMWF ecCodes.","text_prefix":false,"timestamp":1780320965,"version":"1.2.7"},"r-gridbase":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gridBase","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gridBase/gridBase_0.4-7.tar.gz","https://cran.r-project.org/src/contrib/gridBase_0.4-7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Integration of base and grid graphics","text_prefix":false,"timestamp":1536954372,"version":"0.4_7"},"r-gridextra":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gridExtra","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gridExtra/gridExtra_2.3.1.tar.gz","https://cran.r-project.org/src/contrib/gridExtra_2.3.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a number of user-level functions to work with \"grid\" graphics, notably to arrange multiple grid-based plots on a page, and draw tables.","text_prefix":false,"timestamp":1536504155,"version":"2.3.1"},"r-gridgraphics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/pmur002/gridgraphics","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gridGraphics/gridGraphics_0.5-1.tar.gz","https://cran.r-project.org/src/contrib/gridGraphics_0.5-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions to convert a page of plots drawn with the  'graphics' package into identical output drawn with the 'grid' package. The result looks like the original 'graphics'-based plot, but consists of 'grid' grobs and viewports that can then be manipulated with  'grid' functions (e.g., edit grobs and revisit viewports).","text_prefix":false,"timestamp":1537041000,"version":"0.5_1"},"r-gridpattern":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://trevorldavis.com/R/gridpattern/, https://github.com/trevorld/gridpattern","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gridpattern/gridpattern_1.4.2.tar.gz","https://cran.r-project.org/src/contrib/gridpattern_1.4.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides 'grid' grobs that fill in a user-defined area with various patterns.  Includes enhanced versions of the geometric and image-based patterns originally contained in the 'ggpattern' package as well as original 'pch', 'polygon_tiling', 'regular_polygon', 'rose', 'text', 'wave', and 'weave' patterns plus support for custom user-defined patterns.","text_prefix":false,"timestamp":1648010455,"version":"1.4.2"},"r-gridsvg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=gridSVG","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gridSVG/gridSVG_1.7-7.tar.gz","https://cran.r-project.org/src/contrib/gridSVG_1.7-7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions to export graphics drawn with package grid to SVG format.  Additional functions provide access to SVG features that are not available in standard R graphics, such as hyperlinks, animation, filters, masks, clipping paths, and gradient and pattern fills.","text_prefix":false,"timestamp":1540066367,"version":"1.7_7"},"r-gridtext":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://wilkelab.org/gridtext","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/gridtext/gridtext_0.1.6.tar.gz","https://cran.r-project.org/src/contrib/gridtext_0.1.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides support for rendering of formatted text using 'grid' graphics. Text can be formatted via a minimal subset of 'Markdown', 'HTML', and inline 'CSS' directives, and it can be rendered both with and without word wrap.","text_prefix":false,"timestamp":1580004551,"version":"0.1.6"},"r-grimport":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=grImport","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/grImport/grImport_0.9-7.tar.gz","https://cran.r-project.org/src/contrib/grImport_0.9-7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions for converting, importing, and drawing PostScript  pictures in R plots.","text_prefix":false,"timestamp":1540040784,"version":"0.9_7"},"r-grimport2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://r-forge.r-project.org/projects/grimport/,https://stattech.wordpress.fos.auckland.ac.nz/2013/09/,https://stattech.wordpress.fos.auckland.ac.nz/2016/08/30/2016-11-the-butterfly-affectation/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/grImport2/grImport2_0.3-3.tar.gz","https://cran.r-project.org/src/contrib/grImport2_0.3-3.tar.gz"],"subdirs":["noarch"],"summary":"Functions for importing external vector images and drawing them as part of 'R' plots.  This package is different from the 'grImport' package because, where that package imports 'PostScript' format images, this package imports 'SVG' format images.  Furthermore, this package imports a specific subset of 'SVG', so external images must be preprocessed using a package like 'rsvg' to produce 'SVG' that this package can import.  'SVG' features that are not supported by 'R' graphics, e.g., gradient fills, can be imported and then exported via the 'gridSVG' package.","text_prefix":false,"timestamp":1565433227,"version":"0.3_3"},"r-grobblr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=grobblR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/grobblR/grobblR_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/grobblR_0.2.2.tar.gz"],"subdirs":["noarch"],"summary":"A tool which allows users the ability to intuitively create flexible, reproducible portable document format reports comprised of aesthetically pleasing tables, images, plots and/or text.","text_prefix":false,"timestamp":1593314047,"version":"0.2.2"},"r-groupdata2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ludvigolsen/groupdata2","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/groupdata2/groupdata2_2.0.5.tar.gz","https://cran.r-project.org/src/contrib/groupdata2_2.0.5.tar.gz"],"subdirs":["noarch"],"summary":"Methods for dividing data into groups. 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Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. 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Allows multiple tracks of annotation for RowSideColors and ColSideColors.","text_prefix":false,"timestamp":1535631239,"version":"1.3"},"r-heatmap3":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=heatmap3","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/heatmap3/heatmap3_1.1.9.tar.gz","https://cran.r-project.org/src/contrib/heatmap3_1.1.9.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An improved heatmap package. Completely compatible with the original R function 'heatmap', and provides more powerful and convenient features.","text_prefix":false,"timestamp":1540463417,"version":"1.1.9"},"r-heatmaply":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://talgalili.github.io/heatmaply/, https://cran.r-project.org/package=heatmaply, https://github.com/talgalili/heatmaply/, https://www.r-statistics.com/tag/heatmaply/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/heatmaply/heatmaply_1.6.0.tar.gz","https://cran.r-project.org/src/contrib/heatmaply_1.6.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Create interactive cluster 'heatmaps' that can be saved as a stand- alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for visualizing observations, correlations, missing values patterns, and more. Interactive 'heatmaps' allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the 'heatmap' by dragging a rectangle around the relevant area. This work is based on the 'ggplot2' and 'plotly.js' engine. It produces similar 'heatmaps' as 'heatmap.2' or 'd3heatmap', with the advantage of speed ('plotly.js' is able to handle larger size matrix), the ability to zoom from the 'dendrogram' panes, and the placing of factor variables in the sides of the 'heatmap'.","text_prefix":false,"timestamp":1529935081,"version":"1.6.0"},"r-heavy":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://heavy.mat.utfsm.cl","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/heavy/heavy_0.38.196.tar.gz","https://cran.r-project.org/src/contrib/heavy_0.38.196.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions to perform robust estimation considering heavy-tailed distributions. Currently, the package includes routines for linear regression, linear mixed-effect models, Grubbs' model, multivariate location and scatter estimation, multivariate regression, penalized splines, random variate generation and some support functions.","text_prefix":false,"timestamp":1665003775,"version":"0.38.196"},"r-henna":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/andrei-stoica26/henna","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/henna/henna_0.8.5.tar.gz","https://cran.r-project.org/src/contrib/henna_0.8.5.tar.gz"],"subdirs":["noarch"],"summary":"A visualization suite primarily designed for single-cell RNA-sequencing data analysis applications but well-suited for other purposes as well. It introduces novel plots to represent two-variable and frequency data and optimizes some commonly used plotting options (e.g., correlation, network, density, alluvial and volcano plots) for ease of usage and flexibility.","text_prefix":false,"timestamp":1784312642,"version":"0.8.5"},"r-heplots":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://datavis.ca/R/index.php#heplots","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/heplots/heplots_1.8.6.tar.gz","https://cloud.r-project.org/src/contrib/heplots_1.8.6.tar.gz"],"subdirs":["noarch"],"summary":"Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and- products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.","text_prefix":false,"timestamp":1603979687,"version":"1.8.6"},"r-here":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/krlmlr/here, http://krlmlr.github.io/here","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/here/here_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/here_1.0.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Constructs paths to your project's files. The 'here()' function uses a reasonable heuristics to find your project's files, based on the current working directory at the time when the package is loaded. Use it as a drop-in replacement for 'file.path()', it will always locate the files relative to your project root.","text_prefix":false,"timestamp":1537373015,"version":"1.0.2"},"r-hergm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=hergm","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hergm/hergm_4.1-10.tar.gz","https://cran.r-project.org/src/contrib/hergm_4.1-10.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Hierarchical exponential-family random graph models with local dependence. See Schweinberger and Luna (2018) <doi:10.18637/jss.v085.i01>.","text_prefix":false,"timestamp":1665888092,"version":"4.1_10"},"r-hexbin":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://github.com/edzer/hexbin","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hexbin/hexbin_1.28.6.tar.gz","https://cran.r-project.org/src/contrib/hexbin_1.28.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Binning and plotting functions for hexagonal bins.","text_prefix":false,"timestamp":0,"version":"1.28.6"},"r-hexview":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=hexView","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hexView/hexView_0.3-4.tar.gz","https://cran.r-project.org/src/contrib/hexView_0.3-4.tar.gz"],"subdirs":["noarch"],"summary":"Functions to view files in raw binary form like in a hex editor.  Additional functions to specify and read arbitrary binary formats.","text_prefix":false,"timestamp":1758283219,"version":"0.3_4"},"r-hgnchelper":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/waldronlab/HGNChelper","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/HGNChelper/HGNChelper_0.8.15.tar.gz","https://cran.r-project.org/src/contrib/HGNChelper_0.8.15.tar.gz"],"subdirs":["noarch"],"summary":"Contains functions for identifying and correcting HGNC human gene symbols and MGI mouse gene symbols which have been converted to date format by Excel, withdrawn, or aliased. Also contains functions for reversibly converting between HGNC symbols and valid R names.","text_prefix":false,"timestamp":1758680314,"version":"0.8.15"},"r-hh":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=HH","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/HH/HH_3.1-53.tar.gz","https://cran.r-project.org/src/contrib/HH_3.1-53.tar.gz"],"subdirs":["noarch"],"summary":"Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.","text_prefix":false,"timestamp":1643300585,"version":"3.1_53"},"r-hiclimr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/hsbadr/HiClimR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/HiClimR/HiClimR_2.2.1.tar.gz","https://cran.r-project.org/src/contrib/HiClimR_2.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file.","text_prefix":false,"timestamp":1609881563,"version":"2.2.1"},"r-hiddenmarkov":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.statsresearch.co.nz/dsh/sslib/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/HiddenMarkov/HiddenMarkov_1.8-14.tar.gz","https://cran.r-project.org/src/contrib/HiddenMarkov_1.8-14.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic \"HiddenMarkov\" for an introduction to the package, and \"Change Log\" for a list of recent changes. The algorithms are based of those of Walter Zucchini.","text_prefix":false,"timestamp":1533309633,"version":"1.8_14"},"r-hier.part":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=hier.part","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hier.part/hier.part_1.0-6.tar.gz","https://cran.r-project.org/src/contrib/hier.part_1.0-6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Partitioning of the independent and joint contributions of each variable in a multivariate data set, to a linear regression by hierarchical decomposition of goodness-of-fit measures of regressions using all subsets of predictors in the data set. (i.e., model (1), (2), ..., (N), (1,2), ..., (1,N), ..., (1,2,3,...,N)). A Z-score based estimate of the 'importance' of each predictor is provided by using a randomisation test.","text_prefix":false,"timestamp":1758565386,"version":"1.0_6"},"r-hierfstat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.r-project.org, http://github.com/jgx65/hierfstat","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hierfstat/hierfstat_0.5-11.tar.gz","https://cran.r-project.org/src/contrib/hierfstat_0.5-11.tar.gz"],"subdirs":["noarch"],"summary":"Allows the estimation of hierarchical F-statistics from haploid or diploid genetic data  with any numbers  of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956;  <DOI:10.2307/2411227>. Functions are also given to test via randomisations the significance of each F and variance components,   using the likelihood-ratio statistics G.","text_prefix":false,"timestamp":1637162305,"version":"0.5_11"},"r-highcharter":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://jkunst.com/highcharter","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/highcharter/highcharter_0.9.5.tar.gz","https://cran.r-project.org/src/contrib/highcharter_0.9.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A wrapper for the 'Highcharts' library including shortcut functions to plot R objects. 'Highcharts' <http://www.highcharts.com/> is a charting library offering numerous chart types with a simple configuration syntax.","text_prefix":false,"timestamp":1539922945,"version":"0.9.5"},"r-highlight":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/romainfrancois/highlight","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/highlight/highlight_0.5.2.tar.gz","https://cran.r-project.org/src/contrib/highlight_0.5.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Syntax highlighter for R code based  on the results of the R parser. Rendering in HTML and latex  markup. Custom Sweave driver performing syntax highlighting  of R code chunks.","text_prefix":false,"timestamp":1576316783,"version":"0.5.2"},"r-highr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/yihui/highr","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/highr/highr_0.12.tar.gz","https://cran.r-project.org/src/contrib/highr_0.12.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides syntax highlighting for R source code. Currently it supports LaTeX and HTML output. Source code of other languages is supported via Andre Simon's highlight package (<http://www.andre-simon.de>).","text_prefix":false,"timestamp":0,"version":"0.12"},"r-highs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://gitlab.com/roigrp/solver/highs","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/highs/highs_1.14.0-2.tar.gz","https://cran.r-project.org/src/contrib/highs_1.14.0-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64"],"summary":"R interface to 'HiGHS', an optimization solver for solving mixed integer optimization problems with quadratic or linear objective and linear constraints.","text_prefix":false,"timestamp":1784731840,"version":"1.14.0_2"},"r-hillr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/daijiang/hillR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hillR/hillR_0.5.2.tar.gz","https://cran.r-project.org/src/contrib/hillR_0.5.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Calculate taxonomic, functional and phylogenetic diversity measures through Hill Numbers proposed by Chao, Chiu and Jost (2014) <doi:10.1146/annurev-ecolsys-120213-091540>.","text_prefix":false,"timestamp":1758831351,"version":"0.5.2"},"r-histdat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"In some cases you will have data in a histogram format, where you have a vector of all possible observations, and a vector of how many times each observation appeared.  You could expand this into a single 1D vector, but this may not be advisable if the counts are extremely large. 'HistDat' allows for the calculation of summary statistics without the need for expanding your data.","home":"https://CRAN.R-project.org/package=HistDat","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/HistDat/HistDat_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/HistDat_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Summary Statistics for Histogram/Count Data","text_prefix":false,"timestamp":1758007605,"version":"0.2.0"},"r-histdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=HistData","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/HistData/HistData_1.1.1.tar.gz","https://cloud.r-project.org/src/contrib/HistData_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"The 'HistData' package provides a collection of small data sets that are interesting and important in the history of statistics and data visualization. The goal of the package is to make these available, both for instructional use and for historical research. Some of these present interesting challenges for graphics or analysis in R.","text_prefix":false,"timestamp":1791281498,"version":"1.1.1"},"r-hitandrun":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gertvv/hitandrun","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hitandrun/hitandrun_0.5-6.tar.gz","https://cran.r-project.org/src/contrib/hitandrun_0.5-6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The \"Hit and Run\" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the \"Shake and Bake\" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) <doi:10.1016/j.ejor.2012.08.026>. van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) <doi:10.1016/j.ejor.2014.06.036>.","text_prefix":false,"timestamp":1773524021,"version":"0.5_6"},"r-hive":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=hive","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hive/hive_0.2-2.tar.gz","https://cran.r-project.org/src/contrib/hive_0.2-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Hadoop InteractiVE facilitates distributed  computing via the MapReduce paradigm through R and Hadoop. 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Keywords: networks, food webs, linnet, systems biology, bioinformatics.","text_prefix":false,"timestamp":1591128708,"version":"0.4.0"},"r-hmeasure":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.hmeasure.net","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hmeasure/hmeasure_1.0-2.tar.gz","https://cran.r-project.org/src/contrib/hmeasure_1.0-2.tar.gz"],"subdirs":["noarch"],"summary":"Classification performance metrics that are derived from the ROC curve of a classifier. The package includes the H-measure performance metric as described in <http://link.springer.com/article/10.1007/s10994-009-5119-5>, which computes the minimum total misclassification cost, integrating over any uncertainty about the relative misclassification costs, as per a user-defined prior. 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In preprocessing stage, the nonparanormal(npn) transformation is applied to help relax the normality assumption. In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated by the lossy screening rule preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a computationally efficient approach, correlation thresholding graph estimation. Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso.","text_prefix":false,"timestamp":1550865129,"version":"2.0.1"},"r-humaniformat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ironholds/humaniformat/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/humaniformat/humaniformat_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/humaniformat_0.6.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Human names are complicated and nonstandard things. Humaniformat, which is based on Anthony Ettinger's 'humanparser' project (https://github.com/ chovy/humanparser) provides functions for parsing human names, making a best- guess attempt to distinguish sub-components such as prefixes, suffixes, middle names and salutations.","text_prefix":false,"timestamp":1757840635,"version":"0.6.0"},"r-hunspell":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/ropensci/hunspell#readme (devel) https://hunspell.github.io (upstream)","license":"GPL-2.0-only OR LGPL-2.1-only OR MPL-1.1","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hunspell/hunspell_3.0.6.tar.gz","https://cran.r-project.org/src/contrib/hunspell_3.0.6.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Low level spell checker and morphological analyzer based on the  famous 'hunspell' library <https://hunspell.github.io>. The package can analyze or check individual words as well as parse text, latex, html or xml documents. For a more user-friendly interface use the 'spelling' package which builds on this package to automate checking of files, documentation and vignettes in all common formats.","text_prefix":false,"timestamp":0,"version":"3.0.6"},"r-huxtable":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://hughjonesd.github.io/huxtable","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/huxtable/huxtable_6.0.2.tar.gz","https://cloud.r-project.org/src/contrib/huxtable_6.0.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Like 'xtable', creates styled tables. Export to HTML, LaTeX, 'Word',  'Excel', 'PowerPoint' and RTF. Simple, modern interface to manipulate  borders, size, position, captions, colours, text styles and number formatting. Table cells can span multiple rows and/or columns. Includes  a 'huxreg' function for creation of regression tables, and 'quick_*'  one-liners to print data to a new document.","text_prefix":false,"timestamp":1530861081,"version":"6.0.2"},"r-hwriter":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=hwriter","license":"LGPL-2.1-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hwriter/hwriter_1.3.2.1.tar.gz","https://cran.r-project.org/src/contrib/hwriter_1.3.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Easy-to-use and versatile functions to output R objects in HTML format","text_prefix":false,"timestamp":1539733217,"version":"1.3.2.1"},"r-hydrogof":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/hzambran/hydroGOF","home":"https://hzambran.github.io/hydroGOF/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hydroGOF/hydroGOF_0.7-0.tar.gz","https://cran.r-project.org/src/contrib/hydroGOF_0.7-0.tar.gz"],"subdirs":["noarch"],"summary":"S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.","text_prefix":false,"timestamp":1777799365,"version":"0.7_0"},"r-hydrotsm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/hzambran/hydroTSM","home":"https://github.com/hzambran/hydroTSM","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hydroTSM/hydroTSM_0.8-6.tar.gz","https://cran.r-project.org/src/contrib/hydroTSM_0.8-6.tar.gz"],"subdirs":["noarch"],"summary":"S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.","text_prefix":false,"timestamp":1777734582,"version":"0.8_6"},"r-hyper2":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/RobinHankin/hyper2","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hyper2/hyper2_3.2-3.tar.gz","https://cran.r-project.org/src/contrib/hyper2_3.2-3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A suite of routines for the hyperdirichlet distribution; supersedes the 'hyperdirichlet' package.","text_prefix":false,"timestamp":1637356869,"version":"3.2_3"},"r-hypergea":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=hypergea","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hypergea/hypergea_1.3.6.tar.gz","https://cran.r-project.org/src/contrib/hypergea_1.3.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Performs (exact) hypergeometric tests on IxJ and 2x2x2 contingency tables using parallelized C code.","text_prefix":false,"timestamp":1621769780,"version":"1.3.6"},"r-hypergeo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=hypergeo","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/hypergeo/hypergeo_1.2-14.tar.gz","https://cran.r-project.org/src/contrib/hypergeo_1.2-14.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The Gaussian hypergeometric function for complex numbers.","text_prefix":false,"timestamp":1536437762,"version":"1.2_14"},"r-hypetools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/rcapell/HYPEtools","home":"https://hypeweb.smhi.se/","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/HYPEtools/HYPEtools_1.6.8.tar.gz","https://cloud.r-project.org/src/contrib/HYPEtools_1.6.8.tar.gz"],"subdirs":["noarch"],"summary":"Work with model files (setup, input, output) from the hydrological catchment model HYPE: Streamlined file import and export, standard evaluation plot routines, diverse post-processing and aggregation routines for hydrological model analysis.","text_prefix":false,"timestamp":1665762976,"version":"1.6.8"},"r-ibd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ibd","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ibd/ibd_1.6.tar.gz","https://cran.r-project.org/src/contrib/ibd_1.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of several utility functions related to binary incomplete block designs. The package contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. The package also contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Package also allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.","text_prefix":false,"timestamp":0,"version":"1.6"},"r-ibreakdown":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://ModelOriented.github.io/iBreakDown/, https://github.com/ModelOriented/iBreakDown","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iBreakDown/iBreakDown_2.1.2.tar.gz","https://cran.r-project.org/src/contrib/iBreakDown_2.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Model agnostic tool for decomposition of predictions from black boxes. Supports additive attributions and attributions with interactions. The Break Down Table shows contributions of every variable to a final prediction. The Break Down Plot presents variable contributions in a concise graphical way. This package works for classification and regression models. It is an extension of the 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>, with new and faster strategies for orderings. It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) <arXiv:1903.11420> This package is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.","text_prefix":false,"timestamp":1588736059,"version":"2.1.2"},"r-ic.infer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://prof.bht-berlin.de/groemping/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ic.infer/ic.infer_1.1-8.tar.gz","https://cloud.r-project.org/src/contrib/ic.infer_1.1-8.tar.gz"],"subdirs":["noarch"],"summary":"Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.","text_prefix":false,"timestamp":1788422884,"version":"1.1_8"},"r-ic10trainingdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=iC10TrainingData","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iC10TrainingData/iC10TrainingData_2.0.1.tar.gz","https://cran.r-project.org/src/contrib/iC10TrainingData_2.0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.","text_prefix":false,"timestamp":0,"version":"2.0.1"},"r-ica":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ica","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ica/ica_1.0-3.tar.gz","https://cran.r-project.org/src/contrib/ica_1.0-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).","text_prefix":false,"timestamp":1540204622,"version":"1.0_3"},"r-icarus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=icarus","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/icarus/icarus_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/icarus_0.3.3.tar.gz"],"subdirs":["noarch"],"summary":"Provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to 'icarus'. In addition to calibration (with linear, raking and logit methods), 'icarus' features functions for calibration on tight bounds and penalized calibration.","text_prefix":false,"timestamp":1665762708,"version":"0.3.3"},"r-iccbeta":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tmsalab/iccbeta","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iccbeta/iccbeta_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/iccbeta_1.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>. This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.","text_prefix":false,"timestamp":1772798021,"version":"1.2.1"},"r-icebox":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/kapelner/ICEbox","license":"GPL-2.0-only OR GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ICEbox/ICEbox_1.2.tar.gz","https://cran.r-project.org/src/contrib/ICEbox_1.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.","text_prefix":false,"timestamp":1768244582,"version":"1.2"},"r-icellr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rezakj/iCellR","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iCellR/iCellR_1.7.0.tar.gz","https://cran.r-project.org/src/contrib/iCellR_1.7.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550>  and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.","text_prefix":false,"timestamp":1773522159,"version":"1.7.0"},"r-icenreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=icenReg","license":"LGPL-2.0-only OR LGPL-2.1-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/icenReg/icenReg_2.0.16.tar.gz","https://cran.r-project.org/src/contrib/icenReg_2.0.16.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data.","text_prefix":false,"timestamp":1758074301,"version":"2.0.16"},"r-icio":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Four global value chain (GVC) decompositions of gross exports from inter-country input-output tables are implemented. The Leontief decomposition derives the value added origin of exports by country and industry, as in Hummels, Ishii and Yi (2001) <doi:10.1016/S0022-1996(00)00093-3>. The Koopman, Wang and Wei (2014) <doi:10.1257/aer.104.2.459> decomposition splits country-level exports into 9 value added components, and the Wang, Wei and Zhu (2013) <doi:10.3386/w19677> decomposition splits bilateral exports into 16 value added components. The Borin and Mancini (2019) <doi:10.1596/1813-9450-8804> decomposition splits country-, sector- or bilateral-level exports into up to 13 value added and GVC components, and also provides a corrected version of the (biased) Koopman-Wang-Wei decomposition. It is the recommended method and reproduces the 'icio' command for 'Stata' described in Belotti, Borin and Mancini (2021) <doi:10.1177/1536867X211045573>.","dev_url":"https://github.com/SebKrantz/icio","doc_url":"https://sebkrantz.github.io/icio/","home":"https://sebkrantz.github.io/icio/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["linux-64","osx-64","win-64"],"summary":"Global Value Chain Decomposition of Inter-Country Input-Output Tables","text_prefix":false,"timestamp":1789737175,"version":"1.0.0"},"r-icluster":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=iCluster","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iCluster/iCluster_2.1.0.tar.gz","https://cran.r-project.org/src/contrib/iCluster_2.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Integrative clustering of multiple genomic data types using a joint latent variable model.","text_prefix":false,"timestamp":1539472282,"version":"2.1.0"},"r-ics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ICS","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ICS/ICS_1.4-2.tar.gz","https://cran.r-project.org/src/contrib/ICS_1.4-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009, <doi:10.1111/j.1467-9868.2009.00706.x>) and Oja, Sirkia and Eriksson's (AJS, 2006, <http://www.ajs.or.at/index.php/ajs/article/view/vol35,%20no2%263%20-%207>) method of two different scatter matrices to obtain an invariant coordinate system or independent components, depending on the underlying assumptions.","text_prefix":false,"timestamp":1539732901,"version":"1.4_2"},"r-icsnp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ICSNP","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ICSNP/ICSNP_1.1-3.tar.gz","https://cran.r-project.org/src/contrib/ICSNP_1.1-3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.","text_prefix":false,"timestamp":1542960850,"version":"1.1_3"},"r-ideafilter":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/Biogen-Inc/IDEAFilter","home":"https://biogen-inc.github.io/IDEAFilter/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/IDEAFilter/IDEAFilter_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/IDEAFilter_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"When added to an existing shiny app, users may subset any developer-chosen R data.frame on the fly. That is, users are empowered to slice & dice data by applying multiple (order specific) filters using the AND (&) operator between each, and getting real-time updates on the number of rows effected/available along the way. Thus, any downstream processes that leverage this data source (like tables, plots, or statistical procedures) will re-render after new filters are applied. The shiny module\u2019s user interface has a 'minimalist' aesthetic so that the focus can be on the data & other visuals. In addition to returning a reactive (filtered) data.frame, 'IDEAFilter' as also returns 'dplyr' filter statements used to actually slice the data.","text_prefix":false,"timestamp":1758098400,"version":"0.2.1"},"r-idefix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/traets/idefix","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/idefix/idefix_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/idefix_1.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.","text_prefix":false,"timestamp":1758123813,"version":"1.1.0"},"r-idm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=idm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/idm/idm_1.8.3.tar.gz","https://cran.r-project.org/src/contrib/idm_1.8.3.tar.gz"],"subdirs":["noarch"],"summary":"Incremental Multiple Correspondence Analysis and Principal Component Analysis.","text_prefix":false,"timestamp":1758715684,"version":"1.8.3"},"r-idpmisc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=IDPmisc","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/IDPmisc/IDPmisc_1.1.21.tar.gz","https://cran.r-project.org/src/contrib/IDPmisc_1.1.21.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The IDPmisc package contains different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region.  In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra.","text_prefix":false,"timestamp":1563692188,"version":"1.1.21"},"r-idr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=idr","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/idr/idr_1.3.tar.gz","https://cran.r-project.org/src/contrib/idr_1.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This is a package for estimating the copula mixture model and plotting correspondence curves in \"Measuring reproducibility of high-throughput experiments\" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel","text_prefix":false,"timestamp":1539968472,"version":"1.3"},"r-ids":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/richfitz/ids","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ids/ids_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/ids_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Generate random or human readable and pronounceable identifiers.","text_prefix":false,"timestamp":1758407699,"version":"1.0.1"},"r-ieugwasr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/MRCIEU/ieugwasr","home":"https://mrcieu.github.io/ieugwasr/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ieugwasr/ieugwasr_1.2.0.tar.gz","https://cloud.r-project.org/src/contrib/ieugwasr_1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Interface to the 'OpenGWAS' database API <https://gwas-api.mrcieu.ac.uk/>. Includes a wrapper to make generic calls to the API, plus convenience functions for specific queries.","text_prefix":false,"timestamp":1791402531,"version":"1.2.0"},"r-ife":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ife","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ife/ife_0.2.5.tar.gz","https://cran.r-project.org/src/contrib/ife_0.2.5.tar.gz"],"subdirs":["noarch"],"summary":"Implements an S7 class for estimates based on influence functions, with forward mode automatic differentiation defined for standard arithmetic operations.","text_prefix":false,"timestamp":1782979047,"version":"0.2.5"},"r-ifultools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ifultools","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ifultools/ifultools_2.0-26.tar.gz","https://cran.r-project.org/src/contrib/ifultools_2.0-26.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Insightful Research Tools.","text_prefix":false,"timestamp":1606396182,"version":"2.0_26"},"r-igraph":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/igraph/rigraph","home":"https://r.igraph.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/igraph/igraph_2.3.4.tar.gz","https://cloud.r-project.org/src/contrib/igraph_2.3.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.","text_prefix":true,"timestamp":1665518751,"version":"2.3.4"},"r-iheatmapr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/iheatmapr/ (website) https://github.com/ropensci/iheatmapr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iheatmapr/iheatmapr_0.7.1.tar.gz","https://cran.r-project.org/src/contrib/iheatmapr_0.7.1.tar.gz"],"subdirs":["noarch"],"summary":"Make complex, interactive heatmaps. 'iheatmapr' includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.","text_prefix":false,"timestamp":1758587441,"version":"0.7.1"},"r-ijtiff":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/ijtiff/, https://github.com/ropensci/ijtiff","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ijtiff/ijtiff_3.2.0.tar.gz","https://cran.r-project.org/src/contrib/ijtiff_3.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"General purpose TIFF file I/O for R users.  Currently the only such package with read and write support for TIFF files with floating point (real-numbered) pixels, and the only package that can correctly import TIFF files that were saved from 'ImageJ' and write TIFF files than can be correctly read by 'ImageJ' <https://imagej.net/ij/>. Also supports text image I/O.","text_prefix":false,"timestamp":1773526883,"version":"3.2.0"},"r-imager":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/dahtah/imager","doc_url":"https://dahtah.github.io/imager/imager.html","home":"https://dahtah.github.io/imager","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/imager/imager_1.0.8.tar.gz","https://cran.r-project.org/src/contrib/imager_1.0.8.tar.gz"],"subdirs":["linux-64"],"summary":"Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.","text_prefix":false,"timestamp":1623320219,"version":"1.0.8"},"r-imbalance":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://github.com/ncordon/imbalance","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/imbalance/imbalance_1.0.2.1.tar.gz","https://cran.r-project.org/src/contrib/imbalance_1.0.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Class imbalance usually damages the performance of classifiers. Thus, it is important to treat data before applying a classifier algorithm. This package includes recent resampling algorithms in the literature: (Barua et al. 2014) <doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>, (Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014) <doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014) <doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to perform oversampling.","text_prefix":false,"timestamp":1576656997,"version":"1.0.2.1"},"r-imgur":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/leeper/imguR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/imguR/imguR_1.0.3.tar.gz","https://cran.r-project.org/src/contrib/imguR_1.0.3.tar.gz"],"subdirs":["noarch"],"summary":"A complete API client for the image hosting service Imgur.com, including the an imgur graphics device, enabling the easy upload and sharing of plots.","text_prefix":false,"timestamp":1758695434,"version":"1.0.3"},"r-iml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/christophM/iml","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iml/iml_0.11.4.tar.gz","https://cran.r-project.org/src/contrib/iml_0.11.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Interpretability methods to analyze the behavior and predictions of any machine learning model.  Implemented methods are: Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>, accumulated local effects plots described by Apley (2018) <arXiv:1612.08468>, partial dependence plots described by Friedman (2001) <http://www.jstor.org/stable/2699986>, individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>, the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.","text_prefix":false,"timestamp":1543245612,"version":"0.11.4"},"r-immunarch":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/immunomind/immunarch","home":"https://immunarch.com/","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/immunarch/immunarch_0.9.1.tar.gz","https://cran.r-project.org/src/contrib/immunarch_0.9.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq) and single-cell sequencing (scRNAseq). It implements most of the widely used AIRR analysis methods, such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor databases and clonotype tracking in vaccination and cancer studies. A successor to our previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.","text_prefix":false,"timestamp":1635527811,"version":"0.9.1"},"r-immundata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/immunomind/immundata","home":"https://immunomind.github.io/docs/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/immundata/immundata_0.1.0.tar.gz","https://cloud.r-project.org/src/contrib/immundata_0.1.0.tar.gz"],"subdirs":["linux-64","noarch"],"summary":"Provides a unified data layer for single-cell, spatial and bulk T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data.","text_prefix":false,"timestamp":1788620934,"version":"0.1.0"},"r-imp4p":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=imp4p","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/imp4p/imp4p_1.3.tar.gz","https://cran.r-project.org/src/contrib/imp4p_1.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.","text_prefix":false,"timestamp":1595953002,"version":"1.3"},"r-import":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/smbache/import","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/import/import_1.3.4.tar.gz","https://cran.r-project.org/src/contrib/import_1.3.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This is an alternative mechanism for importing objects from packages. The syntax allows for importing multiple objects from a package with a single command in an expressive way. The import package bridges some of the gap between using library (or require) and direct (single-object) imports. Furthermore the imported objects are not placed in the current environment. It is also possible to import objects from stand-alone .R files. For more information, refer to the package vignette.","text_prefix":false,"timestamp":1621747772,"version":"1.3.4"},"r-imputets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SteffenMoritz/imputeTS, https://steffenmoritz.github.io/imputeTS/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/imputeTS/imputeTS_3.4.tar.gz","https://cran.r-project.org/src/contrib/imputeTS_3.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.","text_prefix":false,"timestamp":1783190396,"version":"3.4"},"r-inaparc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=inaparc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/inaparc/inaparc_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/inaparc_1.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting to partition the data sets. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the data elements, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'.","text_prefix":false,"timestamp":1621892763,"version":"1.2.1"},"r-incadata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cancercentrum.bitbucket.io/incadata","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/incadata/incadata_0.9.1.tar.gz","https://cran.r-project.org/src/contrib/incadata_0.9.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Handle data in formats used by cancer centers in Sweden, both from 'INCA'  (<https://rcc.incanet.se>) and by the older register platform 'Rockan'. All variables are coerced to suitable classes based on their format.  Dates (from various formats such as with missing month or day, with or  without century prefix or with just a week number) are all recognized as dates and coerced to the ISO 8601 standard (Y-m-d). Boolean variables (internally stored either as 0/1 or \"True\"/\"False\"/blanks  when exported) are coerced to logical.  Variable names ending in '_Beskrivning' and '_Varde' will be character,  and 'PERSNR' will be coerced (if possible) to a valid personal identification  number 'pin' (by the 'sweidnumbr' package). The package also allow the user to interactively choose if a variable should  be coerced into a potential format even though not all of its values might  conform to the recognized pattern. It also contain a caching mechanism in order to temporarily store data sets  with its newly decided formats in order to not rerun the identification  process each time.  The package also include a mechanism to aid the documentation process  connected to projects build on data from 'INCA'. From version 0.7, some general help functions are also included,  as previously found in the 'rccmisc' package.","text_prefix":false,"timestamp":1537829448,"version":"0.9.1"},"r-inctools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SACEMA/inctools","home":"http://www.incidence-estimation.org/page/inctools","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/inctools/inctools_1.0.15.tar.gz","https://cran.r-project.org/src/contrib/inctools_1.0.15.tar.gz"],"subdirs":["noarch"],"summary":"Tools for estimating incidence from biomarker data in cross- sectional surveys, and for calibrating tests for recent infection. Implements and extends the method of Kassanjee et al. (2012) <doi:10.1097/EDE.0b013e3182576c07>.","text_prefix":false,"timestamp":1758260143,"version":"1.0.15"},"r-indicspecies":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://emf-creaf.github.io/indicspecies/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/indicspecies/indicspecies_1.8.0.tar.gz","https://cran.r-project.org/src/contrib/indicspecies_1.8.0.tar.gz"],"subdirs":["noarch"],"summary":"Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].","text_prefix":false,"timestamp":1758618594,"version":"1.8.0"},"r-ineq":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ineq","license":"GPL-2 | GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ineq/ineq_0.2-13.tar.gz","https://cran.r-project.org/src/contrib/ineq_0.2-13.tar.gz"],"subdirs":["noarch"],"summary":"Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).","text_prefix":false,"timestamp":1758475632,"version":"0.2_13"},"r-inext":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://chao.stat.nthu.edu.tw/wordpress/software_download/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iNEXT/iNEXT_3.0.2.tar.gz","https://cran.r-project.org/src/contrib/iNEXT_3.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation of species diversity (Hill numbers) for individual-based (abundance) data or sampling-unit- based (incidence) data. (Hsieh, Ma and Chao 2014) <doi: 10.1111/2041-210X.12613>.","text_prefix":false,"timestamp":1622076518,"version":"3.0.2"},"r-infer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tidymodels/infer","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/infer/infer_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/infer_1.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.","text_prefix":false,"timestamp":1536501143,"version":"1.1.0"},"r-inflection":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=inflection","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/inflection/inflection_1.3.7.tar.gz","https://cran.r-project.org/src/contrib/inflection_1.3.7.tar.gz"],"subdirs":["noarch"],"summary":"Implementation of methods Extremum Surface Estimator (ESE) and Extremum Distance Estimator (EDE) to identify the inflection point of a curve . 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Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's SNAP library), implementations of Burt's constraint and effective network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's bridging metric. 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Semaphores act as counters. Message queues move text strings from one process to another. All these interprocess communication (IPC) tools can optionally block with or without a timeout. 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The ratio between the different penalty parameters can be chosen by cross-validation.","text_prefix":false,"timestamp":1563662632,"version":"1.1"},"r-ipmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://indrajeetpatil.github.io/ipmisc/, https://github.com/IndrajeetPatil/ipmisc","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ipmisc/ipmisc_6.0.2.tar.gz","https://cran.r-project.org/src/contrib/ipmisc_6.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides functions needed for data cleaning and formatting and forms data cleaning and wrangling backend for the following packages: 'ggstatsplot', 'groupedstats', 'pairwiseComparisons', and 'statsExpressions'.","text_prefix":false,"timestamp":1588581166,"version":"6.0.2"},"r-ipred":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ipred","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ipred/ipred_0.9-15.tar.gz","https://cran.r-project.org/src/contrib/ipred_0.9-15.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Improved predictive models by indirect classification and bagging for classification, regression and survival problems  as well as resampling based estimators of prediction error.","text_prefix":false,"timestamp":1631712380,"version":"0.9_15"},"r-ips":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/heibl/ips","home":"https://CRAN.R-project.org/package=ips","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ips/ips_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/ips_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states.","text_prefix":false,"timestamp":1784102175,"version":"0.1.1"},"r-iptools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/hrbrmstr/iptools","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iptools/iptools_0.7.2.tar.gz","https://cran.r-project.org/src/contrib/iptools_0.7.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A toolkit for manipulating, validating and testing 'IP' addresses and ranges, along with datasets relating to 'IP' addresses. Tools are also provided to map 'IPv4' blocks to country codes. While it primarily has support for the 'IPv4' address space, more extensive 'IPv6' support is intended.","text_prefix":false,"timestamp":1622096000,"version":"0.7.2"},"r-iq":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tvpham/iq","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iq/iq_2.0.1.tar.gz","https://cran.r-project.org/src/contrib/iq_2.0.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An implementation of the MaxLFQ algorithm by Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive pipeline for processing proteomics data in data-independent acquisition mode (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>; Pham et al. 2026 <doi:10.1021/acs.jproteome.5c01038>). It offers additional options for protein quantification using the N most intense fragment ions, using all fragment ions, the median polish algorithm by Tukey (1977, ISBN:0201076160), and a robust linear model. In general, the tool can be used to integrate multiple proportional observations into a single quantitative value.","text_prefix":false,"timestamp":1784624027,"version":"2.0.1"},"r-irace":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/MLopez-Ibanez/irace","home":"http://iridia.ulb.ac.be/irace","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/irace/irace_4.5.tar.gz","https://cloud.r-project.org/src/contrib/irace_4.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Iterated race is an extension of the Iterated F-race method for the automatic configuration of optimization algorithms, that is, (offline) tuning their parameters by finding the most appropriate settings given a set of instances of an optimization problem.","text_prefix":false,"timestamp":1585577969,"version":"4.5"},"r-irdisplay":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/IRkernel/IRdisplay/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/IRkernel/IRdisplay/archive/1.1.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook'). Designed to be used from a running 'IRkernel'.","text_prefix":false,"timestamp":0,"version":"1.1"},"r-irkernel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=IRkernel","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/IRkernel/IRkernel_1.3.2.tar.gz","https://cran.r-project.org/src/contrib/IRkernel_1.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The R kernel for the 'Jupyter' environment executes R code which the front-end ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.","text_prefix":false,"timestamp":0,"version":"1.3.2"},"r-irlba":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/bwlewis/irlba","home":"https://CRAN.R-project.org/package=irlba","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/irlba/irlba_2.4.1.tar.gz","https://cloud.r-project.org/src/contrib/irlba_2.4.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fast and memory efficient methods for truncated singular and eigenvalue decompositions and principal component analysis of large sparse or dense matrices.","text_prefix":false,"timestamp":1638552619,"version":"2.4.1"},"r-irr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.r-project.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/irr/irr_0.85.tar.gz","https://cran.r-project.org/src/contrib/irr_0.85.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data: ICC, Finn-Coefficient, Robinson'A, Kendall's W, Cohen's Kappa, ...","text_prefix":false,"timestamp":1528738661,"version":"0.85"},"r-isa2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gaborcsardi/ISA","license":"CC-BY-NC-SA-4.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/isa2/isa2_0.3.6.tar.gz","https://cran.r-project.org/src/contrib/isa2_0.3.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The ISA is a biclustering algorithm that finds modules  in an input matrix. A module or bicluster is a block of the reordered input matrix.","text_prefix":false,"timestamp":1665763362,"version":"0.3.6"},"r-isingfit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=IsingFit","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/IsingFit/IsingFit_0.4.tar.gz","https://cran.r-project.org/src/contrib/IsingFit_0.4.tar.gz"],"subdirs":["noarch"],"summary":"This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.","text_prefix":false,"timestamp":1758735417,"version":"0.4"},"r-isingsampler":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"github.com/SachaEpskamp/IsingSampler","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/IsingSampler/IsingSampler_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/IsingSampler_0.5.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.","text_prefix":false,"timestamp":1784213881,"version":"0.5.0"},"r-islr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.statlearning.com","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ISLR/ISLR_1.4.tar.gz","https://cran.r-project.org/src/contrib/ISLR_1.4.tar.gz"],"subdirs":["noarch"],"summary":"We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.","text_prefix":false,"timestamp":1758194773,"version":"1.4"},"r-islr2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://cran.r-project.org/package=ISLR2","home":"https://www.statlearning.com","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ISLR2/ISLR2_1.3-2.tar.gz","https://cran.r-project.org/src/contrib/ISLR2_1.3-2.tar.gz"],"subdirs":["noarch"],"summary":"We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.","text_prefix":false,"timestamp":1666120376,"version":"1.3_2"},"r-ismev":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.ral.ucar.edu/~ericg/softextreme.php","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ismev/ismev_1.43.tar.gz","https://cran.r-project.org/src/contrib/ismev_1.43.tar.gz"],"subdirs":["noarch"],"summary":"Functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values' by Stuart Coles. The functions may be divided into the following  groups; maxima/minima, order statistics, peaks over thresholds and point processes.","text_prefix":false,"timestamp":1758554230,"version":"1.43"},"r-iso":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.stat.auckland.ac.nz/~rolf/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Iso/Iso_0.0-21.tar.gz","https://cran.r-project.org/src/contrib/Iso_0.0-21.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Linear order and unimodal order (univariate) isotonic regression; bivariate isotonic regression with linear order on both variables.","text_prefix":false,"timestamp":1544526496,"version":"0.0_21"},"r-isoband":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-lib/isoband","doc_url":"https://isoband.r-lib.org/reference/index.html","home":"https://isoband.r-lib.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/isoband/isoband_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/isoband_0.3.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.","text_prefix":false,"timestamp":1592686724,"version":"0.3.0"},"r-isocodes":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ISOcodes","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ISOcodes/ISOcodes_2026.03.28.tar.gz","https://cran.r-project.org/src/contrib/ISOcodes_2026.03.28.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"ISO language, territory, currency, script and character codes. Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217 currency codes, ISO 15924 script codes, and the ISO 8859 character codes as well as the UN M.49 area codes.","text_prefix":false,"timestamp":1536471669,"version":"2026.03.28"},"r-isotone":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://r-forge.r-project.org/projects/psychor/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/isotone/isotone_1.1-2.tar.gz","https://cran.r-project.org/src/contrib/isotone_1.1-2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Contains two main functions: one for solving general isotone regression problems using the pool-adjacent-violators algorithm (PAVA); another one provides a framework for active set methods for isotone optimization problems with arbitrary order restrictions. Various types of loss functions are prespecified.","text_prefix":false,"timestamp":1668069629,"version":"1.1_2"},"r-isotree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/david-cortes/isotree","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/isotree/isotree_0.6.1-5.tar.gz","https://cran.r-project.org/src/contrib/isotree_0.6.1-5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), fair-cut forest (Cortes (2021) <arXiv:2110:13402>), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <arXiv:1910.12362>), isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>), and imputation of missing values (Cortes (2019) <arXiv:1911.06646>), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) <arXiv:2111.11639>). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.","text_prefix":false,"timestamp":1667638489,"version":"0.6.1_5"},"r-iswr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ISwR","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ISwR/ISwR_2.0-11.tar.gz","https://cran.r-project.org/src/contrib/ISwR_2.0-11.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Data sets and scripts for text examples and exercises in P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.","text_prefix":false,"timestamp":1538246384,"version":"2.0_11"},"r-iterators":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=iterators","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iterators/iterators_1.0.14.tar.gz","https://cran.r-project.org/src/contrib/iterators_1.0.14.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Support for iterators, which allow a programmer to traverse through all the elements of a vector, list, or other collection of data.","text_prefix":false,"timestamp":0,"version":"1.0.14"},"r-iterpc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/randy3k/iterpc","home":"https://randy3k.github.io/iterpc","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/iterpc/iterpc_0.4.2.tar.gz","https://cran.r-project.org/src/contrib/iterpc_0.4.2.tar.gz"],"subdirs":["noarch"],"summary":"Iterator for generating permutations and combinations. They can be either drawn with or without replacement, or with distinct/ non-distinct items (multiset). The generated sequences are in lexicographical order (dictionary order). The algorithms to generate permutations and combinations are memory efficient. These iterative algorithms enable users to process all sequences without putting all results in the memory at the same time. The algorithms are written in C/C++ for faster performance. Note: 'iterpc' is no longer being maintained. Users are recommended to switch to 'arrangements'.","text_prefix":false,"timestamp":1758099607,"version":"0.4.2"},"r-itertools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=itertools","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/itertools/itertools_0.1-3.tar.gz","https://cran.r-project.org/src/contrib/itertools_0.1-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Various tools for creating iterators, many patterned after functions in the Python itertools module, and others patterned after functions in the 'snow' package.","text_prefix":false,"timestamp":1539397455,"version":"0.1_3"},"r-ivreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://zeileis.github.io/ivreg/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ivreg/ivreg_0.6-8.tar.gz","https://cran.r-project.org/src/contrib/ivreg_0.6-8.tar.gz"],"subdirs":["noarch"],"summary":"Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.","text_prefix":false,"timestamp":1667252537,"version":"0.6_8"},"r-ivs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/DavisVaughan/ivs, https://davisvaughan.github.io/ivs/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ivs/ivs_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/ivs_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a library for generic interval manipulations using a new interval vector class. Capabilities include: locating various kinds of relationships between two interval vectors, merging overlaps within a single interval vector, splitting an interval vector on its overlapping endpoints, and applying set theoretical operations on interval vectors. Many of the operations in this package were inspired by James Allen's interval algebra, Allen (1983) <doi:10.1145/182.358434>.","text_prefix":false,"timestamp":1665761956,"version":"0.2.0"},"r-jade":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=JADE","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/JADE/JADE_2.0-4.tar.gz","https://cran.r-project.org/src/contrib/JADE_2.0-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.","text_prefix":false,"timestamp":1568021916,"version":"2.0_4"},"r-janeaustenr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/juliasilge/janeaustenr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/janeaustenr/janeaustenr_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/janeaustenr_1.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Full texts for Jane Austen's 6 completed novels, ready for text analysis. These novels are \"Sense and Sensibility\", \"Pride and Prejudice\", \"Mansfield Park\", \"Emma\", \"Northanger Abbey\", and \"Persuasion\".","text_prefix":false,"timestamp":0,"version":"1.0.0"},"r-janitor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sfirke/janitor","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/janitor/janitor_2.2.1.tar.gz","https://cran.r-project.org/src/contrib/janitor_2.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and isolate duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel. 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First, the package makes it easy for an auditor to plan a statistical sample, select the sample from the population, and evaluate the misstatement in the sample compliant with international auditing standards. Next to classical audit sampling methodology, the package implements Bayesian equivalents of these methods whose statistical underpinnings are described in Derks et al. (2021) <doi:10.1111/ijau.12240>, Derks et al. (2021) <doi:10.31234/osf.io/kzqp5>, and Derks et al. (2022) <doi:10.31234/osf.io/8nf3e>. Second, the package provides statistical methods for auditing data, including (Bayesian) tests of digit distributions and tests for repeated values.","text_prefix":false,"timestamp":1784900737,"version":"0.7.4"},"r-jinjar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://davidchall.github.io/jinjar/, https://github.com/davidchall/jinjar","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/jinjar/jinjar_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/jinjar_0.3.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Template engine powered by the 'inja' C++ library. Users write a template document, using syntax inspired by the 'Jinja' Python package, and then render the final document by passing data from R. 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This package is also useable from the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more information).","text_prefix":false,"timestamp":1538300199,"version":"2.8.0"},"r-jmvcore":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.jamovi.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/jmvcore/jmvcore_2.7.38.tar.gz","https://cran.r-project.org/src/contrib/jmvcore_2.7.38.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A framework for creating rich interactive analyses for the jamovi platform (see <https://www.jamovi.org> for more information).","text_prefix":false,"timestamp":1538190853,"version":"2.7.38"},"r-jobqueue":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/cmmr/jobqueue","home":"https://cmmr.github.io/jobqueue/index.html","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/jobqueue/jobqueue_1.7.0.tar.gz","https://cran.r-project.org/src/contrib/jobqueue_1.7.0.tar.gz"],"subdirs":["noarch"],"summary":"Takes an R expression and returns a Job object with a $stop() method which can be called to terminate the background job. 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The report appears in the 'RStudio' viewer pane.","text_prefix":false,"timestamp":1758386307,"version":"0.1.0"},"r-jskm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/jinseob2kim/jskm","home":"https://jinseob2kim.github.io/jskm/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/jskm/jskm_0.6.3.tar.gz","https://cran.r-project.org/src/contrib/jskm_0.6.3.tar.gz"],"subdirs":["noarch"],"summary":"The function 'jskm()' creates publication quality Kaplan-Meier plot with at risk tables below. 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It is primarily intended for web-based programming environments, interoperable web services and for  storing linked data in JSON-based databases. This package provides bindings to the  JavaScript library for converting, expanding and compacting JSON-LD documents.","text_prefix":false,"timestamp":1588570519,"version":"2.2.1"},"r-jsonlite":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://arxiv.org/abs/1403.2805, https://www.opencpu.org/posts/jsonlite-a-smarter-json-encoder","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/jsonlite/jsonlite_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/jsonlite_2.0.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A fast JSON parser and generator optimized for statistical data and the web. 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The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.","text_prefix":false,"timestamp":1666355666,"version":"2.0.0"},"r-jsonvalidate":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/jsonvalidate","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/jsonvalidate/jsonvalidate_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/jsonvalidate_1.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Uses the node library 'is-my-json-valid' or 'ajv' to validate 'JSON' against a 'JSON' schema.  Drafts 04, 06 and 07 of 'JSON' schema are supported.","text_prefix":false,"timestamp":1634913045,"version":"1.5.0"},"r-jtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://jtools.jacob-long.com","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/jtools/jtools_2.3.1.tar.gz","https://cran.r-project.org/src/contrib/jtools_2.3.1.tar.gz"],"subdirs":["noarch"],"summary":"This is a collection of tools that the author (Jacob) has written for the purpose of more efficiently understanding and sharing the results of (primarily) regression analyses. There are also a number of miscellaneous functions for statistical and programming purposes. Just about everything supports models from the survey package.","text_prefix":false,"timestamp":1636010218,"version":"2.3.1"},"r-juicyjuice":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rich-iannone/juicyjuice","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/juicyjuice/juicyjuice_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/juicyjuice_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"There are occasions where you need a piece of HTML with integrated styles. A prime example of this is HTML email. This transformation involves moving the CSS and associated formatting instructions from the style block in the head of your document into the body of the HTML. Many prominent email clients require integrated styles in HTML email; otherwise a received HTML email will be displayed without any styling. This package will quickly and precisely perform these CSS transformations when given HTML text and it does so by using the JavaScript 'juice' library.","text_prefix":false,"timestamp":1758511450,"version":"0.1.0"},"r-juliacall":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Non-Contradiction/JuliaCall","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/JuliaCall/JuliaCall_0.17.6.tar.gz","https://cran.r-project.org/src/contrib/JuliaCall_0.17.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides an R interface to 'Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see <https://julialang.org/> for more information. It provides a high-level interface as well as a low-level interface. 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These colours make it easier to create relatively accessible and colour-blind safe visualisation.","text_prefix":false,"timestamp":1783926966,"version":"0.1.2"},"r-juniperkernel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/JuniperKernel/JuniperKernel","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/JuniperKernel/JuniperKernel_1.4.1.0.tar.gz","https://cran.r-project.org/src/contrib/JuniperKernel_1.4.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides a full implementation of the 'Jupyter' <http://jupyter.org/> messaging protocol in C++ by leveraging 'Rcpp' and 'Xeus' <https://github.com/QuantStack/xeus>. 'Jupyter' supplies an interactive computing environment and a messaging protocol defined over 'ZeroMQ' for multiple programming languages. This package implements the 'Jupyter' kernel interface so that 'R' is exposed to this interactive computing environment. 'ZeroMQ' functionality is provided by the 'pbdZMQ' package. 'Xeus' is a C++ library that facilitates the implementation of kernels for 'Jupyter'. Additionally, 'Xeus' provides an interface to libraries that exist in the 'Jupyter' ecosystem for building widgets, plotting, and more <https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92>. 'JuniperKernel' uses 'Xeus' as a library for the 'Jupyter' messaging protocol.","text_prefix":false,"timestamp":1758680168,"version":"1.4.1.0"},"r-justifyalpha":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=JustifyAlpha","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/JustifyAlpha/JustifyAlpha_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/JustifyAlpha_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. 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See the package URL for details.","text_prefix":false,"timestamp":1540987100,"version":"1.0.12"},"r-knitr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://yihui.org/knitr/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/knitr/knitr_1.52.tar.gz","https://cloud.r-project.org/src/contrib/knitr_1.52.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.","text_prefix":false,"timestamp":0,"version":"1.52"},"r-knitrbootstrap":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jimhester/knitrBootstrap#readme","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/knitrBootstrap/knitrBootstrap_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/knitrBootstrap_1.0.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A framework to create Bootstrap <http://getbootstrap.com/> HTML reports from 'knitr' 'rmarkdown'.","text_prefix":false,"timestamp":0,"version":"1.0.4"},"r-knn.covertree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/flying-sheep/knn.covertree","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/knn.covertree/knn.covertree_1.1.tar.gz","https://cran.r-project.org/src/contrib/knn.covertree_1.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix. The implementation is based on cover trees introduced by Alina Beygelzimer, Sham Kakade, and John Langford (2006) <doi:10.1145/1143844.1143857>.","text_prefix":false,"timestamp":1765200977,"version":"1.1"},"r-knnmi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=knnmi","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/knnmi/knnmi_1.0.tar.gz","https://cran.r-project.org/src/contrib/knnmi_1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This is a 'C++' mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) <doi:10.1103/PhysRevE.69.066138>, BC Ross (2014)<doi:10.1371/journal.pone.0087357>, and A. Tsimpiris (2012) <doi:10.1016/j.eswa.2012.05.014>, respectively.","text_prefix":false,"timestamp":1773519414,"version":"1.0"},"r-knockoff":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://web.stanford.edu/group/candes/knockoffs/index.html","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/knockoff/knockoff_0.3.6.tar.gz","https://cran.r-project.org/src/contrib/knockoff_0.3.6.tar.gz"],"subdirs":["noarch"],"summary":"The knockoff filter is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. For more information, see the website below and the accompanying paper: Candes et al., \"Panning for gold: model-X knockoffs for high-dimensional controlled variable selection\", J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.","text_prefix":false,"timestamp":1758659655,"version":"0.3.6"},"r-kohonen":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=kohonen","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/kohonen/kohonen_3.0.13.tar.gz","https://cran.r-project.org/src/contrib/kohonen_3.0.13.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.","text_prefix":false,"timestamp":1665276067,"version":"3.0.13"},"r-korpus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://reaktanz.de/?c=hacking&s=koRpus","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/koRpus/koRpus_0.13-9.tar.gz","https://cran.r-project.org/src/contrib/koRpus_0.13-9.tar.gz"],"subdirs":["noarch"],"summary":"A set of tools to analyze texts. Includes, amongst others, functions for automatic language detection, hyphenation, several indices of lexical diversity (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability (e.g., Flesch, SMOG, LIX, Dale-Chall). Basic import functions for language corpora are also provided, to enable frequency analyses (supports Celex and Leipzig Corpora Collection file formats) and measures like tf-idf. Note: For full functionality a local installation of TreeTagger is recommended. It is also recommended to not load this package directly, but by loading one of the available language support packages from the 'l10n' repository <https://undocumeantit.github.io/repos/l10n>. 'koRpus' also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. To make full use of this feature, please install RKWard from <https://rkward.kde.org> (plugins are detected automatically). Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).","text_prefix":false,"timestamp":1607730408,"version":"0.13_9"},"r-korpus.lang.en":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://reaktanz.de/?c=hacking&s=koRpus","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/koRpus.lang.en/koRpus.lang.en_0.1-4.tar.gz","https://cran.r-project.org/src/contrib/koRpus.lang.en_0.1-4.tar.gz"],"subdirs":["noarch"],"summary":"Adds support for the English language to the 'koRpus' package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).","text_prefix":false,"timestamp":1588605396,"version":"0.1_4"},"r-kpeaks":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=kpeaks","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/kpeaks/kpeaks_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/kpeaks_1.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary,  the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.","text_prefix":false,"timestamp":0,"version":"1.1.0"},"r-kpmt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=kpmt","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/kpmt/kpmt_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/kpmt_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Functions that implement the known population median test.","text_prefix":false,"timestamp":1758559160,"version":"0.1.0"},"r-kriging":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=kriging","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/kriging/kriging_1.2.tar.gz","https://cran.r-project.org/src/contrib/kriging_1.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Simple and highly optimized ordinary kriging algorithm to plot geographical data","text_prefix":false,"timestamp":1640921259,"version":"1.2"},"r-ks":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.mvstat.net/mvksa","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ks/ks_1.15.3.tar.gz","https://cran.r-project.org/src/contrib/ks_1.15.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Kernel smoothers for univariate and multivariate data, including densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. 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Except for  Steel's test and the JT test it also combines these tests across several blocks of  samples.  Also analyzed are 2 x t contingency tables and their blocked combinations  using the Kruskal-Wallis criterion.  Steel's test is inverted to provide simultaneous  confidence bounds for shift parameters.  A plotting function compares tail probabilities obtained under asymptotic approximation with those obtained via simulation or exact  calculations.","text_prefix":false,"timestamp":1545941243,"version":"1.2_12"},"r-kutils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=kutils","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/kutils/kutils_1.73.tar.gz","https://cran.r-project.org/src/contrib/kutils_1.73.tar.gz"],"subdirs":["noarch"],"summary":"Tools for data importation, recoding, and inspection. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame.  The main feature here is the systematic implementation of the \"variable key\" framework for data importation and recoding.  We are eager to have community feedback about the variable key and the vignette about it. In version 1.7, the function 'semTable' is removed. It was deprecated since 1.67. That is provided in a separate package, 'semTable'.","text_prefix":false,"timestamp":1757859055,"version":"1.73"},"r-kvh":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://serguei.sokol.free.fr/kvh-format/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/kvh/kvh_1.5.0.tar.gz","https://cloud.r-project.org/src/contrib/kvh_1.5.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The format KVH is a lightweight format that can be read/written both by humans and machines. It can be useful in situations where XML or alike formats seem to be an overkill. 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Includes many of the common ordination methods, with  graphical routines to facilitate their interpretation,  as well as several novel analyses.","text_prefix":false,"timestamp":1563654284,"version":"2.3_1"},"r-label.switching":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=label.switching","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/label.switching/label.switching_1.8.tar.gz","https://cran.r-project.org/src/contrib/label.switching_1.8.tar.gz"],"subdirs":["noarch"],"summary":"The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. 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Currently the following file formats are supported: comma separated format (CSV) and fixed width format. It is assumed that the files are too large to fit into memory, although the package can also be used to efficiently access files that do fit into memory. Methods are provided to access and process files blockwise. Furthermore, an opened file can be accessed as one would an ordinary data.frame. The LaF vignette gives an overview of the functionality provided.","text_prefix":false,"timestamp":1758099239,"version":"0.8.6"},"r-lahman":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://lahman.r-forge.r-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Lahman/Lahman_14.0-0.tar.gz","https://cran.r-project.org/src/contrib/Lahman_14.0-0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides the tables from the 'Sean Lahman Baseball Database' as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2018, as recorded in the 2019 version of the database. Documentation examples show how many baseball questions can be investigated.","text_prefix":false,"timestamp":0,"version":"14.0_0"},"r-lambda.r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lambda.r","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lambda.r/lambda.r_1.2.4.tar.gz","https://cran.r-project.org/src/contrib/lambda.r_1.2.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.","text_prefix":false,"timestamp":0,"version":"1.2.4"},"r-lambertw":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/gmgeorg/LambertW http://arxiv.org/abs/0912.4554 http://arxiv.org/abs/1010.2265 http://arxiv.org/abs/1602.02200","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/LambertW/LambertW_0.6.9-2.tar.gz","https://cran.r-project.org/src/contrib/LambertW_0.6.9-2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. Probably the most important function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away.","text_prefix":false,"timestamp":1646056728,"version":"0.6.9_2"},"r-laminr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"laminr is a R client for LaminDB. LaminDB is an open-source data framework to enable learning at scale in computational biology. 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Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, normalization, individual tree segmentation and other manipulations in a powerful and versatile processing chain.","text_prefix":true,"timestamp":1790801377,"version":"0.21.2"},"r-lasso2":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.maths.uwa.edu.au/~berwin/software/lasso.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lasso2/lasso2_1.2-22.tar.gz","https://cran.r-project.org/src/contrib/lasso2_1.2-22.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Routines and documentation for solving regression problems while imposing an L1 constraint on the estimates, based on the algorithm of Osborne et al. 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Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2019) <doi:10.1093/biostatistics/kxy082>). 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Utility functions for reading/writing data typically used in topic models, as well as tools for examining posterior distributions are also included.","text_prefix":false,"timestamp":1758127721,"version":"1.5.2"},"r-ldbounds":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ldbounds","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ldbounds/ldbounds_2.0.2.tar.gz","https://cran.r-project.org/src/contrib/ldbounds_2.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach. Based on FORTRAN program ld98 implemented by Reboussin, et al. 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Some missing Markdown features in 'commonmark' are also supported, such as raw HTML or 'LaTeX' blocks, 'LaTeX' math, superscripts, subscripts, footnotes, element attributes, and appendices, but not all 'Pandoc' Markdown features are (or will be) supported. With additional JavaScript and CSS, you can also create HTML slides and articles. This package can be viewed as a trimmed-down version of R Markdown and 'knitr'. It does not aim at rich Markdown features or a large variety of output formats (the primary formats are HTML and 'LaTeX'). 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Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.","text_prefix":false,"timestamp":0,"version":"3.2_1"},"r-lmesplines":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lmeSplines","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lmeSplines/lmeSplines_1.1.20.tar.gz","https://cran.r-project.org/src/contrib/lmeSplines_1.1.20.tar.gz"],"subdirs":["noarch"],"summary":"Add smoothing spline modelling capability to nlme. Fit smoothing spline terms in Gaussian linear and nonlinear mixed-effects models","text_prefix":false,"timestamp":1621888667,"version":"1.1.20"},"r-lmms":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lmms","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lmms/lmms_1.3.3.tar.gz","https://cran.r-project.org/src/contrib/lmms_1.3.3.tar.gz"],"subdirs":["noarch"],"summary":"Linear Mixed effect Model Splines ('lmms') implements linear mixed effect model splines for modelling and differential expression for highly dimensional data sets: investNoise() for quality control and filterNoise() for removing non-informative trajectories; lmmSpline() to model time course expression profiles and lmmsDE() performs differential expression analysis to identify differential expression between groups, time and/or group x time interaction.","text_prefix":false,"timestamp":1758521704,"version":"1.3.3"},"r-lmodel2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lmodel2","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lmodel2/lmodel2_1.7-4.tar.gz","https://cran.r-project.org/src/contrib/lmodel2_1.7-4.tar.gz"],"subdirs":["noarch"],"summary":"Computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA).","text_prefix":false,"timestamp":1757864859,"version":"1.7_4"},"r-lmom":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lmom","license":"CPL-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lmom/lmom_3.3.tar.gz","https://cran.r-project.org/src/contrib/lmom_3.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions related to L-moments: computation of L-moments and trimmed L-moments of distributions and data samples; parameter estimation; L-moment ratio diagram; plot vs. quantiles of an extreme-value distribution.","text_prefix":false,"timestamp":1621726972,"version":"3.3"},"r-lmomco":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.amazon.com/dp/1463508417/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/lmomco/lmomco_2.5.8.tar.gz","https://cloud.r-project.org/src/contrib/lmomco_2.5.8.tar.gz"],"subdirs":["noarch"],"summary":"Extensive functions for L-moments (LMs) and probability-weighted moments (PWMs), parameter estimation for distributions, LM computation for distributions, and L-moment ratio diagrams. Maximum likelihood and maximum product of spacings estimation are also available. LMs for right-tail and left-tail censoring by known or unknown threshold and by indicator variable are available. Asymmetric (asy) trimmed LMs (TL-moments, TLMs) are supported. LMs of residual (resid) and reversed (rev) resid life are implemented along with 13 quantile function operators for reliability and survival analyses. Exact analytical bootstrap estimates of order statistics, LMs, and variances- covariances of LMs are provided. The Harri-Coble Tau34-squared Normality Test is available. Distribution support with \"L\" (LMs), \"TL\" (TLMs) and added (+) support for right-tail censoring (RC) encompasses: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L,TL], Gen Logistic [L), Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Resid. Quantile Function [L], Normal [L], 3-p log-Normal [L], Pearson Type III [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice/Rician [L], Slash [TL], 3-p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L]. Multivariate sample L-comoments (LCMs) are implemented to measure asymmetric associations.","text_prefix":false,"timestamp":1628058756,"version":"2.5.8"},"r-lmoments":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://users.jyu.fi/~jutakarv/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Lmoments/Lmoments_1.3-2.tar.gz","https://cran.r-project.org/src/contrib/Lmoments_1.3-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains functions to estimate L-moments and trimmed L-moments from the data. Also contains functions to estimate the parameters of the normal polynomial quantile mixture and the Cauchy polynomial quantile mixture from L-moments and trimmed L-moments.","text_prefix":false,"timestamp":1539745001,"version":"1.3_2"},"r-lmperm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mtorchiano/lmPerm","doc_url":"https://cran.r-project.org/web/packages/lmPerm/lmPerm.pdf","home":"https://cran.r-project.org/web/packages/lmPerm/index.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lmPerm/lmPerm_2.1.6.tar.gz","https://cran.r-project.org/src/contrib/lmPerm_2.1.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Linear model functions using permutation tests.","text_prefix":false,"timestamp":1763739821,"version":"2.1.6"},"r-lmtest":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lmtest","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lmtest/lmtest_0.9-40.tar.gz","https://cran.r-project.org/src/contrib/lmtest_0.9-40.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A collection of tests, data sets, and examples for diagnostic checking in linear regression models. Furthermore, some generic tools for inference in parametric models are provided.","text_prefix":false,"timestamp":1533314673,"version":"0.9_40"},"r-lmtp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/nt-williams/lmtp","home":"https://beyondtheate.com/","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lmtp/lmtp_1.5.4.tar.gz","https://cran.r-project.org/src/contrib/lmtp_1.5.4.tar.gz"],"subdirs":["noarch"],"summary":"Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).","text_prefix":false,"timestamp":1778337807,"version":"1.5.4"},"r-lmvar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lmvar","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lmvar/lmvar_1.5.2.tar.gz","https://cran.r-project.org/src/contrib/lmvar_1.5.2.tar.gz"],"subdirs":["noarch"],"summary":"Runs a linear-like regression with in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.","text_prefix":false,"timestamp":1758348053,"version":"1.5.2"},"r-loader":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SantanderMetGroup/loadeR","doc_url":"https://github.com/SantanderMetGroup/loadeR/wiki","home":"https://github.com/SantanderMetGroup/climate4R","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/loadeR/archive/refs/tags/v1.8.4.tar.gz","subdirs":["noarch"],"summary":"Enables harmonized access to either remote climate databases via OPeNDAP or to locally stored data, allowing the retrieval of user-defined dimensional slices from large climate datasets. For more details see Iturbide et al 2019 <http://dx.doi.org/10.1016/j.envsoft.2018.09.009>.","text_prefix":false,"timestamp":1652796133,"version":"1.8.4"},"r-loader.2nc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SantanderMetGroup/loadeR/wiki","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/loadeR.2nc/archive/refs/tags/v0.1.3.tar.gz","subdirs":["noarch"],"summary":"Write climate4R grids to netCDF files (http://www.meteo.unican.es/climate4R).","text_prefix":false,"timestamp":1652794625,"version":"0.1.3"},"r-loader.ecoms":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SantanderMetGroup/loadeR.ECOMS","doc_url":"https://github.com/SantanderMetGroup/loadeR.ECOMS","home":"https://github.com/SantanderMetGroup/climate4R","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/loadeR.ECOMS/archive/refs/tags/v1.4.8.tar.gz","subdirs":["noarch"],"summary":"Enables harmonized access via OPeNDAP to hindcast and other climate databases of the FP-7 ECOMS consortium, stored at the Santander MetGroup's User Data Gateway (<http://meteo.unican.es/ecoms-udg>), as described in Cofi\u00f1o et al 2017 <http://dx.doi.org/10.1016/j.cliser.2017.07.001>. Previous registration via the Santander MetGroup's THREDDS Administration Panel (TAP) is required. Data access is regulated through a fine-grained authorization scheme depending on data access policies, although permission for the PUBLIC role is granted by default. Go to <http://meteo.unican.es/ecoms-udg/dataserver/registration> for more details.","text_prefix":false,"timestamp":1758023222,"version":"1.4.8"},"r-loader.java":{"activate.d":true,"binary_prefix":false,"deactivate.d":true,"home":"https://github.com/SantanderMetGroup/loadeR.java","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/loadeR.java/archive/refs/tags/v1.2.2.tar.gz","subdirs":["noarch"],"summary":"Initializes the Unidata's NetCDF Java Library that implements the full CDM model, including all other .jar dependencies.","text_prefix":false,"timestamp":1775132804,"version":"1.2.2"},"r-lobstr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-lib/lobstr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/lobstr/lobstr_1.2.2.tar.gz","https://cloud.r-project.org/src/contrib/lobstr_1.2.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A set of tools for inspecting and understanding R data structures inspired by str(). Includes ast() for visualizing abstract  syntax trees, ref() for showing shared references, cst() for showing  call stack trees, and obj_size() for computing object sizes.","text_prefix":false,"timestamp":1545700881,"version":"1.2.2"},"r-locfdr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=locfdr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/locfdr/locfdr_1.1-8.tar.gz","https://cran.r-project.org/src/contrib/locfdr_1.1-8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Computation of local false discovery rates.","text_prefix":false,"timestamp":0,"version":"1.1_8"},"r-locfit":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=locfit","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/locfit/locfit_1.5-9.12.tar.gz","https://cran.r-project.org/src/contrib/locfit_1.5-9.12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Local regression, likelihood and density estimation.","text_prefix":false,"timestamp":1563538074,"version":"1.5_9.12"},"r-locpol":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=locpol","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/locpol/locpol_0.9.0.tar.gz","https://cran.r-project.org/src/contrib/locpol_0.9.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Computes local polynomial estimators for  the regression and also density. It comprises several  different utilities to handle kernel estimators.","text_prefix":false,"timestamp":1665442200,"version":"0.9.0"},"r-loe":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=loe","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/loe/loe_1.1.tar.gz","https://cran.r-project.org/src/contrib/loe_1.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Local Ordinal embedding (LOE) is one of graph embedding methods for unweighted graphs.","text_prefix":false,"timestamp":1758604874,"version":"1.1"},"r-log4r":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/johnmyleswhite/log4r","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/log4r/log4r_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/log4r_0.5.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","win-64"],"summary":"The log4r package is meant to provide a fast, lightweight, object-oriented approach to logging in R based on the widely-emulated 'log4j' system and etymology.","text_prefix":false,"timestamp":0,"version":"0.5.0"},"r-logger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/daroczig/logger","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/logger/logger_0.4.3.tar.gz","https://cloud.r-project.org/src/contrib/logger_0.4.3.tar.gz"],"subdirs":["noarch"],"summary":"Inspired by the the 'futile.logger' R package and 'logging' Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.","text_prefix":false,"timestamp":1625599177,"version":"0.4.3"},"r-logging":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/WLOGSolutions/r-logging","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/logging/logging_0.10-111.tar.gz","https://cran.r-project.org/src/contrib/logging_0.10-111.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Pure R implementation of the ubiquitous log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting.","text_prefix":false,"timestamp":1540158391,"version":"0.10_111"},"r-logicreg":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=LogicReg","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/LogicReg/LogicReg_1.6.6.tar.gz","https://cran.r-project.org/src/contrib/LogicReg_1.6.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) <DOI:10.1002/gepi.20042>.","text_prefix":false,"timestamp":1546632980,"version":"1.6.6"},"r-logistf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"Fit a logistic regression model using Firths bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys  prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firths method was proposed as ideal solution to the problem of separation in logistic regression. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained.","dev_url":"https://github.com/georgheinze/logistf","home":"https://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/firth-correction/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/logistf/logistf_1.26.1.tar.gz","https://cran.r-project.org/src/contrib/logistf_1.26.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.","text_prefix":false,"timestamp":1665962074,"version":"1.26.1"},"r-logitnorm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=logitnorm","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/logitnorm/logitnorm_0.8.39.tar.gz","https://cran.r-project.org/src/contrib/logitnorm_0.8.39.tar.gz"],"subdirs":["noarch"],"summary":"Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.","text_prefix":false,"timestamp":1758648765,"version":"0.8.39"},"r-logitr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jhelvy/logitr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/logitr/logitr_1.1.3.tar.gz","https://cran.r-project.org/src/contrib/logitr_1.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using \"Preference\" space or \"Willingness-to-pay\" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.","text_prefix":false,"timestamp":1763450249,"version":"1.1.3"},"r-lognormreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=logNormReg","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/logNormReg/logNormReg_0.5-0.tar.gz","https://cran.r-project.org/src/contrib/logNormReg_0.5-0.tar.gz"],"subdirs":["noarch"],"summary":"Functions to fits simple linear regression models with log normal errors and identity link (taking the responses on the original scale). See Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.","text_prefix":false,"timestamp":1634044585,"version":"0.5_0"},"r-logr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/dbosak01/logr","home":"https://logr.r-sassy.org","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/logr/logr_1.4.1.tar.gz","https://cloud.r-project.org/src/contrib/logr_1.4.1.tar.gz"],"subdirs":["noarch"],"summary":"Contains functions to help create log files.  The package aims to overcome the difficulty of the base R sink() command.  The log_print() function will print to both the console and the file log, without interfering in other write operations.","text_prefix":false,"timestamp":1792139364,"version":"1.4.1"},"r-logrx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pharmaverse.github.io/logrx/, https://github.com/pharmaverse/logrx","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/logrx/logrx_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/logrx_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"A utility to facilitate the logging and review of R programs in clinical trial programming workflows.","text_prefix":false,"timestamp":1758284942,"version":"0.4.0"},"r-logspline":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=logspline","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/logspline/logspline_2.1.22.tar.gz","https://cran.r-project.org/src/contrib/logspline_2.1.22.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e. the Kooperberg and Stone (1992)  algorithm (with an improved interface).  The recommended routine logspline() uses an algorithm from Stone et al (1997)  <DOI:10.1214/aos/1031594728>.","text_prefix":false,"timestamp":1544635804,"version":"2.1.22"},"r-lokern":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lokern","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lokern/lokern_1.1-12.tar.gz","https://cran.r-project.org/src/contrib/lokern_1.1-12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Kernel regression smoothing with adaptive local or global plug-in bandwidth selection.","text_prefix":false,"timestamp":1603205632,"version":"1.1_12"},"r-longcateda":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=longCatEDA","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/longCatEDA/longCatEDA_0.31.tar.gz","https://cran.r-project.org/src/contrib/longCatEDA_0.31.tar.gz"],"subdirs":["noarch"],"summary":"Methods for plotting categorical longitudinal and time-series data by mapping individuals to the vertical space (each horizontal line represents a participant), time (or repeated measures) to the horizontal space, categorical (or discrete) states as facets using color or shade, and events to points using plotting characters. Sorting individuals in the vertical space and (or) stratifying them by groups can reveal patterns in the changes over time.","text_prefix":false,"timestamp":1758263156,"version":"0.31"},"r-longitudinal":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://strimmerlab.org/software/longitudinal/","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/longitudinal/longitudinal_1.1.13.tar.gz","https://cran.r-project.org/src/contrib/longitudinal_1.1.13.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains general data structures and functions for longitudinal data with multiple variables, repeated measurements, and irregularly spaced time points. Also implements a shrinkage estimator of dynamical correlation and dynamical covariance.","text_prefix":false,"timestamp":1539922269,"version":"1.1.13"},"r-longitudinaldata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=longitudinalData","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/longitudinalData/longitudinalData_2.4.7.tar.gz","https://cran.r-project.org/src/contrib/longitudinalData_2.4.7.tar.gz"],"subdirs":["linux-64","noarch","win-64"],"summary":"Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).","text_prefix":false,"timestamp":1565137584,"version":"2.4.7"},"r-loo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://mc-stan.org/loo, https://discourse.mc-stan.org","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/loo/loo_2.10.1.tar.gz","https://cran.r-project.org/src/contrib/loo_2.10.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also  provides methods for using stacking and other model weighting techniques  to average Bayesian predictive distributions.","text_prefix":false,"timestamp":0,"version":"2.10.1"},"r-loon":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://great-northern-diver.github.io/loon/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/loon/loon_1.4.3.tar.gz","https://cran.r-project.org/src/contrib/loon_1.4.3.tar.gz"],"subdirs":["noarch"],"summary":"An extendable toolkit for interactive data visualization and exploration.","text_prefix":false,"timestamp":1666349192,"version":"1.4.3"},"r-loose.rock":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.github.com/averissimo/loose.rock","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/loose.rock/loose.rock_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/loose.rock_1.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Collection of functions to improve work-flow in survival analysis and data science. The package features include: the generation of balanced datasets, live retrieval of  protein coding genes from two public databases, generation of random  matrix based on covariance matrix, cache function to store function results. This work was supported by two grants from the Portuguese Foundation for Science and technology, and the EU Commission under SOUND project.","text_prefix":false,"timestamp":1758633133,"version":"1.1.0"},"r-lotri":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/nlmixrdevelopment/lotri","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lotri/lotri_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/lotri_1.0.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a simple mechanism to specify a symmetric block diagonal matrices (often used for covariance matrices).  This is based on the domain specific language implemented in 'nlmixr' but expanded to create matrices in R generally instead of specifying parts of matrices to estimate.","text_prefix":false,"timestamp":1650559049,"version":"1.0.2"},"r-lpsolve":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lpSolve","license":"LGPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lpSolve/lpSolve_5.6.23.tar.gz","https://cran.r-project.org/src/contrib/lpSolve_5.6.23.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. In this implementation we supply a \"wrapper\" function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. This version calls lp_solve version 5.5.","text_prefix":false,"timestamp":1665474355,"version":"5.6.23"},"r-lpsolveapi":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lpSolveAPI","license":"LGPL-2.1-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lpSolveAPI/lpSolveAPI_5.5.2.0-17.15.tar.gz","https://cran.r-project.org/src/contrib/lpSolveAPI_5.5.2.0-17.15.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"The lpSolveAPI package provides an R interface to 'lp_solve', a Mixed Integer Linear Programming (MILP) solver with support for pure linear, (mixed) integer/binary, semi-continuous and special ordered sets (SOS) models.","text_prefix":false,"timestamp":1546659096,"version":"5.5.2.0_17.15"},"r-lsa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lsa","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lsa/lsa_0.73.4.tar.gz","https://cran.r-project.org/src/contrib/lsa_0.73.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The basic idea of latent semantic analysis (LSA) is,  that text do have a higher order (=latent semantic) structure which,  however, is obscured by word usage (e.g. through the use of synonyms  or polysemy). By using conceptual indices that are derived statistically  via a truncated singular value decomposition (a two-mode factor analysis)  over a given document-term matrix, this variability problem can be overcome.","text_prefix":false,"timestamp":0,"version":"0.73.4"},"r-lsd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=LSD","license":"GPL-2.0-only OR GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/LSD/LSD_4.1-0.tar.gz","https://cran.r-project.org/src/contrib/LSD_4.1-0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Create lots of colorful plots in a plethora of variations. Try the LSD demotour().","text_prefix":false,"timestamp":1539918999,"version":"4.1_0"},"r-lsei":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/lsei/index.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lsei/lsei_1.3-0.tar.gz","https://cran.r-project.org/src/contrib/lsei_1.3-0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"It contains functions that solve least squares linear regression problems under linear equality/inequality constraints. Functions for solving quadratic programming problems are also available, which transform such problems into least squares ones first. It is developed based on the 'Fortran' program of Lawson and Hanson (1974, 1995), which is public domain and available at <http","text_prefix":false,"timestamp":1588909285,"version":"1.3_0"},"r-lsmeans":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=lsmeans","license":"GPL-2.0-only OR GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lsmeans/lsmeans_2.30-2.tar.gz","https://cran.r-project.org/src/contrib/lsmeans_2.30-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Obtain least-squares means for linear, generalized linear,  and mixed models. Compute contrasts or linear functions of  least-squares means, and comparisons of slopes.  Plots and compact letter displays. Least-squares means were proposed in Harvey, W (1960) \"Least-squares analysis of data with unequal subclass numbers\", Tech Report ARS-20-8, USDA National Agricultural Library, and discussed further in Searle, Speed, and Milliken (1980) \"Population marginal means  in the linear model: An alternative to least squares means\",  The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>. NOTE: lsmeans now relies primarily on code in the 'emmeans' package. 'lsmeans' will be archived in the near future.","text_prefix":false,"timestamp":1539007025,"version":"2.30_2"},"r-lsoda":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mclements/lsoda","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lsoda/lsoda_1.2.tar.gz","https://cran.r-project.org/src/contrib/lsoda_1.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A 'C++' header library for using the 'libsoda-cxx' library with R. The 'C++' header reimplements the 'lsoda' function from the 'ODEPACK' library for solving initial value problems for first order ordinary differential equations (Hindmarsh, 1982; <https://computing.llnl.gov/sites/default/files/ODEPACK_pub1_u88007.pdf>). The 'C++' header can be used by other R packages by linking against this package. The 'C++' functions can be called inline using 'Rcpp'. Finally, the package provides an 'ode' function to call from R.","text_prefix":false,"timestamp":1763221952,"version":"1.2"},"r-lsr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lsr/lsr_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/lsr_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"A collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. It accompanies Navarro, D. J. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.5. [Lecture notes] School of Psychology, University of Adelaide, Adelaide, Australia. ISBN: 978-1-326-18972-3. URL: http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/.","text_prefix":false,"timestamp":1632375078,"version":"1.0.0"},"r-ltmle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://joshuaschwab.github.io/ltmle/","home":"https://github.com/joshuaschwab/ltmle","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ltmle/ltmle_1.3-0.tar.gz","https://cran.r-project.org/src/contrib/ltmle_1.3-0.tar.gz"],"subdirs":["noarch"],"summary":"Targeted Maximum Likelihood Estimation ('TMLE') of treatment/censoring specific mean outcome or marginal structural model for point-treatment and longitudinal data.","text_prefix":false,"timestamp":1758467096,"version":"1.3_0"},"r-lubridate":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/tidyverse/lubridate","doc_url":"https://lubridate.tidyverse.org/reference/","home":"https://lubridate.tidyverse.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lubridate/lubridate_1.9.5.tar.gz","https://cran.r-project.org/src/contrib/lubridate_1.9.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The 'lubridate' package has a consistent and memorable syntax that makes working with dates easy and fun.","text_prefix":false,"timestamp":0,"version":"1.9.5"},"r-luminescence":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Luminescence","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/Luminescence/Luminescence_1.3.1.tar.gz","https://cloud.r-project.org/src/contrib/Luminescence_1.3.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.","text_prefix":false,"timestamp":1619826025,"version":"1.3.1"},"r-lutz":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://andyteucher.ca/lutz/, https://github.com/ateucher/lutz","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lutz/lutz_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/lutz_0.3.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Input latitude and longitude values or an 'sf/sfc' POINT object and get back the time zone in which they exist. Two methods are implemented. One is very fast and uses 'Rcpp' in conjunction with data from the 'Javascript' library (<https://github.com/darkskyapp/tz-lookup-oss/>). This method also works outside of countries' borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the 'sf' package to intersect points with a detailed map of time zones from here: <https://github.com/evansiroky/timezone-boundary-builder/>. The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect.","text_prefix":false,"timestamp":1780077785,"version":"0.3.2"},"r-luz":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://mlverse.github.io/luz/, https://github.com/mlverse/luz","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/luz/luz_0.5.2.tar.gz","https://cran.r-project.org/src/contrib/luz_0.5.2.tar.gz"],"subdirs":["noarch"],"summary":"A high level interface for 'torch' providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to support expressing a large range of models. It's heavily inspired by 'fastai' by Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.","text_prefix":false,"timestamp":1777449339,"version":"0.5.2"},"r-lvm4net":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/igollini/lvm4net","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/lvm4net/lvm4net_0.3.tar.gz","https://cran.r-project.org/src/contrib/lvm4net_0.3.tar.gz"],"subdirs":["noarch"],"summary":"Latent variable models for network data using fast inferential procedures. For more information please visit: <http://igollini.github.io/lvm4net/>.","text_prefix":false,"timestamp":1758745729,"version":"0.3"},"r-lwgeom":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"Access to selected functions found in liblwgeom, the light-weight geometry library used by PostGIS","dev_url":"https://github.com/r-spatial/lwgeom/","doc_url":"https://cran.r-project.org/web/packages/lwgeom/lwgeom.pdf","home":"https://r-spatial.github.io/lwgeom/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/lwgeom/lwgeom_0.2-17.tar.gz","https://cloud.r-project.org/src/contrib/lwgeom_0.2-17.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Access to selected functions found in 'liblwgeom' <https://github.com/postgis/postgis/tree/svn-trunk/liblwgeom>, the light-weight geometry library used by 'PostGIS' <http://postgis.net/>.","text_prefix":false,"timestamp":1633273882,"version":"0.2_17"},"r-maditr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gdemin/maditr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maditr/maditr_0.8.6.tar.gz","https://cran.r-project.org/src/contrib/maditr_0.8.6.tar.gz"],"subdirs":["noarch"],"summary":"Provides pipe-style interface for 'data.table'. Package preserves all 'data.table' features without significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data manipulation tasks. For example, you can write 'take(mtcars, mean(mpg), by = am)' for aggregation or 'let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional aggregation/modification. Additionally there are some conveniences such as automatic 'data.frame' conversion to 'data.table'.","text_prefix":false,"timestamp":1641659304,"version":"0.8.6"},"r-magic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RobinHankin/magic.git","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/magic/magic_1.6-1.tar.gz","https://cran.r-project.org/src/contrib/magic_1.6-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of efficient, vectorized algorithms for the creation and investigation of magic squares and hypercubes, including a variety of functions for the manipulation and analysis of arbitrarily dimensioned arrays.  The package includes methods for creating normal magic squares of any order greater than 2.  The ultimate intention is for the package to be a computerized embodiment all magic square knowledge, including direct numerical verification of properties of magic squares (such as recent results on the determinant of odd-ordered semimagic squares).  Some antimagic functionality is included.  The package also serves as a rebuttal to the often-heard comment \"I thought R was just for statistics\".","text_prefix":false,"timestamp":0,"version":"1.6_1"},"r-magicaxis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=magicaxis","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/magicaxis/magicaxis_2.5.1.tar.gz","https://cran.r-project.org/src/contrib/magicaxis_2.5.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.","text_prefix":false,"timestamp":1534789993,"version":"2.5.1"},"r-magick":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ropensci/magick","home":"https://docs.ropensci.org/magick","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/magick/magick_2.9.1.tar.gz","https://cran.r-project.org/src/contrib/magick_2.9.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Bindings to 'ImageMagick': the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. 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In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.","text_prefix":false,"timestamp":1627586408,"version":"1.22.3"},"r-maldiquantforeign":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://strimmerlab.org/software/maldiquant/ https://github.com/sgibb/MALDIquantForeign/","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MALDIquantForeign/MALDIquantForeign_0.14.1.tar.gz","https://cran.r-project.org/src/contrib/MALDIquantForeign_0.14.1.tar.gz"],"subdirs":["noarch"],"summary":"Functions for reading (tab, csv, Bruker fid, Ciphergen XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and writing (tab, csv, mMass MSD, mzML, imzML) different file formats of mass spectrometry data into/from 'MALDIquant' objects.","text_prefix":false,"timestamp":1621920536,"version":"0.14.1"},"r-maldirppa":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MALDIrppa","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MALDIrppa/MALDIrppa_1.1.0-3.tar.gz","https://cran.r-project.org/src/contrib/MALDIrppa_1.1.0-3.tar.gz"],"subdirs":["noarch"],"summary":"Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data.","text_prefix":false,"timestamp":1634303519,"version":"1.1.0_3"},"r-manhattanly":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sahirbhatnagar/manhattanly/, https://sahirbhatnagar.com/manhattanly/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/manhattanly/manhattanly_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/manhattanly_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Create interactive manhattan, Q-Q and volcano plots that are usable from the R console, in 'Dash' apps, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover the mouse pointer over a point to show details or drag a rectangle to zoom. A manhattan plot is a popular graphical method for visualizing results from high-dimensional data analysis such as a (epi)genome wide association study (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter plot against their genomic position. Manhattan plots are used for visualizing potential regions of interest in the genome that are associated with a phenotype. Interactive manhattan plots allow the inspection of specific value (e.g. rs number or gene name) by hovering the mouse over a cell, as well as zooming into a region of the genome (e.g. a chromosome) by dragging a rectangle around the relevant area. This work is based on the 'qqman' package and the 'plotly.js' engine. It produces similar manhattan and Q-Q plots as the 'manhattan' and 'qq' functions in the 'qqman' package, with the advantage of including extra annotation information and interactive web-based visualizations directly from R. Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) can be viewed and modified in a web browser.","text_prefix":false,"timestamp":1758591685,"version":"0.3.0"},"r-manipulate":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=manipulate","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/manipulate/manipulate_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/manipulate_1.0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Interactive plotting functions for use within RStudio. The manipulate function accepts a plotting expression and a set of controls (e.g. slider, picker, checkbox, or button) which are used to dynamically change values within the expression. When a value is changed using its corresponding control the expression is automatically re-executed and the plot is redrawn.","text_prefix":false,"timestamp":0,"version":"1.0.1"},"r-manipulatewidget":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rte-antares-rpackage/manipulateWidget","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/manipulateWidget/manipulateWidget_0.11.2.tar.gz","https://cran.r-project.org/src/contrib/manipulateWidget_0.11.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Like package 'manipulate' does for static graphics, this package helps to easily add controls like sliders, pickers, checkboxes, etc. that  can be used to modify the input data or the parameters of an interactive  chart created with package 'htmlwidgets'.","text_prefix":false,"timestamp":1540933473,"version":"0.11.2"},"r-mantar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/kai-nehler/mantar","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/mantar/mantar_0.3.2.tar.gz","https://cloud.r-project.org/src/contrib/mantar_0.3.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides functionality for estimating cross-sectional network structures representing partial correlations while accounting for missing data. 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The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.","text_prefix":false,"timestamp":1757897633,"version":"1.5.3"},"r-mapproj":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mapproj","license":"LPL-1.02","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mapproj/mapproj_1.2.12.tar.gz","https://cran.r-project.org/src/contrib/mapproj_1.2.12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Converts latitude/longitude into projected coordinates.","text_prefix":false,"timestamp":1667310624,"version":"1.2.12"},"r-maps":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=maps","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maps/maps_3.4.3.tar.gz","https://cran.r-project.org/src/contrib/maps_3.4.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Display of maps.  Projection code and larger maps are in separate packages ('mapproj' and 'mapdata').","text_prefix":false,"timestamp":0,"version":"3.4.3"},"r-maptiles":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/riatelab/maptiles/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/maptiles/maptiles_0.13.0.tar.gz","https://cloud.r-project.org/src/contrib/maptiles_0.13.0.tar.gz"],"subdirs":["linux-64","noarch"],"summary":"To create maps from tiles, 'maptiles' downloads, composes and displays tiles from a large number of providers (e.g. 'OpenStreetMap', 'Stadia', 'Esri', 'CARTO', or 'Thunderforest').","text_prefix":false,"timestamp":1792315419,"version":"0.13.0"},"r-maptools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://r-forge.r-project.org/projects/maptools/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maptools/maptools_1.1-8.tar.gz","https://cran.r-project.org/src/contrib/maptools_1.1-8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Set of tools for manipulating geographic data. It includes binary access to 'GSHHG' shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat', 'maps', 'RArcInfo', and others.","text_prefix":false,"timestamp":1589483671,"version":"1.1_8"},"r-maptpx":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://faculty.chicagobooth.edu/matt.taddy/index.html","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maptpx/maptpx_1.9-7.tar.gz","https://cran.r-project.org/src/contrib/maptpx_1.9-7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Posterior maximization for topic models (LDA) in text analysis, as described in Taddy (2012) `on estimation and selection for topic models'.  Previous versions of this code were included as part of the textir package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.","text_prefix":false,"timestamp":1588771972,"version":"1.9_7"},"r-maptree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=maptree","license":"Unlimited","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maptree/maptree_1.4-9.tar.gz","https://cran.r-project.org/src/contrib/maptree_1.4-9.tar.gz"],"subdirs":["noarch"],"summary":"Functions with example data for graphing, pruning, and mapping models from hierarchical clustering, and classification and regression trees.","text_prefix":false,"timestamp":1621920537,"version":"1.4_9"},"r-mapview":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-spatial/mapview","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mapview/mapview_2.11.4.tar.gz","https://cran.r-project.org/src/contrib/mapview_2.11.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.","text_prefix":false,"timestamp":1588768767,"version":"2.11.4"},"r-marginaleffects":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/vincentarelbundock/marginaleffects","home":"https://vincentarelbundock.github.io/marginaleffects/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/marginaleffects/marginaleffects_1.0.0.tar.gz","https://cloud.r-project.org/src/contrib/marginaleffects_1.0.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Compute, summarize, and plot marginal effects, adjusted predictions, contrasts, and marginal means for a wide variety of models.","text_prefix":false,"timestamp":1788434862,"version":"1.0.0"},"r-margins":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/leeper/margins","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/margins/margins_0.3.28.tar.gz","https://cran.r-project.org/src/contrib/margins_0.3.28.tar.gz"],"subdirs":["noarch"],"summary":"An R port of Stata's 'margins' command, which can be used to calculate marginal (or partial) effects from model objects.","text_prefix":false,"timestamp":1588773606,"version":"0.3.28"},"r-markdown":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/rstudio/markdown","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/markdown/markdown_2.0.tar.gz","https://cran.r-project.org/src/contrib/markdown_2.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Provides R bindings to the 'Sundown' Markdown rendering library (<https://github.com/vmg/sundown>). Markdown is a plain-text formatting syntax that can be converted to 'XHTML' or other formats. See <http://en.wikipedia.org/wiki/Markdown> for more information about Markdown.","text_prefix":false,"timestamp":1666205159,"version":"2.0"},"r-markovchain":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/spedygiorgio/markovchain/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/markovchain/markovchain_1.1.1.tar.gz","https://cloud.r-project.org/src/contrib/markovchain_1.1.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided.","text_prefix":false,"timestamp":1583612489,"version":"1.1.1"},"r-marmap":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ericpante/marmap","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/marmap/marmap_1.0.12.tar.gz","https://cran.r-project.org/src/contrib/marmap_1.0.12.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Import xyz data from the NOAA (National Oceanic and Atmospheric Administration, <https://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans, <https://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.","text_prefix":false,"timestamp":1668454170,"version":"1.0.12"},"r-marquee":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/r-lib/marquee","home":"https://marquee.r-lib.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/marquee/marquee_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/marquee_1.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides the mean to parse and render markdown text with grid along with facilities to define the styling of the text.","text_prefix":false,"timestamp":1758533662,"version":"1.2.1"},"r-mashr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/stephenslab/mashr","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mashr/mashr_0.2.79.tar.gz","https://cran.r-project.org/src/contrib/mashr_0.2.79.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.","text_prefix":false,"timestamp":1759103603,"version":"0.2.79"},"r-mass":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.stats.ox.ac.uk/pub/MASS4/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MASS/MASS_7.3-66.tar.gz","https://cran.r-project.org/src/contrib/MASS_7.3-66.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions and datasets to support Venables and Ripley, \"Modern Applied Statistics with S\" (4th edition, 2002).","text_prefix":false,"timestamp":1642073547,"version":"7.3_66"},"r-matching":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://sekhon.berkeley.edu/matching","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Matching/Matching_4.10-15.tar.gz","https://cran.r-project.org/src/contrib/Matching_4.10-15.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides functions for multivariate and propensity score matching  and for finding optimal balance based on a genetic search algorithm.  A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided.","text_prefix":false,"timestamp":1621769436,"version":"4.10_15"},"r-matchingr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jtilly/matchingR/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/matchingR/matchingR_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/matchingR_2.0.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Computes matching algorithms quickly using Rcpp. Implements the Gale-Shapley Algorithm to compute the stable matching for two-sided markets, such as the stable marriage problem and the college-admissions problem. Implements Irving's Algorithm for the stable roommate problem. Implements the top trading cycle algorithm for the indivisible goods trading problem.","text_prefix":false,"timestamp":1607331159,"version":"2.0.0"},"r-matchit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://kosukeimai.github.io/MatchIt/, https://github.com/kosukeimai/MatchIt","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MatchIt/MatchIt_4.7.2.tar.gz","https://cran.r-project.org/src/contrib/MatchIt_4.7.2.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures.  The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)","text_prefix":false,"timestamp":1665749857,"version":"4.7.2"},"r-matchthem":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/FarhadPishgar/MatchThem","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MatchThem/MatchThem_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/MatchThem_1.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for more details).","text_prefix":false,"timestamp":1758721996,"version":"1.2.1"},"r-mathjaxr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/wviechtb/mathjaxr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mathjaxr/mathjaxr_2.0-0.tar.gz","https://cran.r-project.org/src/contrib/mathjaxr_2.0-0.tar.gz"],"subdirs":["noarch"],"summary":"Provides 'MathJax' and macros to enable its use within Rd files for rendering equations in the HTML help files.","text_prefix":false,"timestamp":1612031756,"version":"2.0_0"},"r-matlab":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://cran.r-project.org/package=matlab","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/matlab/matlab_1.0.4.1.tar.gz","https://cran.r-project.org/src/contrib/matlab_1.0.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Emulate MATLAB code using R","text_prefix":false,"timestamp":1538329662,"version":"1.0.4.1"},"r-matlib":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/friendly/matlib","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/matlib/matlib_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/matlib_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"A collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. These functions are mainly for tutorial purposes in learning matrix algebra ideas using R. In some cases, functions are provided for concepts available elsewhere in R, but where the function call or name is not obvious. In other cases, functions are provided to show or demonstrate an algorithm. In addition, a collection of functions are provided for drawing vector diagrams in 2D and 3D.","text_prefix":false,"timestamp":1568961787,"version":"1.0.1"},"r-matrix":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://codeberg.org/jaganmn/Matrix","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Matrix/Matrix_1.7-6.tar.gz","https://cran.r-project.org/src/contrib/Matrix_1.7-6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A rich hierarchy of sparse and dense matrix classes, including general, symmetric, triangular, and diagonal matrices with numeric, logical, or pattern entries. Efficient methods for operating on such matrices, often wrapping the 'BLAS', 'LAPACK', and 'SuiteSparse' libraries.","text_prefix":false,"timestamp":1552829913,"version":"1.7_6"},"r-matrix.utils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/cvarrichio/Matrix.utils","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Matrix.utils/Matrix.utils_0.9.8.tar.gz","https://cran.r-project.org/src/contrib/Matrix.utils_0.9.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implements data manipulation methods such as cast, aggregate, and merge/join for Matrix and matrix-like objects.","text_prefix":false,"timestamp":0,"version":"0.9.8"},"r-matrixcalc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=matrixcalc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/matrixcalc/matrixcalc_1.0-6.tar.gz","https://cran.r-project.org/src/contrib/matrixcalc_1.0-6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201.","text_prefix":false,"timestamp":1538233999,"version":"1.0_6"},"r-matrixcorrelation":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MatrixCorrelation","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MatrixCorrelation/MatrixCorrelation_0.10.1.tar.gz","https://cran.r-project.org/src/contrib/MatrixCorrelation_0.10.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Computation and visualization of matrix correlation coefficients. The main method is the Similarity of Matrices Index, while various related measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2 and adjusted RV are included for comparison.","text_prefix":false,"timestamp":1589587023,"version":"0.10.1"},"r-matrixeqtl":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/","license":"LGPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MatrixEQTL/MatrixEQTL_2.3.tar.gz","https://cran.r-project.org/src/contrib/MatrixEQTL_2.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Matrix eQTL is designed for fast eQTL analysis on large datasets. Matrix eQTL can test for association between genotype and gene expression using linear regression with either additive or ANOVA genotype effects. The models can include covariates to account for factors as population stratification, gender, and clinical variables. It also supports models with heteroscedastic and/or correlated errors, false discovery rate estimation and separate treatment of local (cis) and distant (trans) eQTLs. For more details see Shabalin (2012) <doi:10.1093/bioinformatics/bts163>.","text_prefix":false,"timestamp":1539922556,"version":"2.3"},"r-matrixextra":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/david-cortes/MatrixExtra","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MatrixExtra/MatrixExtra_0.1.15.tar.gz","https://cran.r-project.org/src/contrib/MatrixExtra_0.1.15.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Extends sparse matrix and vector classes from the 'Matrix' package by providing: (a) Methods and operators that work natively on CSR formats (compressed sparse row, a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), mathematical operators for CSR and COO such as addition (\"+\") or sqrt(), and methods such as diag(); (b) Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, including the 'float32' type from 'float'; (c) Coercion methods between pairs of classes which are not present in 'Matrix', such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; (d) Utility functions for sparse matrices such as sorting the indices or removing zero-valued entries; (e) Fast transposes that work by outputting in the opposite storage format; (f) Faster replacements for many 'Matrix' methods for all sparse types, such as slicing and elementwise multiplication. (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.","text_prefix":false,"timestamp":1652687555,"version":"0.1.15"},"r-matrixlaplacian":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=matrixLaplacian","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/matrixLaplacian/matrixLaplacian_1.0.tar.gz","https://cran.r-project.org/src/contrib/matrixLaplacian_1.0.tar.gz"],"subdirs":["noarch"],"summary":"Constructs the normalized Laplacian matrix of a square matrix, returns the eigenvectors (singular vectors) and visualization of normalized Laplacian map.","text_prefix":false,"timestamp":1758616069,"version":"1.0"},"r-matrixmodels":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://Matrix.R-forge.R-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MatrixModels/MatrixModels_0.5-4.tar.gz","https://cran.r-project.org/src/contrib/MatrixModels_0.5-4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Modelling with sparse and dense 'Matrix' matrices, using modular prediction and response module classes.","text_prefix":false,"timestamp":1536446648,"version":"0.5_4"},"r-matrixstats":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/matrixStats","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/matrixStats/matrixStats_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/matrixStats_1.5.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds().  Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.  There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().","text_prefix":false,"timestamp":1533311132,"version":"1.5.0"},"r-matrixtests":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/KKPMW/matrixTests","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/matrixTests/matrixTests_0.2.3.1.tar.gz","https://cran.r-project.org/src/contrib/matrixTests_0.2.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Functions to perform fast statistical hypothesis tests on rows/columns of matrices. The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use, 3) compatibility with tests implemented in R (like those available in the 'stats' package).","text_prefix":false,"timestamp":1578641971,"version":"0.2.3.1"},"r-maxlik":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=maxLik","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maxLik/maxLik_1.5-2.2.tar.gz","https://cran.r-project.org/src/contrib/maxLik_1.5-2.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions for Maximum Likelihood (ML) estimation and non-linear optimization, and related tools.  It includes a unified way to call different optimizers, and classes and methods to handle the results from the ML viewpoint.  It also includes a number of convenience tools for testing and developing your own models.","text_prefix":false,"timestamp":1539633840,"version":"1.5_2.2"},"r-maxnet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mrmaxent/maxnet","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maxnet/maxnet_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/maxnet_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options.  See the 'Maxent' website <http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.","text_prefix":false,"timestamp":1621951876,"version":"0.1.4"},"r-maxstat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=maxstat","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maxstat/maxstat_0.7-26.tar.gz","https://cran.r-project.org/src/contrib/maxstat_0.7-26.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Maximally selected rank statistics with several p-value approximations.","text_prefix":false,"timestamp":1757678189,"version":"0.7_26"},"r-maybe":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://armcn.github.io/maybe/","home":"https://github.com/armcn/maybe","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/maybe/maybe_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/maybe_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"The maybe type represents the possibility of some value or nothing. It is often used instead of throwing an error or returning `NULL`. The advantage of using a maybe type over `NULL` is that it is both composable and requires the developer to explicitly acknowledge the potential absence of a value, helping to avoid the existence of unexpected behaviour.","text_prefix":false,"timestamp":1758156565,"version":"1.1.0"},"r-mba":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/finleya/MBA","home":"https://finleya.github.io/MBA/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MBA/MBA_0.1-3.tar.gz","https://cran.r-project.org/src/contrib/MBA_0.1-3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by \u00d8yvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).","text_prefix":false,"timestamp":1665321825,"version":"0.1_3"},"r-mbess":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://nd.edu/~kkelley/site/MBESS.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MBESS/MBESS_5.0.1.tar.gz","https://cran.r-project.org/src/contrib/MBESS_5.0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implements methods that useful in designing research studies and analyzing data, with  particular emphasis on methods that are developed for or used within the behavioral,  educational, and social sciences (broadly defined). That being said, many of the methods  implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a  suite of functions for a variety of related topics, such as effect sizes, confidence intervals  for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and  minimum-risk point estimation perspectives), mediation analysis, various properties of  distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally  an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but at this  point MBESS contains methods applicable and used in a wide variety of fields and is an  orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has  greatly benefited from others, see <http://nd.edu/~kkelley/site/MBESS.html> for a detailed  list of those that have contributed and other details.","text_prefix":false,"timestamp":1538309862,"version":"5.0.1"},"r-mbest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/patperry/r-mbest","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mbest/mbest_0.6.1.tar.gz","https://cran.r-project.org/src/contrib/mbest_0.6.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fast moment-based hierarchical model fitting. Implements methods from the papers \"Fast Moment-Based Estimation for Hierarchical Models,\" by Perry (2017) and \"Fitting a Deeply Nested Hierarchical Model to a Large Book Review Dataset Using a Moment-Based Estimator,\" by Zhang, Schmaus, and Perry (2018).","text_prefix":false,"timestamp":1758674429,"version":"0.6.1"},"r-mblm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.r-project.org, http://www.komsta.net/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mblm/mblm_0.12.1.tar.gz","https://cran.r-project.org/src/contrib/mblm_0.12.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides linear models based on Theil-Sen single median and Siegel repeated medians. They are very robust (29 or 50 percent breakdown point, respectively), and if no outliers are present, the estimators are very similar to OLS.","text_prefix":false,"timestamp":1758369542,"version":"0.12.1"},"r-mboost":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/boost-R/mboost","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/mboost/mboost_2.9-14.tar.gz","https://cloud.r-project.org/src/contrib/mboost_2.9-14.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.","text_prefix":false,"timestamp":1649481446,"version":"2.9_14"},"r-mbsp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MBSP","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MBSP/MBSP_5.0.tar.gz","https://cran.r-project.org/src/contrib/MBSP_5.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage priors (MBSP), using the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <doi:10.1016/j.jmva.2018.04.010>.","text_prefix":false,"timestamp":1752002310,"version":"5.0"},"r-mbsts":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mbsts","license":"LGPL-2.1-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mbsts/mbsts_3.0.tar.gz","https://cran.r-project.org/src/contrib/mbsts_3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Multivariate time series regression using dynamic linear models fit by MCMC. See Qiu, Jammalamadaka and Ning (2018) <http://www.jmlr.org/papers/volume19/18-009/18-009.pdf>.","text_prefix":false,"timestamp":1613143355,"version":"3.0"},"r-mc2d":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mc2d","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mc2d/mc2d_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/mc2d_0.2.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous).","text_prefix":false,"timestamp":1540056724,"version":"0.2.2"},"r-mcbiopi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mcbiopi","license":"LGPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mcbiopi/mcbiopi_1.1.7.tar.gz","https://cran.r-project.org/src/contrib/mcbiopi_1.1.7.tar.gz"],"subdirs":["noarch"],"summary":"Computes the prime implicants or a minimal disjunctive normal form for a logic expression presented by a truth table or a logic tree. Has been particularly  developed for logic expressions resulting from a logic regression analysis, i.e. logic expressions typically consisting of up to 16 literals, where the prime implicants  are typically composed of a maximum of 4 or 5 literals.","text_prefix":false,"timestamp":1769541540,"version":"1.1.7"},"r-mccf1":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://bitbucket.org/hoffmanlab/mccf1/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mccf1/mccf1_1.1.tar.gz","https://cran.r-project.org/src/contrib/mccf1_1.1.tar.gz"],"subdirs":["noarch"],"summary":"The MCC-F1 analysis is a method to evaluate the performance of binary classifications. The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth. The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.","text_prefix":false,"timestamp":1758626701,"version":"1.1"},"r-mcgibbsit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mcgibbsit","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mcgibbsit/mcgibbsit_1.2.2.tar.gz","https://cran.r-project.org/src/contrib/mcgibbsit_1.2.2.tar.gz"],"subdirs":["noarch"],"summary":"'mcgibbsit' provides an implementation of Warnes & Raftery's MCGibbsit run-length diagnostic for a set of (not-necessarily independent) MCMC samplers.  It combines the estimate error-bounding approach of the Raftery and Lewis MCMC run length diagnostic with the between verses within chain approach of the Gelman and Rubin MCMC convergence diagnostic.","text_prefix":false,"timestamp":1758115630,"version":"1.2.2"},"r-mcl":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MCL","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MCL/MCL_1.0.tar.gz","https://cran.r-project.org/src/contrib/MCL_1.0.tar.gz"],"subdirs":["noarch"],"summary":"Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.","text_prefix":false,"timestamp":1758585201,"version":"1.0"},"r-mclogit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/melff/mclogit/","home":"https://www.elff.eu/software/mclogit/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mclogit/mclogit_0.9.15.tar.gz","https://cran.r-project.org/src/contrib/mclogit_0.9.15.tar.gz"],"subdirs":["noarch"],"summary":"Provides estimators for multinomial logit models in their conditional logit and baseline logit variants, with or without random effects, with or without overdispersion. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small.","text_prefix":false,"timestamp":1758131217,"version":"0.9.15"},"r-mclust":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://mclust-org.github.io/mclust/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mclust/mclust_6.1.3.tar.gz","https://cran.r-project.org/src/contrib/mclust_6.1.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.","text_prefix":false,"timestamp":1550579236,"version":"6.1.3"},"r-mclustcomp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mclustcomp","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mclustcomp/mclustcomp_0.3.5.tar.gz","https://cran.r-project.org/src/contrib/mclustcomp_0.3.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Given a set of data points, a clustering is defined as a disjoint partition where each pair of sets in a partition has no overlapping elements. This package provides 25 methods that play a role somewhat similar to distance or metric that measures similarity of two clusterings - or partitions. For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.","text_prefix":false,"timestamp":1758584516,"version":"0.3.5"},"r-mcmc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.stat.umn.edu/geyer/mcmc/, https://github.com/cjgeyer/mcmc","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mcmc/mcmc_0.9-8.tar.gz","https://cran.r-project.org/src/contrib/mcmc_0.9-8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC).  Users specify the distribution by an R function that evaluates the log unnormalized density.  Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>, function morph.metrop), which achieves geometric ergodicity by change of variable.","text_prefix":false,"timestamp":1582097893,"version":"0.9_8"},"r-mcmcglmm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MCMCglmm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MCMCglmm/MCMCglmm_2.36.tar.gz","https://cran.r-project.org/src/contrib/MCMCglmm_2.36.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"MCMC Generalised Linear Mixed Models.","text_prefix":false,"timestamp":1643270070,"version":"2.36"},"r-mcmcpack":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/cran/MCMCpack","home":"https://CRAN.R-project.org/package=MCMCpack","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MCMCpack/MCMCpack_1.7-1.tar.gz","https://cran.r-project.org/src/contrib/MCMCpack_1.7-1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.","text_prefix":true,"timestamp":1648702015,"version":"1.7_1"},"r-mcmcprecision":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/danheck/MCMCprecision","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MCMCprecision/MCMCprecision_0.4.2.tar.gz","https://cran.r-project.org/src/contrib/MCMCprecision_0.4.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Estimates the precision of transdimensional Markov chain Monte Carlo  (MCMC) output, which is often used for Bayesian analysis of models with different  dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible  jump MCMC) relies on sampling a discrete model-indicator variable to estimate  the posterior model probabilities. If only few switches occur between the models,  precision may be low and assessment based on the assumption of independent  samples misleading. Based on the observed transition matrix of the indicator  variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2018,  Statistics & Computing) <doi:10.1007/s11222-018-9828-0> draws posterior samples  of the stationary distribution to (a) assess the uncertainty in the estimated  posterior model probabilities and (b) estimate the effective sample size of  the MCMC output.","text_prefix":false,"timestamp":1565425367,"version":"0.4.2"},"r-mco":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://git.p-value.net/p/mco.git","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mco/mco_1.17.tar.gz","https://cran.r-project.org/src/contrib/mco_1.17.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions for multiple criteria optimization using genetic algorithms and related test problems","text_prefix":false,"timestamp":1588584006,"version":"1.17"},"r-mcp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://lindeloev.github.io/mcp/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mcp/mcp_0.3.4.tar.gz","https://cran.r-project.org/src/contrib/mcp_0.3.4.tar.gz"],"subdirs":["noarch"],"summary":"Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindel\u00f8v (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.","text_prefix":false,"timestamp":1758492118,"version":"0.3.4"},"r-mcpan":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MCPAN","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MCPAN/MCPAN_1.1-22.tar.gz","https://cran.r-project.org/src/contrib/MCPAN_1.1-22.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Multiple contrast tests and simultaneous confidence intervals based on normal approximation. With implementations for binomial proportions in a 2xk setting (risk difference and odds ratio), poly-3-adjusted tumour rates, biodiversity indices (multinomial data)  and expected values under lognormal assumption. Approximative power  calculation for multiple contrast tests of binomial and Gaussian data.","text_prefix":false,"timestamp":0,"version":"1.1_22"},"r-mcprogress":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/myles-lewis/mcprogress","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mcprogress/mcprogress_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/mcprogress_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Tools for monitoring progress during parallel processing. Lightweight package which acts as a wrapper around mclapply() and adds a progress bar to it in 'RStudio' or 'Linux' environments. Simply replace your original call to mclapply() with pmclapply(). A progress bar can also be displayed during parallelisation via the 'foreach' package. Also included are functions to safely print messages (including error messages) from within parallelised code, which can be useful for debugging parallelised R code.","text_prefix":false,"timestamp":1765546891,"version":"0.1.1"},"r-mcr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mcr","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mcr/mcr_1.3.3.1.tar.gz","https://cran.r-project.org/src/contrib/mcr_1.3.3.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. It implements the CLSI recommendations (see J. A. Budd et al. (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) for analytical method comparison and bias estimation using patient samples. Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>). A comprehensive overview over the implemented methods and references can be found in the manual pages \"mcr-package\" and \"mcreg\".","text_prefix":false,"timestamp":1758093955,"version":"1.3.3.1"},"r-mda":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mda","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mda/mda_0.5-5.tar.gz","https://cran.r-project.org/src/contrib/mda_0.5-5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, ...","text_prefix":false,"timestamp":1621733283,"version":"0.5_5"},"r-mdmr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/dmcartor/mdmr","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MDMR/MDMR_0.5.2.tar.gz","https://cran.r-project.org/src/contrib/MDMR_0.5.2.tar.gz"],"subdirs":["noarch"],"summary":"Allows users to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size. For details on the method, see McArtor, Lubke, & Bergeman (2017) <doi:10.1007/s11336-016-9527-8>.","text_prefix":false,"timestamp":1758335374,"version":"0.5.2"},"r-mdscore":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://codeberg.org/iagogv/mdscore","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mdscore/mdscore_0.1-4.tar.gz","https://cran.r-project.org/src/contrib/mdscore_0.1-4.tar.gz"],"subdirs":["noarch"],"summary":"A set of functions to obtain modified score test for generalized linear models.","text_prefix":false,"timestamp":1778142875,"version":"0.1_4"},"r-measurements":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=measurements","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/measurements/measurements_1.5.1.tar.gz","https://cran.r-project.org/src/contrib/measurements_1.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Collection of tools to make working with physical measurements easier. Convert between metric and imperial units, or calculate a dimension's unknown value from other dimensions' measurements.","text_prefix":false,"timestamp":1665747979,"version":"1.5.1"},"r-measures":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=measures","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/measures/measures_0.3.tar.gz","https://cran.r-project.org/src/contrib/measures_0.3.tar.gz"],"subdirs":["noarch"],"summary":"Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.","text_prefix":false,"timestamp":1757897762,"version":"0.3"},"r-medflex":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jmpsteen/medflex","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/medflex/medflex_0.6-11.tar.gz","https://cran.r-project.org/src/contrib/medflex_0.6-11.tar.gz"],"subdirs":["noarch"],"summary":"Run flexible mediation analyses using natural effect models as described in Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.","text_prefix":false,"timestamp":1760373915,"version":"0.6_11"},"r-mediation":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://imai.princeton.edu/projects/mechanisms.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mediation/mediation_4.5.1.tar.gz","https://cran.r-project.org/src/contrib/mediation_4.5.1.tar.gz"],"subdirs":["noarch"],"summary":"We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.","text_prefix":false,"timestamp":1758341711,"version":"4.5.1"},"r-medicalrisk":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/patrickmdnet/medicalrisk","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/medicalrisk/medicalrisk_1.3.tar.gz","https://cran.r-project.org/src/contrib/medicalrisk_1.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Generates risk estimates and comorbidity flags from ICD-9-CM codes available in administrative medical datasets. The package supports the Charlson Comorbidity Index, the Elixhauser Comorbidity classification, the Revised Cardiac Risk Index, and the Risk Stratification Index.  Methods are table-based, fast, and use the 'plyr' package, so parallelization is possible for large jobs. Also includes a sample of real ICD-9 data for 100 patients from a publicly available dataset.","text_prefix":false,"timestamp":1534180338,"version":"1.3"},"r-medrxivr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/medrxivr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/medrxivr/medrxivr_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/medrxivr_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"An increasingly important source of health-related bibliographic content are preprints - preliminary versions of research articles that have yet to undergo peer review. The two preprint repositories most relevant to health-related sciences are medRxiv <https://www.medrxiv.org/> and bioRxiv <https://www.biorxiv.org/>, both of which are operated by the Cold Spring Harbor Laboratory. 'medrxivr' provides programmatic access to the 'Cold Spring Harbour Laboratory (CSHL)' API <https://api.biorxiv.org/>, allowing users to easily download medRxiv and bioRxiv preprint metadata (e.g. title, abstract, publication date, author list, etc) into R. 'medrxivr' also provides functions to search the downloaded preprint records using regular expressions and Boolean logic, as well as helper functions that allow users to export their search results to a .BIB file for easy import to a reference manager and to download the full-text PDFs of preprints matching their search criteria.","text_prefix":false,"timestamp":1778619147,"version":"0.1.4"},"r-mefa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/mefa/index.html","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mefa/mefa_3.2-10.tar.gz","https://cran.r-project.org/src/contrib/mefa_3.2-10.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.","text_prefix":false,"timestamp":0,"version":"3.2_10"},"r-memisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/melff/memisc/","home":"https://www.elff.eu/software/memisc/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/memisc/memisc_0.99.31.8.3.tar.gz","https://cran.r-project.org/src/contrib/memisc_0.99.31.8.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.","text_prefix":false,"timestamp":1757871909,"version":"0.99.31.8.3"},"r-memoise":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hadley/memoise","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/memoise/memoise_2.0.1.tar.gz","https://cran.r-project.org/src/contrib/memoise_2.0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Cache the results of a function so that when you call it again with the same arguments it returns the pre-computed value.","text_prefix":false,"timestamp":1536304677,"version":"2.0.1"},"r-memuse":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/shinra-dev/memuse","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/memuse/memuse_4.2-3.tar.gz","https://cran.r-project.org/src/contrib/memuse_4.2-3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"How much ram do you need to store a 100,000 by 100,000 matrix? How much ram is your current R session using? How much ram do you even have? Learn the scintillating answer to these and many more such questions with the 'memuse' package.","text_prefix":false,"timestamp":1563649151,"version":"4.2_3"},"r-mendelianrandomization":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MendelianRandomization","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MendelianRandomization/MendelianRandomization_0.10.0.tar.gz","https://cran.r-project.org/src/contrib/MendelianRandomization_0.10.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.","text_prefix":false,"timestamp":1758981521,"version":"0.10.0"},"r-mertools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=merTools","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/merTools/merTools_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/merTools_1.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides methods for extracting results from mixed-effect model objects fit with the 'lme4' package. Allows construction of prediction intervals efficiently from large scale linear and generalized linear mixed-effects models.","text_prefix":false,"timestamp":1528302247,"version":"1.0.0"},"r-mess":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/ekstroem/MESS","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MESS/MESS_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/MESS_0.6.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book. See Ekstr\u00f8m, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall.","text_prefix":false,"timestamp":1563649123,"version":"0.6.0"},"r-messydates":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=messydates","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/messydates/messydates_1.1.1.tar.gz","https://cloud.r-project.org/src/contrib/messydates_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Contains a set of tools for constructing and coercing into and from the \"mdate\" class. This date class implements ISO 8601-2:2019(E) and allows regular dates to be annotated to express unspecified date components, approximate or uncertain date components, date ranges, and sets of dates. This is useful for describing and analysing temporal information, whether historical or recent, where date precision may vary.","text_prefix":false,"timestamp":1788267651,"version":"1.1.1"},"r-meta":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://meta-analysis-with-r.org","home":"https://github.com/guido-s/meta","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/meta/meta_8.5-0.tar.gz","https://cran.r-project.org/src/contrib/meta_8.5-0.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and R\u00fccker <DOI:10.1007/978-3-319-21416-0>, \"Meta-Analysis with R\" (2015): - fixed effect and random effects meta-analysis; - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); - statistical tests and trim-and-fill method to evaluate bias in meta-analysis; - import data from 'RevMan 5'; - prediction interval, Hartung-Knapp and Paule-Mandel method for random effects model; - cumulative meta-analysis and leave-one-out meta-analysis; - meta-regression; - generalised linear mixed models; - produce forest plot summarising several (subgroup) meta-analyses.","text_prefix":false,"timestamp":1528129110,"version":"8.5_0"},"r-metabma":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/danheck/metaBMA","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metaBMA/metaBMA_0.6.9.tar.gz","https://cran.r-project.org/src/contrib/metaBMA_0.6.9.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Computes the posterior model probabilities for standard meta-analysis models  (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size  as the weighted average of the mean effect size estimates of the random- and  fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, &  Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define  a wide range of non-informative or informative priors for the mean effect size  and the heterogeneity coefficient. Moreover, using pre-compiled Stan models,  meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS)  priors can be fitted and tested. This allows to compute Bayes factors and  perform Bayesian model averaging across random- and fixed-effects meta-analysis  with and without moderators.","text_prefix":false,"timestamp":1766290379,"version":"0.6.9"},"r-metabook":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/guido-s/metabook/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metabook/metabook_0.2-0.tar.gz","https://cran.r-project.org/src/contrib/metabook_0.2-0.tar.gz"],"subdirs":["noarch"],"summary":"Data sets and code supporting the second edition of \"Meta-Analysis with R\"; first edition: Schwarzer, Carpenter, and R\u00fccker (2015) <DOI:10.1007/978-3-319-21416-0>.","text_prefix":false,"timestamp":1780056178,"version":"0.2_0"},"r-metacore":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/atorus-research/metacore","home":"https://atorus-research.github.io/metacore/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metacore/metacore_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/metacore_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow.","text_prefix":false,"timestamp":1776932183,"version":"0.3.0"},"r-metacycle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MetaCycle","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MetaCycle/MetaCycle_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/MetaCycle_1.2.1.tar.gz"],"subdirs":["noarch"],"summary":"There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, 'meta2d' is  suggested, which could incorporates multiple methods from ARSER,  JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For  analyzing time-series datasets with individual information, 'meta3d' is  suggested, which takes use of any one of these three methods to analyze  time-series data individual by individual and gives out integrated values  based on analysis result of each individual.","text_prefix":false,"timestamp":1770009962,"version":"1.2.1"},"r-metadat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/wviechtb/metadat","home":"https://wviechtb.github.io/metadat/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metadat/metadat_1.6-0.tar.gz","https://cran.r-project.org/src/contrib/metadat_1.6-0.tar.gz"],"subdirs":["noarch"],"summary":"A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.","text_prefix":false,"timestamp":1777636709,"version":"1.6_0"},"r-metafor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/wviechtb/metafor","doc_url":"https://wviechtb.github.io/metafor/","home":"https://www.metafor-project.org https://www.wvbauer.com","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metafor/metafor_5.0-1.tar.gz","https://cran.r-project.org/src/contrib/metafor_5.0-1.tar.gz"],"subdirs":["noarch"],"summary":"A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.","text_prefix":false,"timestamp":1622727558,"version":"5.0_1"},"r-metalite":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://merck.github.io/metalite/, https://github.com/Merck/metalite","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metalite/metalite_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/metalite_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"A metadata structure for clinical data analysis and reporting based on Analysis Data Model (ADaM) datasets. The package simplifies clinical analysis and reporting tool development by defining standardized inputs, outputs, and workflow. The package can be used to create analysis and reporting planning grid, mock table, and validated analysis and reporting results based on consistent inputs.","text_prefix":false,"timestamp":1757902785,"version":"0.1.4"},"r-metalite.ae":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://merck.github.io/metalite.ae/, https://github.com/Merck/metalite.ae","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metalite.ae/metalite.ae_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/metalite.ae_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Analyzes adverse events in clinical trials using the 'metalite' data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the adverse events analysis chapters of \"R for Clinical Study Reports and Submission\" by Zhang et al. (2022) <https://r4csr.org/>.","text_prefix":false,"timestamp":1758021886,"version":"0.1.3"},"r-metamix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=metaMix","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metaMix/metaMix_0.3.tar.gz","https://cran.r-project.org/src/contrib/metaMix_0.3.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Resolves complex metagenomic mixtures by analysing deep sequencing data, using a mixture model based approach. The use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species more likely to contribute to the mixture.","text_prefix":false,"timestamp":1758528322,"version":"0.3"},"r-metap":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.dewey.myzen.co.uk/meta/meta.html","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metap/metap_1.1.tar.gz","https://cran.r-project.org/src/contrib/metap_1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and a routine for graphical display.","text_prefix":false,"timestamp":1540853486,"version":"1.1"},"r-metaplus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=metaplus","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metaplus/metaplus_1.0-8.tar.gz","https://cran.r-project.org/src/contrib/metaplus_1.0-8.tar.gz"],"subdirs":["noarch"],"summary":"Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).","text_prefix":false,"timestamp":1622364057,"version":"1.0_8"},"r-metarep":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/IJaljuli/metarep","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metarep/metarep_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/metarep_1.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings.","text_prefix":false,"timestamp":1752001819,"version":"1.2.1"},"r-metarnaseq":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=metaRNASeq","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metaRNASeq/metaRNASeq_1.0.8.tar.gz","https://cran.r-project.org/src/contrib/metaRNASeq_1.0.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implementation of two p-value combination techniques (inverse normal and Fisher methods). 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These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.","text_prefix":false,"timestamp":1604665487,"version":"0.90"},"r-metatools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/pharmaverse/metatools, https://pharmaverse.github.io/metatools/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metatools/metatools_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/metatools_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Uses the metadata information stored in 'metacore' objects to check and build metadata associated columns.","text_prefix":false,"timestamp":1777449316,"version":"0.3.0"},"r-metbrewer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MetBrewer","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MetBrewer/MetBrewer_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/MetBrewer_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Palettes Inspired by Works at the Metropolitan Museum of Art in New York. Currently contains 50 color schemes and checks for colorblind-friendliness of palettes. Colorblind accessibility checked using Jakub Nowosad's package 'colorblindcheck'<https://nowosad.github.io/colorblindcheck/>.","text_prefix":false,"timestamp":1641756892,"version":"0.2.0"},"r-metr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/eliocamp/metR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/metR/metR_0.19.0.tar.gz","https://cloud.r-project.org/src/contrib/metR_0.19.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Many useful functions and extensions for dealing with meteorological data in the tidy data framework. Extends 'ggplot2' for better plotting of scalar and vector fields and provides commonly used analysis methods in the atmospheric sciences.","text_prefix":false,"timestamp":1543258149,"version":"0.19.0"},"r-metrica":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://adriancorrendo.github.io/metrica/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/metrica/metrica_2.1.1.tar.gz","https://cran.r-project.org/src/contrib/metrica_2.1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.","text_prefix":false,"timestamp":1665937328,"version":"2.1.1"},"r-metrics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mfrasco/Metrics","license":"BSD_3_clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Metrics/Metrics_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/Metrics_0.1.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.","text_prefix":false,"timestamp":1543231622,"version":"0.1.4"},"r-mets":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://kkholst.github.io/mets/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mets/mets_1.3.12.tar.gz","https://cran.r-project.org/src/contrib/mets_1.3.12.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and  bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Also contains two-stage binomial modelling that can do pairwise odds-ratio dependence modelling based marginal logistic regression models. This is an alternative to the alternating logistic regression approach (ALR).","text_prefix":false,"timestamp":1630920998,"version":"1.3.12"},"r-metstat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MetStaT","license":"Apache (== 2.0)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MetStaT/MetStaT_1.0.tar.gz","https://cran.r-project.org/src/contrib/MetStaT_1.0.tar.gz"],"subdirs":["noarch"],"summary":"A diverse collection of metabolomics related statistical tools.","text_prefix":false,"timestamp":1758470804,"version":"1.0"},"r-mgc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/neurodata/r-mgc","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mgc/mgc_2.0.2.tar.gz","https://cran.r-project.org/src/contrib/mgc_2.0.2.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64"],"summary":"Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.","text_prefix":false,"timestamp":1769053619,"version":"2.0.2"},"r-mgcv":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mgcv","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mgcv/mgcv_1.9-4.tar.gz","https://cran.r-project.org/src/contrib/mgcv_1.9-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.","text_prefix":false,"timestamp":1666377657,"version":"1.9_4"},"r-mgcviz":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mfasiolo/mgcViz","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mgcViz/mgcViz_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/mgcViz_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.","text_prefix":false,"timestamp":1772088385,"version":"0.2.1"},"r-mglm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MGLM","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/MGLM/MGLM_0.2.3.tar.gz","https://cloud.r-project.org/src/contrib/MGLM_0.2.3.tar.gz"],"subdirs":["noarch"],"summary":"Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.","text_prefix":false,"timestamp":1621888504,"version":"0.2.3"},"r-mgm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://arxiv.org/abs/1510.06871","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mgm/mgm_1.2-15.tar.gz","https://cran.r-project.org/src/contrib/mgm_1.2-15.tar.gz"],"subdirs":["noarch"],"summary":"Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.","text_prefix":false,"timestamp":1626119098,"version":"1.2_15"},"r-mgrs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://gitlab.com/hrbrmstr/mgrs","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/hrbrmstr/mgrs/archive/v0.2.1.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"The 'Military Grid Reference System' ('MGRS') is the geocoordinate standard used by 'NATO' militaries for locating points on the earth. The 'MGRS' is derived from the 'Universal Transverse Mercator' ('UTM') grid system and the universal polar stereographic ('UPS') grid system, but uses a different labeling convention. The 'MGRS' is used for the entire earth. Methods are provided to convert 'MGRS' coordinates to and from other coordinate systems.","text_prefix":false,"timestamp":1758713234,"version":"0.2.1"},"r-mgsub":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mgsub","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mgsub/mgsub_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/mgsub_2.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches).","text_prefix":false,"timestamp":1538311490,"version":"2.0.0"},"r-mhadaptive":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MHadaptive","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MHadaptive/MHadaptive_1.1-8.tar.gz","https://cran.r-project.org/src/contrib/MHadaptive_1.1-8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Performs general Metropolis-Hastings Markov Chain Monte Carlo sampling of a user defined function which returns the un-normalized value (likelihood times prior) of a Bayesian model.  The proposal variance-covariance structure is updated adaptively for efficient mixing when the structure of the target distribution is unknown.  The package also provides some functions for Bayesian inference including Bayesian Credible Intervals (BCI) and Deviance Information Criterion (DIC) calculation.","text_prefix":false,"timestamp":1534107527,"version":"1.1_8"},"r-mhsmm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jaredo/mhsmm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mhsmm/mhsmm_0.4.21.tar.gz","https://cran.r-project.org/src/contrib/mhsmm_0.4.21.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.","text_prefix":false,"timestamp":1758465741,"version":"0.4.21"},"r-mi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.stat.columbia.edu/~gelman/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mi/mi_1.3.1.tar.gz","https://cran.r-project.org/src/contrib/mi_1.3.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.","text_prefix":false,"timestamp":1538220842,"version":"1.3.1"},"r-mice":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/stefvanbuuren/mice/","doc_url":"https://stefvanbuuren.github.io/mice/","home":"https://www.stefvanbuuren.name , https://www.stefvanbuuren.name/fimd/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mice/mice_3.19.0.tar.gz","https://cran.r-project.org/src/contrib/mice_3.19.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.","text_prefix":false,"timestamp":1551955962,"version":"3.19.0"},"r-miceadds":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/alexanderrobitzsch/miceadds","home":"https://sites.google.com/site/alexanderrobitzsch2/software","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/miceadds/miceadds_3.20-10.tar.gz","https://cran.r-project.org/src/contrib/miceadds_3.20-10.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are included. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).","text_prefix":false,"timestamp":1661444918,"version":"3.20_10"},"r-microbenchmark":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/joshuaulrich/microbenchmark/","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/microbenchmark/microbenchmark_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/microbenchmark_1.5.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides infrastructure to accurately measure and compare the execution time of R expressions.","text_prefix":false,"timestamp":1665328438,"version":"1.5.0"},"r-microbiomestat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MicrobiomeStat","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MicrobiomeStat/MicrobiomeStat_1.4.tar.gz","https://cran.r-project.org/src/contrib/MicrobiomeStat_1.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects (Zhou et al. (2022)<doi:10.1186/s13059-022-02655-5>).  The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.","text_prefix":false,"timestamp":1780863230,"version":"1.4"},"r-micropan":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=micropan","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/micropan/micropan_2.1.tar.gz","https://cran.r-project.org/src/contrib/micropan_2.1.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions for computations and visualizations of microbial pan-genomes.","text_prefix":false,"timestamp":1579519717,"version":"2.1"},"r-microseq":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=microseq","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/microseq/microseq_2.1.7.tar.gz","https://cran.r-project.org/src/contrib/microseq_2.1.7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.","text_prefix":false,"timestamp":1591444292,"version":"2.1.7"},"r-micsr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.r-project.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/micsr/micsr_0.1-5.tar.gz","https://cran.r-project.org/src/contrib/micsr_0.1-5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions, data sets and examples for the book: Yves Croissant (2025) \"Microeconometrics with R\", Chapman and Hall/CRC The R Series <doi:10.1201/9781003100263>. The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included.","text_prefix":false,"timestamp":1780415929,"version":"0.1_5"},"r-midasim":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mengyu-he/MIDASim","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MIDASim/MIDASim_2.0.tar.gz","https://cran.r-project.org/src/contrib/MIDASim_2.0.tar.gz"],"subdirs":["noarch"],"summary":"Simulating Realistic Microbiome Data using MIDASim","text_prefix":false,"timestamp":1785486159,"version":"2.0"},"r-mikropml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by Top\u00e7uo\u011flu et al. (2020) <doi:10.1128/mBio.00434-20> with reasonable default options for data preprocessing, hyperparameter tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website <http://www.schlosslab.org/mikropml/> for more information, documentation, and examples.","dev_url":"https://github.com/SchlossLab/mikropml","doc_url":"http://www.schlosslab.org/mikropml/","home":"http://www.schlosslab.org/mikropml/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mikropml/mikropml_1.6.2.tar.gz","https://cran.r-project.org/src/contrib/mikropml_1.6.2.tar.gz"],"subdirs":["noarch"],"summary":"User-Friendly R Package for Supervised Machine Learning Pipelines","text_prefix":false,"timestamp":1636597695,"version":"1.6.2"},"r-mime":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/yihui/mime","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mime/mime_0.13.tar.gz","https://cran.r-project.org/src/contrib/mime_0.13.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems.","text_prefix":false,"timestamp":1624431746,"version":"0.13"},"r-mimic.demo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/eth-mds/mimic-demo","home":"https://physionet.org/content/mimiciii-demo/1.4/","license":"ODbL-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://github.com/eth-mds/mimic-demo/archive/refs/tags/v1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"MIMIC-III (Medical Information Mart for Intensive Care III) is a large, freely-available database comprising deidentified health-related data associated with over 40,000 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (both in and out of hospital). An open-access subset of MIMIC-III, comprising 100 randomly selected patients from the subset of patients who eventually die, is distributed under the Open Database License (ODbL) v1.0 and is provided as R package for convenience.","text_prefix":false,"timestamp":1758466341,"version":"1.0.1"},"r-minerva":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.r-project.org, http://minepy.readthedocs.io/en/latest, http://www.exploredata.net","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/minerva/minerva_1.5.10.tar.gz","https://cran.r-project.org/src/contrib/minerva_1.5.10.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Wrapper for 'minepy' implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of 'minepy' can be found at <http://minepy.readthedocs.io/en/latest>.","text_prefix":false,"timestamp":1622043641,"version":"1.5.10"},"r-minicran":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/andrie/miniCRAN","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/miniCRAN/miniCRAN_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/miniCRAN_0.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Makes it possible to create an internally consistent repository consisting of selected packages from CRAN-like repositories. The user specifies a set of desired packages, and 'miniCRAN' recursively reads the dependency tree for these packages, then downloads only this subset. The user can then install packages from this repository directly, rather than from CRAN.  This is useful in production settings, e.g. server behind a firewall, or remote locations with slow (or zero) Internet access.","text_prefix":false,"timestamp":0,"version":"0.3.2"},"r-miniui":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=miniUI","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/miniUI/miniUI_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/miniUI_0.1.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides UI widget and layout functions for writing Shiny apps that work well on small screens.","text_prefix":false,"timestamp":1538342190,"version":"0.1.2"},"r-minpack.lm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=minpack.lm","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/minpack.lm/minpack.lm_1.2-4.tar.gz","https://cran.r-project.org/src/contrib/minpack.lm_1.2-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.","text_prefix":false,"timestamp":1665488695,"version":"1.2_4"},"r-minqa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://optimizer.r-forge.r-project.org","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/minqa/minqa_1.2.8.tar.gz","https://cran.r-project.org/src/contrib/minqa_1.2.8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.","text_prefix":false,"timestamp":1665481789,"version":"1.2.8"},"r-minty":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/gesistsa/minty","home":"https://gesistsa.github.io/minty/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/minty/minty_0.0.6.tar.gz","https://cran.r-project.org/src/contrib/minty_0.0.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Port the type guesser from 'readr' (so-called 'readr' first edition parsing engine, now superseded by 'vroom').","text_prefix":false,"timestamp":1769274502,"version":"0.0.6"},"r-mipfp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jojo-/mipfp","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/mipfp/mipfp_3.2.3.tar.gz","https://cloud.r-project.org/src/contrib/mipfp_3.2.3.tar.gz"],"subdirs":["noarch"],"summary":"An implementation of the iterative proportional fitting (IPFP), maximum likelihood, minimum chi-square and weighted least squares procedures for updating a N-dimensional array with respect to given target marginal distributions (which, in turn can be multidimensional). The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions.","text_prefix":false,"timestamp":1787402596,"version":"3.2.3"},"r-mirai":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/shikokuchuo/mirai/","home":"https://shikokuchuo.net/mirai/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mirai/mirai_2.7.2.tar.gz","https://cran.r-project.org/src/contrib/mirai_2.7.2.tar.gz"],"subdirs":["noarch"],"summary":"Lightweight parallel code execution and distributed computing. Designed for simplicity, a 'mirai' evaluates an R expression asynchronously, on local or network resources, resolving automatically upon completion. Efficient scheduling over fast inter-process communications or secure TLS connections over TCP/IP, built on 'nanonext' and 'NNG' (Nanomsg Next Gen).","text_prefix":false,"timestamp":1784541495,"version":"2.7.2"},"r-mirt":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/philchalmers/mirt, https://github.com/philchalmers/mirt/wiki, https://groups.google.com/forum/#!forum/mirt-package","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/mirt/mirt_1.47.tar.gz","https://cloud.r-project.org/src/contrib/mirt_1.47.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>).  Exploratory and confirmatory models can be estimated with quadrature (EM)  or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier analyses are available for modeling item testlets. Multiple group analysis and mixed effects designs also are available for detecting differential item and test functioning as well as modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, and several other discrete latent variable models, including mixture and zero-inflated response models,  are supported.","text_prefix":false,"timestamp":0,"version":"1.47"},"r-mirtcat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/philchalmers/mirtCAT, https://github.com/philchalmers/mirtCAT/wiki, https://groups.google.com/forum/#!forum/mirt-package","license":"GPL3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mirtCAT/mirtCAT_1.12.2.tar.gz","https://cran.r-project.org/src/contrib/mirtCAT_1.12.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides tools to generate an HTML interface for creating adaptive and non-adaptive educational and psychological tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., \"shadow testing\") are supported for tests which contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying the behavior of computerized adaptive test banks.","text_prefix":false,"timestamp":1759150669,"version":"1.12.2"},"r-misc3d":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=misc3d","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/misc3d/misc3d_0.9-2.tar.gz","https://cran.r-project.org/src/contrib/misc3d_0.9-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of miscellaneous 3d plots, including isosurfaces.","text_prefix":false,"timestamp":1539809728,"version":"0.9_2"},"r-miscf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=miscF","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/miscF/miscF_0.1-5.tar.gz","https://cran.r-project.org/src/contrib/miscF_0.1-5.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Various functions for random number generation, density  estimation, classification, curve fitting, and spatial  data analysis.","text_prefix":false,"timestamp":1564181219,"version":"0.1_5"},"r-misctools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.micEcon.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/miscTools/miscTools_0.6-30.tar.gz","https://cran.r-project.org/src/contrib/miscTools_0.6-30.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Miscellaneous small tools and utilities. Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. Other tools facilitate the work with regression models, e.g. extracting the standard errors, obtaining the number of (estimated) parameters, or calculating R-squared values.","text_prefix":false,"timestamp":1539355684,"version":"0.6_30"},"r-missforest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.r-project.org, https://github.com/stekhoven/missForest","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/missForest/missForest_1.6.1.tar.gz","https://cran.r-project.org/src/contrib/missForest_1.6.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to  save computation time.","text_prefix":false,"timestamp":1539473608,"version":"1.6.1"},"r-missmda":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://factominer.free.fr/missMDA/index.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/missMDA/missMDA_1.22.tar.gz","https://cran.r-project.org/src/contrib/missMDA_1.22.tar.gz"],"subdirs":["noarch"],"summary":"Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.","text_prefix":false,"timestamp":1574246504,"version":"1.22"},"r-missranger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mayer79/missRanger","home":"https://mayer79.github.io/missRanger/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/missRanger/missRanger_2.6.1.tar.gz","https://cran.r-project.org/src/contrib/missRanger_2.6.1.tar.gz"],"subdirs":["noarch"],"summary":"Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the lightning fast random forest package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable).  Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow, e.g., to do multiple imputation when repeating the call to missRanger(). Out-of-sample application is supported as well.","text_prefix":false,"timestamp":1783933708,"version":"2.6.1"},"r-mitml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mitml","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mitml/mitml_0.4-5.tar.gz","https://cran.r-project.org/src/contrib/mitml_0.4-5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides tools for multiple imputation of missing data in multilevel modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo', and several functions for visualization, data management and the analysis  of multiply imputed data sets.","text_prefix":false,"timestamp":1530427529,"version":"0.4_5"},"r-mitools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mitools","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mitools/mitools_2.4.tar.gz","https://cran.r-project.org/src/contrib/mitools_2.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tools to perform analyses and combine results from multiple-imputation datasets.","text_prefix":false,"timestamp":0,"version":"2.4"},"r-mixedcca":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mixedCCA","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mixedCCA/mixedCCA_1.6.3.tar.gz","https://cran.r-project.org/src/contrib/mixedCCA_1.6.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.","text_prefix":false,"timestamp":1659689021,"version":"1.6.3"},"r-mixlm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/khliland/mixlm/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mixlm/mixlm_1.4.3.tar.gz","https://cran.r-project.org/src/contrib/mixlm_1.4.3.tar.gz"],"subdirs":["noarch"],"summary":"The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm() and glm(). A collection of text-book statistics for higher education is also included, e.g. modifications of the functions lm(), glm() and associated summaries from the package 'stats'.","text_prefix":false,"timestamp":1757960180,"version":"1.4.3"},"r-mixomics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.mixOmics.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mixOmics/mixOmics_6.3.2.tar.gz","https://cran.r-project.org/src/contrib/mixOmics_6.3.2.tar.gz"],"subdirs":["linux-64","noarch","win-64"],"summary":"Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.","text_prefix":false,"timestamp":1722737356,"version":"6.3.2"},"r-mixopt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/CollinErickson/mixopt","license":"LGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mixopt/mixopt_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/mixopt_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Mixed variable optimization for non-linear functions. Can optimize function whose inputs are a combination of continuous, ordered, and unordered variables.","text_prefix":false,"timestamp":1777371868,"version":"0.1.3"},"r-mixsmsn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mixsmsn","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mixsmsn/mixsmsn_1.1-12.tar.gz","https://cran.r-project.org/src/contrib/mixsmsn_1.1-12.tar.gz"],"subdirs":["noarch"],"summary":"Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.","text_prefix":false,"timestamp":1621920293,"version":"1.1_12"},"r-mixsqp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/stephenslab/mixsqp","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mixsqp/mixsqp_0.3-54.tar.gz","https://cran.r-project.org/src/contrib/mixsqp_0.3-54.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides optimization algorithms based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithms are expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function \"KWDual\" in the 'REBayes' package), and they are expected to arrive at solutions more quickly in large data sets. The algorithms are described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2012) <arXiv:1806.01412>.","text_prefix":false,"timestamp":1588603225,"version":"0.3_54"},"r-mixtools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/dsy109/mixtools","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mixtools/mixtools_2.0.0.1.tar.gz","https://cran.r-project.org/src/contrib/mixtools_2.0.0.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).","text_prefix":false,"timestamp":1563541545,"version":"2.0.0.1"},"r-mlapi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mlapi","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mlapi/mlapi_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/mlapi_0.1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides 'R6' abstract classes for building machine learning models  with 'scikit-learn' like API. <http://scikit-learn.org/> is a popular module  for 'Python' programming language which design became de facto a standard  in industry for machine learning tasks.","text_prefix":false,"timestamp":1538334280,"version":"0.1.1"},"r-mlbench":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mlbench","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mlbench/mlbench_2.1-11.tar.gz","https://cran.r-project.org/src/contrib/mlbench_2.1-11.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.","text_prefix":false,"timestamp":1588769430,"version":"2.1_11"},"r-mlecens":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://stat.ethz.ch/~maathuis/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MLEcens/MLEcens_0.1-7.1.tar.gz","https://cran.r-project.org/src/contrib/MLEcens_0.1-7.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This package contains functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly.  To be more precise, we consider the situation where we observe a set of rectangles that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles.  The methods can also be used for univariate censored data (see data set 'cosmesis'), and for censored data with competing risks (see data set 'menopause').  We also provide functions to visualize the observed data and the MLE.","text_prefix":false,"timestamp":1636391726,"version":"0.1_7.1"},"r-mlergm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mlergm","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mlergm/mlergm_0.8.1.tar.gz","https://cran.r-project.org/src/contrib/mlergm_0.8.1.tar.gz"],"subdirs":["noarch"],"summary":"Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. 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These CI methods are implemented as 'mlr3' measures, enabling the evaluation of individual algorithms on specific tasks as well as the comparison of different learning algorithms.","text_prefix":false,"timestamp":1766581078,"version":"0.2.1"},"r-mlr3learners":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://mlr3learners.mlr-org.com, https://github.com/mlr-org/mlr3learners","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mlr3learners/mlr3learners_0.15.1.tar.gz","https://cran.r-project.org/src/contrib/mlr3learners_0.15.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64"],"summary":"Recommended Learners for 'mlr3'. Extends 'mlr3' and 'mlr3proba' with interfaces to essential machine learning packages on CRAN.  This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.","text_prefix":false,"timestamp":1785017990,"version":"0.15.1"},"r-mlr3mbo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mlr-org/mlr3mbo","home":"https://mlr3mbo.mlr-org.com","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mlr3mbo/mlr3mbo_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/mlr3mbo_0.3.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. 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See <http://www.sdeevi.com/modes_package> for examples and discussion.","text_prefix":false,"timestamp":1687123403,"version":"0.7.0"},"r-modeva":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://modeva.r-forge.r-project.org/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/modEvA/modEvA_3.46.tar.gz","https://cloud.r-project.org/src/contrib/modEvA_3.46.tar.gz"],"subdirs":["noarch"],"summary":"Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. 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'momr' is the core package and contains routines for biomarker identification and exploration. Developed since the beginning of field, 'momr' has evolved and is structured around the different modules  such as preprocessing, analysis, vizualisation, etc. See package help for more information.","text_prefix":false,"timestamp":1758651745,"version":"1.1"},"r-mongolite":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/jeroen/mongolite/","doc_url":"https://jeroen.github.io/mongolite/","home":"https://jeroen.r-universe.dev/mongolite","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mongolite/mongolite_4.1.0.tar.gz","https://cran.r-project.org/src/contrib/mongolite_4.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS.","text_prefix":false,"timestamp":1784602982,"version":"4.1.0"},"r-monobin":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/andrija-djurovic/monobin","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/monobin/monobin_0.2.4.tar.gz","https://cran.r-project.org/src/contrib/monobin_0.2.4.tar.gz"],"subdirs":["noarch"],"summary":"Performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) development. All functions handle both binary and continuous target variable. Functions that use isotonic regression in the first stage of binning process have an additional feature for correction of minimum percentage of observations and minimum target rate per bin. Additionally, monotonic trend can be identified based on raw data or, if known in advance, forced by functions' argument. Missing values and other possible special values are treated separately from so-called complete cases.","text_prefix":false,"timestamp":1758042031,"version":"0.2.4"},"r-monopoly":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MonoPoly","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MonoPoly/MonoPoly_0.3-10.tar.gz","https://cran.r-project.org/src/contrib/MonoPoly_0.3-10.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions for fitting monotone polynomials to data. Detailed discussion of the methodologies used can be found in Murray, Mueller and Turlach (2013) <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and Turlach (2016) <doi:10.1080/00949655.2016.1139582>.","text_prefix":false,"timestamp":1774276937,"version":"0.3_10"},"r-monotone":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=monotone","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/monotone/monotone_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/monotone_0.1.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The monotone package contains a fast up-and-down-blocks implementation for the pool-adjacent-violators algorithm for simple linear ordered monotone regression, including two spin-off functions for unimodal and bivariate monotone regression (see <doi:10.18637/jss.v102.c01>).","text_prefix":false,"timestamp":1758572896,"version":"0.1.2"},"r-moonbook":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/cardiomoon/moonBook","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/moonBook/moonBook_0.3.1.tar.gz","https://cran.r-project.org/src/contrib/moonBook_0.3.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Several analysis-related functions for the book entitled \"R statistics and graph for medical articles\" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions.","text_prefix":false,"timestamp":1538222200,"version":"0.3.1"},"r-mopa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SantanderMetGroup/mopa/wiki","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SantanderMetGroup/mopa/archive/refs/tags/v1.1.2.tar.gz","subdirs":["noarch"],"summary":"Transferable Species Distribution Modeling and Pseudo-absence data generation.","text_prefix":false,"timestamp":1652794606,"version":"1.1.2"},"r-moreparty":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=moreparty","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/moreparty/moreparty_0.4.2.tar.gz","https://cran.r-project.org/src/contrib/moreparty_0.4.2.tar.gz"],"subdirs":["noarch"],"summary":"Additions to 'party' package : tools for the interpretation of forests (surrogate trees, prototypes, etc.), feature selection (see Gregorutti et al (2017) <arXiv:1310.5726>, Hapfelmeier and Ulm (2013) <doi:10.1016/j.csda.2012.09.020>, Altmann et al (2010) <doi:10.1093/bioinformatics/btq134>) and parallelized versions of conditional forest and variable importance functions.","text_prefix":false,"timestamp":1665871714,"version":"0.4.2"},"r-mosaic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ProjectMOSAIC/mosaic, https://www.mosaic-web.org/mosaic/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mosaic/mosaic_1.10.2.tar.gz","https://cran.r-project.org/src/contrib/mosaic_1.10.2.tar.gz"],"subdirs":["noarch"],"summary":"Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling.  Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.","text_prefix":false,"timestamp":1661557920,"version":"1.10.2"},"r-mosaiccalc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ProjectMOSAIC/mosaicCalc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mosaicCalc/mosaicCalc_0.6.4.tar.gz","https://cran.r-project.org/src/contrib/mosaicCalc_0.6.4.tar.gz"],"subdirs":["noarch"],"summary":"Software to support the introductory *MOSAIC Calculus* textbook <https://www.mosaic-web.org/MOSAIC-Calculus/>), one of many data- and modeling-oriented educational resources developed by Project MOSAIC (<https://www.mosaic-web.org/>). Provides symbolic and numerical differentiation and integration, as well as support for applied linear algebra (for data science), and differential equations/dynamics. Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc. The software is suitable for general use, but intended mainly for teaching calculus.","text_prefix":false,"timestamp":1635624186,"version":"0.6.4"},"r-mosaiccore":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ProjectMOSAIC/mosaicCore","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mosaicCore/mosaicCore_0.9.5.tar.gz","https://cran.r-project.org/src/contrib/mosaicCore_0.9.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Common utilities used in other MOSAIC-family packages are  collected here.","text_prefix":false,"timestamp":1537830150,"version":"0.9.5"},"r-mosaicdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ProjectMOSAIC/mosaicData","home":"https://CRAN.R-project.org/package=mosaicData","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mosaicData/mosaicData_0.20.4.tar.gz","https://cran.r-project.org/src/contrib/mosaicData_0.20.4.tar.gz"],"subdirs":["noarch"],"summary":"Data sets from Project MOSAIC (<http://mosaic-web.org>) used to teach mathematics, statistics, computation and modeling.  Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.","text_prefix":false,"timestamp":1621922645,"version":"0.20.4"},"r-mote":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/doomlab/MOTE","doc_url":"https://www.aggieerin.com/shiny-server/","home":"https://CRAN.R-project.org/package=MOTE","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MOTE/MOTE_1.2.2.tar.gz","https://cran.r-project.org/src/contrib/MOTE_1.2.2.tar.gz"],"subdirs":["noarch"],"summary":"Measure of the Effect ('MOTE') is an effect size calculator, including a wide variety of effect sizes in the mean differences family (all versions of d) and the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central confidence intervals for each effect size, relevant test statistics, and output for reporting in APA Style (American Psychological Association, 2010, <ISBN:1433805618>) with 'LaTeX'. In research, an over-reliance on p-values may conceal the fact that a study is under-powered (Halsey, Curran-Everett, Vowler, & Drummond, 2015 <doi:10.1038/nmeth.3288>). A test may be statistically significant, yet practically inconsequential (Fritz, Scherndl, & K\u00fchberger, 2012 <doi:10.1177/0959354312436870>). Although the American Psychological Association has long advocated for the inclusion of effect sizes (Wilkinson & American Psychological Association Task Force on Statistical Inference, 1999 <doi:10.1037/0003-066X.54.8.594>), the vast majority of peer-reviewed, published academic studies stop short of reporting effect sizes and confidence intervals (Cumming, 2013, <doi:10.1177/0956797613504966>). 'MOTE' simplifies the use and interpretation of effect sizes and confidence intervals. For more information, visit <https://www.aggieerin.com/shiny-server>.","text_prefix":false,"timestamp":1765789830,"version":"1.2.2"},"r-mpath":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/zhuwang46/mpath","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mpath/mpath_0.4-2.27.tar.gz","https://cran.r-project.org/src/contrib/mpath_0.4-2.27.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2024) <doi:10.1111/anzs.12409>.","text_prefix":false,"timestamp":1784102073,"version":"0.4_2.27"},"r-mpinet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MPINet","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MPINet/MPINet_1.0.tar.gz","https://cran.r-project.org/src/contrib/MPINet_1.0.tar.gz"],"subdirs":["noarch"],"summary":"(1) Our system provides a network-based strategies for metabolite pathway identification.(2) The MPINet can support the identification of pathways using Hypergeometric test based on metabolite set. (3)MPINet can support pathways from multiple databases.","text_prefix":false,"timestamp":1758280131,"version":"1.0"},"r-mpm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://mpm.r-forge.r-project.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mpm/mpm_1.0-23.tar.gz","https://cran.r-project.org/src/contrib/mpm_1.0-23.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Exploratory graphical analysis of multivariate data, specifically gene expression data with different projection methods: principal component analysis, correspondence analysis, spectral map analysis.","text_prefix":false,"timestamp":1540181696,"version":"1.0_23"},"r-mpmi":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://r-forge.r-project.org/projects/mpmi/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mpmi/mpmi_0.43.2.1.tar.gz","https://cran.r-project.org/src/contrib/mpmi_0.43.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. Uses a nonparametric bias correction giving Bias Corrected Mutual Information (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to large genomic datasets.","text_prefix":false,"timestamp":1543099780,"version":"0.43.2.1"},"r-mpoly":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dkahle/mpoly","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mpoly/mpoly_1.1.2.tar.gz","https://cran.r-project.org/src/contrib/mpoly_1.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Symbolic computing with multivariate polynomials in R.","text_prefix":false,"timestamp":1568822519,"version":"1.1.2"},"r-mppa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mppa","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mppa/mppa_1.0.tar.gz","https://cran.r-project.org/src/contrib/mppa_1.0.tar.gz"],"subdirs":["noarch"],"summary":"A procedure to test for dependence between point processes on the real line, e.g. causal dependence, correlation, inhibition or anti-correlation. The package also provides a number of utilities for plotting simultaneous point processes, and combining p-values.","text_prefix":false,"timestamp":1758651264,"version":"1.0"},"r-mqtl":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mQTL","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mQTL/mQTL_1.0.tar.gz","https://cran.r-project.org/src/contrib/mQTL_1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"mQTL provides a complete QTL analysis pipeline for metabolomic data.  Distinctive features include normalisation using PQN approach, peak alignment   using RSPA approach, dimensionality reduction using SRV approach and finally  QTL mapping using R/qtl package.","text_prefix":false,"timestamp":1758750640,"version":"1.0"},"r-mratios":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mratios","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mratios/mratios_1.4.4.tar.gz","https://cran.r-project.org/src/contrib/mratios_1.4.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.","text_prefix":false,"timestamp":0,"version":"1.4.4"},"r-mrfdepth":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/PSegaert/mrfDepth","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mrfDepth/mrfDepth_1.0.17.tar.gz","https://cran.r-project.org/src/contrib/mrfDepth_1.0.17.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools to compute depth measures and implementations of related tasks such as outlier detection, data exploration and classification of multivariate, regression and functional data.","text_prefix":false,"timestamp":1758690996,"version":"1.0.17"},"r-mrgsolve":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/metrumresearchgroup/mrgsolve","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mrgsolve/mrgsolve_2.0.1.tar.gz","https://cran.r-project.org/src/contrib/mrgsolve_2.0.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.","text_prefix":false,"timestamp":1652770048,"version":"2.0.1"},"r-mrmlm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mrMLM","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mrMLM/mrMLM_5.0.1.tar.gz","https://cran.r-project.org/src/contrib/mrMLM_5.0.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes, and all the nonzero effects were further identified by likelihood ratio test for significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, <doi:10.1093/bib/bbw145>), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, <doi:10.1016/j.molp.2022.02.012>).","text_prefix":false,"timestamp":1758690971,"version":"5.0.1"},"r-mrmre":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://cran.r-project.org/package=mRMRe","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/mRMRe/mRMRe_2.1.3.1.tar.gz","https://cloud.r-project.org/src/contrib/mRMRe_2.1.3.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique.","text_prefix":false,"timestamp":1621949370,"version":"2.1.3.1"},"r-msar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=msaR","license":"BSL-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/msaR/msaR_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/msaR_0.6.0.tar.gz"],"subdirs":["noarch"],"summary":"Visualizes multiple sequence alignments dynamically within the Shiny web application framework.","text_prefix":false,"timestamp":1758683205,"version":"0.6.0"},"r-msbox":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/YonghuiDong/MSbox","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MSbox/MSbox_1.4.8.tar.gz","https://cran.r-project.org/src/contrib/MSbox_1.4.8.tar.gz"],"subdirs":["noarch"],"summary":"Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks, examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) ion sources.","text_prefix":false,"timestamp":1758623070,"version":"1.4.8"},"r-mschart":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://ardata-fr.github.io/officeverse/, https://ardata-fr.github.io/mschart/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mschart/mschart_0.5.1.tar.gz","https://cran.r-project.org/src/contrib/mschart_0.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Create native charts for 'Microsoft PowerPoint' and 'Microsoft Word' documents. These can then be edited and annotated. 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The optimal model can be selected by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC.","text_prefix":false,"timestamp":1545044859,"version":"1.3.5"},"r-msigdbr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/igordot/msigdbr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/msigdbr/msigdbr_26.1.1.tar.gz","https://cloud.r-project.org/src/contrib/msigdbr_26.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides the 'Molecular Signatures Database' (MSigDB) gene sets typically used with the 'Gene Set Enrichment Analysis' (GSEA) software (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>, Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>) in a standard R data frame with key-value pairs. Included are the original human gene symbols and Entrez IDs as well as the equivalents for various frequently studied model organisms such as mouse, rat, pig, fly, and yeast.","text_prefix":false,"timestamp":1601628994,"version":"26.1.1"},"r-mskcc.oncotree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://maialab.org/mskcc.oncotree/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mskcc.oncotree/mskcc.oncotree_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/mskcc.oncotree_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Programmatic access to 'OncoTree' API <http://oncotree.mskcc.org/>. Get access to tumor main types, identifiers and utility routines to map across to other tumor classification systems.","text_prefix":false,"timestamp":1654850479,"version":"0.1.1"},"r-msm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/chjackson/msm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/msm/msm_1.8.2.tar.gz","https://cran.r-project.org/src/contrib/msm_1.8.2.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Functions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data.  Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.","text_prefix":false,"timestamp":1563541161,"version":"1.8.2"},"r-msos":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/coatless/msos, https://coatless.github.io/msos/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/msos/msos_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/msos_1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Multivariate Analysis methods and data sets used in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>. This also serves as a companion package for the STAT 571: Multivariate Analysis course offered by the Department of Statistics at the University of Illinois at Urbana-Champaign ('UIUC').","text_prefix":false,"timestamp":1758577262,"version":"1.2.0"},"r-msqc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MSQC","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MSQC/MSQC_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/MSQC_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"This is a toolkit for multivariate process monitoring. It computes several multivariate control charts e.g. Hotelling, Chi-squared, MEWMA, MCUSUM and Generalized Variance. Ten didactic datasets are included. It includes some techniques for assessing multivariate normality e.g. Mardia's, Royston's and Henze-Zirkler's tests. Please, see the NEWS file for the latest changes in the package.","text_prefix":false,"timestamp":1622187548,"version":"1.1.0"},"r-mstate":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.lumc.nl/org/bds/research/medische-statistiek/survival-analysis/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mstate/mstate_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/mstate_0.3.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.","text_prefix":false,"timestamp":1622072356,"version":"0.3.3"},"r-mtlr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/haiderstats/MTLR","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MTLR/MTLR_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/MTLR_0.2.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An implementation of Multi-Task Logistic Regression (MTLR) for R. 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This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.","text_prefix":false,"timestamp":1758733074,"version":"1.0.3"},"r-multcomp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://codeberg.org/thothorn/multcomp","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/multcomp/multcomp_1.4-32.tar.gz","https://cloud.r-project.org/src/contrib/multcomp_1.4-32.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including  linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book \"Multiple Comparisons Using R\" (Bretz, Hothorn,  Westfall, 2010, CRC Press).","text_prefix":false,"timestamp":1536708924,"version":"1.4_32"},"r-multcompview":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://lselzer.github.io/multcompview/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multcompView/multcompView_0.1-12.tar.gz","https://cran.r-project.org/src/contrib/multcompView_0.1-12.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Convert a logical vector or a vector of p-values or a correlation, difference, or distance matrix into a display identifying the pairs for which the differences were not significantly different. Designed for use in conjunction with the output of functions like TukeyHSD, dist (stats), simint, simtest, csimint, csimtest (multcomp), friedmanmc, kruskalmc (pgirmess).","text_prefix":false,"timestamp":1538023545,"version":"0.1_12"},"r-multiapply":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://earth.bsc.es/gitlab/ces/multiApply","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multiApply/multiApply_2.1.5.tar.gz","https://cran.r-project.org/src/contrib/multiApply_2.1.5.tar.gz"],"subdirs":["noarch"],"summary":"The base apply function and its variants, as well as the related functions in the 'plyr' package, typically apply user-defined functions to a single argument (or a list of vectorized arguments in the case of mapply). The 'multiApply' package extends this paradigm with its only function, Apply, which efficiently applies functions taking one or a list of multiple unidimensional or multidimensional arrays (or combinations thereof) as input. The input arrays can have different numbers of dimensions as well as different dimension lengths, and the applied function can return one or a list of unidimensional or multidimensional arrays as output. This saves development time by preventing the R user from writing often error-prone and memory-inefficient loops dealing with multiple complex arrays. Also, a remarkable feature of Apply is the transparent use of multi-core through its parameter 'ncores'. In contrast to the base apply function, this package suggests the use of 'target dimensions' as opposite to the 'margins' for specifying the dimensions relevant to the function to be applied.","text_prefix":false,"timestamp":1611268571,"version":"2.1.5"},"r-multiblock":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/khliland/multiblock/","home":"https://khliland.github.io/multiblock/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multiblock/multiblock_0.8.11.tar.gz","https://cran.r-project.org/src/contrib/multiblock_0.8.11.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions and datasets to support Smilde, N\u00e6s and Liland (2021, ISBN: 978-1-119-60096-1) \"Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences\". This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications.","text_prefix":false,"timestamp":1737641640,"version":"0.8.11"},"r-multica":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=multiCA","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multiCA/multiCA_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/multiCA_1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Implements a generalization of the Cochran-Armitage trend test to multinomial data. In addition to an overall test, multiple testing adjusted p-values for trend in individual outcomes and power calculation is available.","text_prefix":false,"timestamp":1758057717,"version":"1.2.0"},"r-multiclasspairs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/NourMarzouka/multiclassPairs","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multiclassPairs/multiclassPairs_0.4.3.tar.gz","https://cran.r-project.org/src/contrib/multiclassPairs_0.4.3.tar.gz"],"subdirs":["noarch"],"summary":"A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret.  For complex problems RF performs better.  Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...).  Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model.  We provide a detailed description with each function in this package to explain the filtration and training methodology in each line.","text_prefix":false,"timestamp":1605872633,"version":"0.4.3"},"r-multicool":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=multicool","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multicool/multicool_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/multicool_1.0.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. (2009)  <DOI:10.1145/1496770.1496877> Loopless Generation of Multiset Permutations by Prefix Shifts. Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from <http://tamivox.org/dave/multinomial/code.html> and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here <http://jeromekelleher.net/tag/integer-partitions.html>. The C++ code and Python code are distributed without conditions.","text_prefix":false,"timestamp":1621641973,"version":"1.0.1"},"r-multidplyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tidyverse/multidplyr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multidplyr/multidplyr_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/multidplyr_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"Partition a data frame across multiple worker processes to provide simple multicore parallelism.","text_prefix":false,"timestamp":1665741728,"version":"0.1.4"},"r-multigroup":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=multigroup","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multigroup/multigroup_0.4.5.tar.gz","https://cran.r-project.org/src/contrib/multigroup_0.4.5.tar.gz"],"subdirs":["noarch"],"summary":"Multivariate analysis methods including principal component analysis, partial least square regression, and multiblock analysis to describe, summarize, and visualize data with a group structure.","text_prefix":false,"timestamp":1758673509,"version":"0.4.5"},"r-multilevel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/multilevel/index.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multilevel/multilevel_2.8.tar.gz","https://cran.r-project.org/src/contrib/multilevel_2.8.tar.gz"],"subdirs":["noarch"],"summary":"Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. Third, simulations for estimating power and generating multilevel data. Fourth, miscellaneous functions for estimating reliability and performing simple calculations and data transformations.","text_prefix":false,"timestamp":1773969485,"version":"2.8"},"r-multilevelmod":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tidymodels/multilevelmod, http://multilevelmod.tidymodels.org/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multilevelmod/multilevelmod_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/multilevelmod_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Bindings for hierarchical regression models for use with the 'parsnip' package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and mixed-effect models (Pinheiro and Bates) <doi:10.1007/978-1-4419-0318-1_1>.","text_prefix":false,"timestamp":1758460918,"version":"1.0.0"},"r-multimode":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://arxiv.org/abs/1803.00472/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multimode/multimode_1.5.tar.gz","https://cran.r-project.org/src/contrib/multimode_1.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2018 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques.","text_prefix":false,"timestamp":1603230993,"version":"1.5"},"r-multinet":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=multinet","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multinet/multinet_4.3.4.tar.gz","https://cran.r-project.org/src/contrib/multinet_4.3.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions for the creation/generation and analysis of multilayer social networks <doi:10.18637/jss.v098.i08>.","text_prefix":false,"timestamp":1777315741,"version":"4.3.4"},"r-multipanelfigure":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=multipanelfigure","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multipanelfigure/multipanelfigure_2.1.6.tar.gz","https://cran.r-project.org/src/contrib/multipanelfigure_2.1.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Tools to create a layout for figures made of multiple panels, and to fill the panels with base, 'lattice', 'ggplot2' and 'ComplexHeatmap' plots, grobs, as well as content from all image formats supported by 'ImageMagick' (accessed through 'magick').","text_prefix":false,"timestamp":1538917722,"version":"2.1.6"},"r-multiroc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=multiROC","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multiROC/multiROC_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/multiROC_1.1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Tools to solve real-world problems with multiple classes classifications by computing the areas under ROC and PR curve via micro-averaging and macro-averaging. The vignettes of this package can be found via <https://github.com/WandeRum/multiROC>. The methodology is described in V. Van Asch (2013) <https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et al. (2011) <http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.","text_prefix":false,"timestamp":0,"version":"1.1.1"},"r-multitaper":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/krahim/multitaper/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multitaper/multitaper_1.0-17.tar.gz","https://cran.r-project.org/src/contrib/multitaper_1.0-17.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.","text_prefix":false,"timestamp":1542329702,"version":"1.0_17"},"r-multiway":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=multiway","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/multiway/multiway_1.0-7.tar.gz","https://cran.r-project.org/src/contrib/multiway_1.0-7.tar.gz"],"subdirs":["noarch"],"summary":"Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.","text_prefix":false,"timestamp":1544647076,"version":"1.0_7"},"r-mumin":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MuMIn","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MuMIn/MuMIn_1.48.19.tar.gz","https://cran.r-project.org/src/contrib/MuMIn_1.48.19.tar.gz"],"subdirs":["noarch"],"summary":"Tools for performing model selection and model averaging. Automated model selection through subsetting the maximum model, with optional constraints for model inclusion. Model parameter and prediction averaging based on model weights derived from information criteria (AICc and alike) or custom model weighting schemes.","text_prefix":false,"timestamp":1548923682,"version":"1.48.19"},"r-munsell":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=munsell, https://github.com/cwickham/munsell/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/munsell/munsell_0.5.1.tar.gz","https://cran.r-project.org/src/contrib/munsell_0.5.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides easy access to, and manipulation of, the Munsell colours. Provides a mapping between Munsell's original notation (e.g. \"5R 5/10\") and hexadecimal strings suitable for use directly in R graphics. Also provides utilities to explore slices through the Munsell colour tree, to transform Munsell colours and display colour palettes.","text_prefix":false,"timestamp":1536484634,"version":"0.5.1"},"r-mustat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/muStat/index.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/muStat/muStat_1.7.0.tar.gz","https://cran.r-project.org/src/contrib/muStat_1.7.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Performs Wilcox rank sum test, Kruskal rank sum test, Friedman rank sum test and McNemar test.","text_prefix":false,"timestamp":0,"version":"1.7.0"},"r-mvn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=MVN","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MVN/MVN_6.3.tar.gz","https://cran.r-project.org/src/contrib/MVN_6.3.tar.gz"],"subdirs":["noarch"],"summary":"Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests, and performs multivariate Box-Cox transformation (Korkmaz et al, (2014), <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).","text_prefix":false,"timestamp":1622239229,"version":"6.3"},"r-mvnfast":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/mfasiolo/mvnfast","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mvnfast/mvnfast_0.2.8.tar.gz","https://cran.r-project.org/src/contrib/mvnfast_0.2.8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides computationally efficient tools related to the multivariate normal and Student's t distributions. The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API.","text_prefix":false,"timestamp":1602911583,"version":"0.2.8"},"r-mvnormtest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mvnormtest","license":"GPL-2 and GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mvnormtest/mvnormtest_0.1-9-3.tar.gz","https://cran.r-project.org/src/contrib/mvnormtest_0.1-9-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Generalization of shapiro-wilk test for multivariate variables.","text_prefix":false,"timestamp":1538244297,"version":"0.1_9_3"},"r-mvord":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=mvord","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/mvord/mvord_1.2.7.tar.gz","https://cloud.r-project.org/src/contrib/mvord_1.2.7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.","text_prefix":false,"timestamp":1622229277,"version":"1.2.7"},"r-mvoutlier":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://cstat.tuwien.ac.at/filz","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mvoutlier/mvoutlier_2.1.4.tar.gz","https://cran.r-project.org/src/contrib/mvoutlier_2.1.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Various Methods for Multivariate Outlier Detection.","text_prefix":false,"timestamp":1540311212,"version":"2.1.4"},"r-mvr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jedazard/MVR","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/MVR/MVR_1.33.0.tar.gz","https://cran.r-project.org/src/contrib/MVR_1.33.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include: (i) Normalization and/or variance stabilization of the data, (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow), (iii) Generation of diverse diagnostic plots, (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.","text_prefix":false,"timestamp":1758645259,"version":"1.33.0"},"r-mvtnorm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://codeberg.org/thothorn/mvtnorm","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mvtnorm/mvtnorm_1.4-2.tar.gz","https://cran.r-project.org/src/contrib/mvtnorm_1.4-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package.","text_prefix":false,"timestamp":1552900455,"version":"1.4_2"},"r-mwcsr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ctlab/mwcsr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://cloud.r-project.org/src/contrib/Archive/mwcsr/mwcsr_0.1.12.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"Algorithms for solving various Maximum Weight Connected Subgraph Problems, including variants with budget constraints, cardinality constraints, weighted edges and signals. The package represents an R interface to high-efficient solvers based on relax-and-cut approach (\u00c1lvarez-Miranda E., Sinnl M. (2017) <doi:10.1016/j.cor.2017.05.015>) mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) <doi:10.1007/978-3-319-43681-4_17>) and simulated annealing.","text_prefix":false,"timestamp":1774276838,"version":"0.1.12"},"r-mxmmod":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/khusmann/mxmmod","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mxmmod/mxmmod_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/mxmmod_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a convenient interface in 'OpenMx' for building Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of Derivatives (MMOD).","text_prefix":false,"timestamp":1588757625,"version":"1.1.0"},"r-mycor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/cardiomoon/mycor","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/mycor/mycor_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/mycor_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Perform correlation and linear regression test among the numeric fields in a data.frame automatically and make plots using pairs or lattice::parallelplot.","text_prefix":false,"timestamp":1773523135,"version":"0.1.1"},"r-n1qn1":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/nlmixrdevelopment/n1qn1","license":"CECILL-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/n1qn1/n1qn1_6.0.1-14.tar.gz","https://cran.r-project.org/src/contrib/n1qn1_6.0.1-14.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides 'Scilab' 'n1qn1', or Quasi-Newton BFGS \"qn\" without constraints and 'qnbd' or Quasi-Newton BFGS with constraints. This takes more memory than traditional L-BFGS.  The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. Both algorithms are described in the 'Scilab' optimization documentation located at <http://www.scilab.org/content/download/250/1714/file/optimization_in_scilab.pdf>.","text_prefix":false,"timestamp":1578385890,"version":"6.0.1_14"},"r-n2r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/kharchenkolab/N2R","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/N2R/N2R_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/N2R_1.0.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical  Navigable Small World (NSW) graphs. The original algorithm is described in \"Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs\", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <arXiv:1603.09320>.","text_prefix":false,"timestamp":1775541932,"version":"1.0.5"},"r-nabor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jefferis/nabor https://github.com/ethz-asl/libnabo","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nabor/nabor_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/nabor_0.5.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 'libnabo' has speed and space advantages over the 'ANN' library wrapped by package 'RANN'. 'nabor' includes a knn function that is designed as a drop-in replacement for 'RANN' function nn2. In addition, objects which include the k-d tree search structure can be returned to speed up repeated queries of the same set of target points.","text_prefix":false,"timestamp":1757827017,"version":"0.5.0"},"r-nacho":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mcanouil/NACHO, https://mcanouil.github.io/NACHO","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/NACHO/NACHO_2.0.8.tar.gz","https://cloud.r-project.org/src/contrib/NACHO_2.0.8.tar.gz"],"subdirs":["noarch"],"summary":"NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. 'NACHO' does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.","text_prefix":false,"timestamp":1609879473,"version":"2.0.8"},"r-nada":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NADA","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NADA/NADA_1.6-1.2.tar.gz","https://cran.r-project.org/src/contrib/NADA_1.6-1.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains methods described by Dennis Helsel in his book \"Nondetects And Data Analysis: Statistics for Censored Environmental Data\".","text_prefix":false,"timestamp":1541737594,"version":"1.6_1.2"},"r-naepirtparams":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NAEPirtparams","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NAEPirtparams/NAEPirtparams_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/NAEPirtparams_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"This data package contains the Item Response Theory (IRT) parameters for the National Center for Education Statistics (NCES) items used on the National Assessment of Education Progress (NAEP) from 1990 to 2015. The values in these tables are used along with NAEP data to turn student item responses into scores and include information about item difficulty, discrimination, and guessing parameter for 3 parameter logit (3PL) items. Parameters for Generalized Partial Credit Model (GPCM) items are also included. The adjustments table contains the information regarding the treatment of items (e.g., deletion of an item or a collapsing of response categories), when these items did not appear to fit the item response models used to describe the NAEP data. Transformation constants change the score estimates that are obtained from the IRT scaling program to the NAEP reporting metric. Values from the years 2000 - 2013 were taken from the NCES website <https://nces.ed.gov/nationsreportcard/> and values from 1990 - 1998 and 2015 were extracted from their NAEP data files. All subtest names were reduced and homogenized to one word (e.g. \"Reading to gain information\" became \"information\"). The various subtest names for univariate transformation constants were all homogenized to \"univariate\".","text_prefix":false,"timestamp":1758186031,"version":"1.0.0"},"r-naepprimer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NAEPprimer","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NAEPprimer/NAEPprimer_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/NAEPprimer_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.","text_prefix":false,"timestamp":1778778638,"version":"1.0.2"},"r-naivebayes":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/majkamichal/naivebayes, https://majkamichal.github.io/naivebayes/","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/naivebayes/naivebayes_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/naivebayes_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"In this implementation of the Naive Bayes classifier following class conditional distributions are available: Bernoulli, Categorical, Gaussian, Poisson and non-parametric representation of the class conditional density estimated via Kernel Density Estimation.","text_prefix":false,"timestamp":1564951287,"version":"1.0.0"},"r-nam":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NAM","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NAM/NAM_1.8.0.tar.gz","https://cran.r-project.org/src/contrib/NAM_1.8.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.","text_prefix":false,"timestamp":1566170892,"version":"1.8.0"},"r-namedropr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/nucleic-acid/namedropR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/namedropR/namedropR_2.4.1.tar.gz","https://cran.r-project.org/src/contrib/namedropR_2.4.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides 'visual citations' containing the metadata of a scientific paper and a 'QR' code. A 'visual citation' is a banner containing title, authors, journal and year of a publication. This package can create such banners based on 'BibTeX' and 'BibLaTeX' references and includes a QR code pointing to the 'DOI'. The resulting HTML object or PNG image can be included in a presentation to point the audience to good resources for further reading. Styling is possible via predefined designs or via custom 'CSS'. This package is not intended as replacement for proper reference manager packages, but a tool to enrich scientific presentation slides.","text_prefix":false,"timestamp":1653033125,"version":"2.4.1"},"r-naniar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/njtierney/naniar","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/naniar/naniar_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/naniar_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures  and functions that facilitate the plotting of missing values and examination  of imputations. This allows missing data dependencies to be explored with  minimal deviation from the common work patterns of 'ggplot2' and tidy data.","text_prefix":false,"timestamp":1599047896,"version":"1.1.0"},"r-nanoarrow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/apache/arrow-nanoarrow","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nanoarrow/nanoarrow_0.8.0-1.tar.gz","https://cran.r-project.org/src/contrib/nanoarrow_0.8.0-1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides an 'R' interface to the 'nanoarrow' 'C' library and the 'Apache Arrow' application binary interface. Functions to import and export 'ArrowArray', 'ArrowSchema', and 'ArrowArrayStream' 'C' structures to and from 'R' objects are provided alongside helpers to facilitate zero-copy data transfer among 'R' bindings to libraries implementing the 'Arrow' 'C' data interface.","text_prefix":false,"timestamp":1780722886,"version":"0.8.0_1"},"r-nanonext":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/shikokuchuo/nanonext/","home":"https://shikokuchuo.net/nanonext/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/nanonext/nanonext_1.10.3.tar.gz","https://cloud.r-project.org/src/contrib/nanonext_1.10.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. NNG is a socket library implementing 'Scalability Protocols', a reliable, high-performance standard for common communications patterns including publish/subscribe, request/reply and service discovery, over in-process, IPC, TCP, WebSocket and secure TLS transports. As its own threaded concurrency framework, provides a toolkit for asynchronous programming and distributed computing, with intuitive 'aio' objects which resolve automatically upon completion of asynchronous operations, and synchronisation primitives allowing R to wait upon events signalled by concurrent threads.","text_prefix":false,"timestamp":1733228542,"version":"1.10.3"},"r-nanoparquet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/r-lib/nanoparquet","home":"https://r-lib.github.io/nanoparquet/","license":"MIT + file","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/nanoparquet/nanoparquet_0.5.2.tar.gz","https://cloud.r-project.org/src/contrib/nanoparquet_0.5.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Self-sufficient reader and writer for flat 'Parquet' files. Can read most 'Parquet' data types. Can write many 'R' data types, including factors and temporal types. See docs for limitations.","text_prefix":false,"timestamp":1789959745,"version":"0.5.2"},"r-nanostringr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/OVCARE/nanostringr, https://ovcare.github.io/nanostringr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nanostringr/nanostringr_0.6.1.tar.gz","https://cran.r-project.org/src/contrib/nanostringr_0.6.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides quality control (QC), normalization, and batch effect correction operations for 'NanoString nCounter' data, Talhouk et al. (2016) <doi:10.1371/journal.pone.0153844>.  Various metrics are used to determine which samples passed or failed QC.  Gene expression should first be normalized to housekeeping genes, before a reference-based approach is used to adjust for batch effects.  Raw NanoString data can be imported in the form of Reporter Code Count (RCC) files.","text_prefix":false,"timestamp":1607196635,"version":"0.6.1"},"r-nanotime":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/eddelbuettel/nanotime","home":"https://eddelbuettel.github.io/nanotime/, https://dirk.eddelbuettel.com/code/nanotime.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nanotime/nanotime_0.3.15.tar.gz","https://cran.r-project.org/src/contrib/nanotime_0.3.15.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Full 64-bit resolution date and time functionality with nanosecond granularity is provided, with easy transition to and from the standard 'POSIXct' type. Three additional classes offer interval, period and duration functionality for nanosecond-resolution timestamps.","text_prefix":true,"timestamp":1589459471,"version":"0.3.15"},"r-nardl":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/zedtaha/nardl","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nardl/nardl_0.1.6.tar.gz","https://cran.r-project.org/src/contrib/nardl_0.1.6.tar.gz"],"subdirs":["noarch"],"summary":"Computes the nonlinear cointegrating autoregressive distributed lag model with p lags of the dependent variables and q lags of independent variables proposed by (Shin, Yu & Greenwood-Nimmo, 2014 <doi:10.1007/978-1-4899-8008-3_9>).","text_prefix":false,"timestamp":1588755751,"version":"0.1.6"},"r-narray":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mschubert/narray","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/narray/narray_0.5.2.tar.gz","https://cran.r-project.org/src/contrib/narray_0.5.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Stacking arrays according to dimension names, subset-aware splitting and mapping of functions, intersecting along arbitrary dimensions, converting to and from data.frames, and many other helper functions.","text_prefix":false,"timestamp":1530338532,"version":"0.5.2"},"r-nasaaccess":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Users need to set up a registration account(s) with Earthdata login as well as authorizing NASA GES DISC data access to generate gridded ascii tables of climate and earth observation remote sensing data. Please refer to  <https://disc.gsfc.nasa.gov/information/documents?title=Data%20Access> for further details. Creating the '.netrc' file at the user machine Home directory and storing the user NASA GES DISC logging information in it is done automatically to execute the NASAaccess package commands. The GES DISC user registration access logging information will be processed by masking in the terminal on any major OS. Without providing GES DISC user registration access logging information, the user will receive 'You need to provide your Earthdata GES DISC login to proceed\u2026' message.","dev_url":"https://github.com/imohamme/NASAaccess","doc_url":"https://imohamme.github.io/NASAaccess/","home":"https://github.com/imohamme/NASAaccess","license":"NASA-1.3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/imohamme/NASAaccess/archive/V4.2.0.tar.gz","subdirs":["noarch"],"summary":"NASAaccess software can generate gridded ascii tables of CMIP5 and CMIP6 climate data as well as earth observation remote sensing data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, DHSVM, RHESSys, \u2026etc.). The NASAaccess platform is available as software packages (i.e., conda and R packages) as well as an interactive format web-based environmental modeling application for earth observation data developed with the Tethys Platform.","text_prefix":false,"timestamp":1657306895,"version":"4.2.0"},"r-natserv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/natserv","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/natserv/natserv_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/natserv_1.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Interface to 'NatureServe' (<http://www.natureserve.org>). Includes methods to get data, image metadata, search taxonomic names, and make maps.","text_prefix":false,"timestamp":1539916523,"version":"1.0.0"},"r-naturalsort":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=naturalsort","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/naturalsort/naturalsort_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/naturalsort_0.1.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-32","win-64"],"summary":"Provides functions related to human natural ordering. It handles adjacent digits in a character sequence as a number so that natural sort function arranges a character vector by their numbers, not digit characters. It is typically seen when operating systems lists file names. For example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered because 1 < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms arrange it into a-1.png, a-10.png, a-2.png owing to their third and fourth characters.","text_prefix":false,"timestamp":1543424393,"version":"0.1.3"},"r-nbclust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://sites.google.com/site/malikacharrad/research/nbclust-package","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NbClust/NbClust_3.0.1.tar.gz","https://cran.r-project.org/src/contrib/NbClust_3.0.1.tar.gz"],"subdirs":["noarch"],"summary":"It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.","text_prefix":false,"timestamp":1621874343,"version":"3.0.1"},"r-ncar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=ncar","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ncar/ncar_0.7.2.tar.gz","https://cloud.r-project.org/src/contrib/ncar_0.7.2.tar.gz"],"subdirs":["noarch"],"summary":"Conduct a noncompartmental analysis with industrial strength. Some features are 1) CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method 5) Produce pdf, rtf, text report files. * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).","text_prefix":false,"timestamp":1790389697,"version":"0.7.2"},"r-ncbit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ncbit","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ncbit/ncbit_2013.03.29.1.tar.gz","https://cran.r-project.org/src/contrib/ncbit_2013.03.29.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"making NCBI taxonomic data locally available and searchable as an R object","text_prefix":false,"timestamp":1540830367,"version":"2013.03.29.1"},"r-ncdf4":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://cirrus.ucsd.edu/~pierce/ncdf","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ncdf4/ncdf4_1.24.tar.gz","https://cran.r-project.org/src/contrib/ncdf4_1.24.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets.  Using this package, netCDF files (either version 4 or \"classic\" version 3) can be opened and data sets read in easily.  It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files.  This package replaces the former ncdf package, which only worked with netcdf version 3 files.  For various reasons the names of the functions have had to be changed from the names in the ncdf package.  The old ncdf package is still available at the URL given below, if you need to have backward compatibility.  It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem.  However, the ncdf package does not provide an interface for netcdf version 4 files.","text_prefix":false,"timestamp":1571168484,"version":"1.24"},"r-ncdf4.helpers":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.r-project.org","license":"LGPL-2.1-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ncdf4.helpers/ncdf4.helpers_0.3-7.tar.gz","https://cran.r-project.org/src/contrib/ncdf4.helpers_0.3-7.tar.gz"],"subdirs":["noarch"],"summary":"Contains a collection of helper functions for dealing with 'NetCDF' files <https://www.unidata.ucar.edu/software/netcdf/> opened using 'ncdf4', particularly 'NetCDF' files that conform to the Climate and Forecast (CF) Metadata Conventions <http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html>.","text_prefix":false,"timestamp":1628061592,"version":"0.3_7"},"r-ncdunnett":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"www.dex.ufla.br/~danielff/r_resources.html","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nCDunnett/nCDunnett_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/nCDunnett_1.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Computes the noncentral Dunnett's test distribution (pdf, cdf and quantile) and generates random numbers.","text_prefix":false,"timestamp":1541026787,"version":"1.1.0"},"r-ncmeta":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hypertidy/ncmeta","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ncmeta/ncmeta_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/ncmeta_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Extract metadata from 'NetCDF' data sources, these can be files, file handles or servers. This package leverages and extends the lower level functions of the 'RNetCDF' package providing a consistent set of functions that all return data frames. We introduce named concepts of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library itself with only the named concepts of 'variables', 'dimensions' and 'attributes'. 'ncmeta' provides a required framework for the in-development 'tidync' project <https://github.com/hypertidy/tidync>.","text_prefix":false,"timestamp":1569703359,"version":"0.5.0"},"r-ncmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NCmisc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NCmisc/NCmisc_1.3.1.tar.gz","https://cran.r-project.org/src/contrib/NCmisc_1.3.1.tar.gz"],"subdirs":["noarch"],"summary":"A set of handy functions. Includes a versatile one line progress bar, one  line function timer with detailed output, time delay function, text histogram, object  preview, CRAN package search, simpler package installer, Linux command install check,  a flexible Mode function, top function, simulation of correlated data, and more.","text_prefix":false,"timestamp":1665553273,"version":"1.3.1"},"r-ncvreg":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://pbreheny.github.io/ncvreg, https://github.com/pbreheny/ncvreg","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ncvreg/ncvreg_3.16.0.tar.gz","https://cran.r-project.org/src/contrib/ncvreg_3.16.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the \"elastic net\" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided.","text_prefix":false,"timestamp":1594726351,"version":"3.16.0"},"r-ndjson":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://gitlab.com/hrbrmstr/ndjson","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ndjson/ndjson_0.9.1.tar.gz","https://cran.r-project.org/src/contrib/ndjson_0.9.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Streaming 'JSON' ('ndjson') has one 'JSON' record per-line and many modern 'ndjson' files contain large numbers of records. These constructs may not be columnar in nature, but it is often useful to read in these files and \"flatten\" the structure out to enable working with the data in an R 'data.frame'-like context. Functions are provided that make it possible to read in plain 'ndjson' files or compressed ('gz') 'ndjson' files and either validate the format of the records or create \"flat\" 'data.table' structures from them.","text_prefix":false,"timestamp":1665441340,"version":"0.9.1"},"r-neldermead":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=neldermead","license":"CeCILL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/neldermead/neldermead_1.0-13.tar.gz","https://cran.r-project.org/src/contrib/neldermead_1.0-13.tar.gz"],"subdirs":["noarch"],"summary":"Provides several direct search optimization algorithms based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed shape simplex), the variable shape simplex method of Nelder and Mead (unconstrained optimization with a variable shape simplex made), and Box's complex method (constrained optimization with a variable shape simplex).","text_prefix":false,"timestamp":1621916113,"version":"1.0_13"},"r-neo4r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/neo4j-rstats/neo4r","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/neo4r/neo4r_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/neo4r_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"A Modern and Flexible 'Neo4J' Driver, allowing you to query data on a 'Neo4J' server and handle the results in R. It's modern in the sense it provides a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis and graph packages. It's flexible in the  way it returns the results, by trying to stay as close as possible to the way 'Neo4J' returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the \"heavy lifting\" of data wrangling is not left to the user.","text_prefix":false,"timestamp":1778582673,"version":"0.1.4"},"r-neonos":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"NEON observational data are provided via the NEON Data Portal <https://www.neonscience.org> and NEON API, and can be downloaded and reformatted by the \"neonUtilities\" package. NEON observational data (human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and algal chemistry) are published in a format consisting of one or more tabular data files. This package provides tools for performing common operations on NEON observational data, including checking for duplicates and joining tables.","dev_url":"https://github.com/NEONScience/NEON-OS-data-processing","doc_url":"https://www.neonscience.org/resources/learning-hub/tutorials/download-explore-neon-data","home":"https://github.com/NEONScience/NEON-OS-data-processing","license":"AGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/neonOS/neonOS_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/neonOS_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Basic Data Wrangling for NEON Observational Data","text_prefix":false,"timestamp":1775644277,"version":"1.1.0"},"r-neonutilities":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org> or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.","dev_url":"https://github.com/NEONScience/NEON-utilities","home":"https://github.com/NEONScience/NEON-utilities","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/neonUtilities/neonUtilities_4.0.1.tar.gz","https://cran.r-project.org/src/contrib/neonUtilities_4.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Utilities for Working with NEON Data","text_prefix":false,"timestamp":1789044261,"version":"4.0.2"},"r-nestcolor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://insightsengineering.github.io/nestcolor/, https://github.com/insightsengineering/nestcolor/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nestcolor/nestcolor_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/nestcolor_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Clinical reporting figures require to use consistent colors and configurations. As a part of the Roche open-source clinical reporting project, namely the NEST project, the 'nestcolor' package specifies the color code and default theme with specifying 'ggplot2' theme parameters. Users can easily customize color and theme settings before using the reset of NEST packages to ensure consistent settings in both static and interactive output at the downstream.","text_prefix":false,"timestamp":1757860442,"version":"0.1.3"},"r-netmeta":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/guido-s/netmeta","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/netmeta/netmeta_3.6-1.tar.gz","https://cran.r-project.org/src/contrib/netmeta_3.6-1.tar.gz"],"subdirs":["noarch"],"summary":"A comprehensive set of functions providing frequentist methods for network meta-analysis and supporting Schwarzer et al. 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Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).","text_prefix":false,"timestamp":1757822791,"version":"2.0.10"},"r-nlmixr2est":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://nlmixr2.github.io/nlmixr2est/","home":"https://github.com/nlmixr2/nlmixr2est","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nlmixr2est/nlmixr2est_5.0.2.tar.gz","https://cran.r-project.org/src/contrib/nlmixr2est_5.0.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).","text_prefix":false,"timestamp":1766533259,"version":"5.0.2"},"r-nlmixr2extra":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/nlmixr2/nlmixr2extra/","home":"https://nlmixr2.github.io/nlmixr2extra/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nlmixr2extra/nlmixr2extra_5.0.0.tar.gz","https://cran.r-project.org/src/contrib/nlmixr2extra_5.0.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for support functions like preconditioned fits <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate selection.","text_prefix":false,"timestamp":1766546764,"version":"5.0.0"},"r-nlmixr2plot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/nlmixr2/nlmixr2plot","home":"https://nlmixr2.github.io/nlmixr2plot/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nlmixr2plot/nlmixr2plot_5.0.2.tar.gz","https://cran.r-project.org/src/contrib/nlmixr2plot_5.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for 'ggplot2' plotting methods for 'nlmixr2' objects.","text_prefix":false,"timestamp":1781164777,"version":"5.0.2"},"r-nloptr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=nloptr","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nloptr/nloptr_2.2.1.tar.gz","https://cran.r-project.org/src/contrib/nloptr_2.2.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See <http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction> for more information on the available algorithms. During installation of nloptr on Unix-based systems, the installer checks whether the NLopt library is installed on the system. If the NLopt library cannot be found, the code is compiled using the NLopt source included in the nloptr package.","text_prefix":false,"timestamp":1651596481,"version":"2.2.1"},"r-nlp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NLP","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NLP/NLP_0.3-3.tar.gz","https://cran.r-project.org/src/contrib/NLP_0.3-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Basic classes and methods for Natural Language Processing.","text_prefix":false,"timestamp":1538314831,"version":"0.3_3"},"r-nls2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/nls2/index.html","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nls2/nls2_0.3-4.tar.gz","https://cran.r-project.org/src/contrib/nls2_0.3-4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Adds brute force and multiple starting values to nls.","text_prefix":false,"timestamp":0,"version":"0.3_4"},"r-nlsic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/MathsCell/nlsic","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nlsic/nlsic_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/nlsic_1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"We solve non linear least squares problems with optional equality and/or inequality constraints. Non linear iterations are globalized with back-tracking method. Linear problems are solved by dense QR decomposition from 'LAPACK' which can limit the size of treated problems. On the other side, we avoid condition number degradation which happens in classical quadratic programming approach. Inequality constraints treatment on each non linear iteration is based on 'NNLS' method (by Lawson and Hanson). We provide an original function 'lsi_ln' for solving linear least squares problem with inequality constraints in least norm sens. Thus if Jacobian of the problem is rank deficient a solution still can be provided. However, truncation errors are probable in this case. Equality constraints are treated by using a basis of Null-space. User defined function calculating residuals must return a list having residual vector (not their squared sum) and Jacobian. If Jacobian is not in the returned list, package 'numDeriv' is used to calculated finite difference version of Jacobian. The 'NLSIC' method was fist published in Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.","text_prefix":false,"timestamp":1642171974,"version":"1.2.0"},"r-nmf":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://renozao.github.io/NMF","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NMF/NMF_0.21.0.tar.gz","https://cran.r-project.org/src/contrib/NMF_0.21.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines.","text_prefix":false,"timestamp":0,"version":"0.21.0"},"r-nmfbin":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://michalovadek.github.io/nmfbin/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nmfbin/nmfbin_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/nmfbin_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Factorize binary matrices into rank-k components using the logistic function in the updating process. See e.g. Tom\u00e9 et al (2015) <doi:10.1007/s11045-013-0240-9> .","text_prefix":false,"timestamp":1758721952,"version":"0.2.1"},"r-nmi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NMI","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NMI/NMI_2.0.tar.gz","https://cran.r-project.org/src/contrib/NMI_2.0.tar.gz"],"subdirs":["noarch"],"summary":"Calculates the normalized mutual information (NMI) of two community structures in network analysis.","text_prefix":false,"timestamp":1758368502,"version":"2.0"},"r-nmslibr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mlampros/nmslibR","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nmslibR/nmslibR_1.0.7.tar.gz","https://cran.r-project.org/src/contrib/nmslibR_1.0.7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A Non-Metric Space Library ('NMSLIB' <https://github.com/nmslib/nmslib>) wrapper, which according to the authors \"is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the 'NMSLIB' <https://github.com/nmslib/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods\". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' <https://github.com/nmslib/nmslib> 'Python' Library.","text_prefix":false,"timestamp":1665736310,"version":"1.0.7"},"r-nnet":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.stats.ox.ac.uk/pub/MASS4/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/nnet/nnet_7.3-21.tar.gz","https://cloud.r-project.org/src/contrib/nnet_7.3-21.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.","text_prefix":false,"timestamp":1621621074,"version":"7.3_21"},"r-nnlasso":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=nnlasso","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nnlasso/nnlasso_0.3.tar.gz","https://cran.r-project.org/src/contrib/nnlasso_0.3.tar.gz"],"subdirs":["noarch"],"summary":"Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.","text_prefix":false,"timestamp":1758565400,"version":"0.3"},"r-nnls":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=nnls","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nnls/nnls_1.6.tar.gz","https://cran.r-project.org/src/contrib/nnls_1.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS).  Also allows the combination of non-negative and non-positive constraints.","text_prefix":false,"timestamp":1757495915,"version":"1.6"},"r-nntensor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rikenbit/nnTensor","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nnTensor/nnTensor_1.4.0.tar.gz","https://cran.r-project.org/src/contrib/nnTensor_1.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Some functions for performing non-negative matrix factorization, non-negative CANDECOMP/PARAFAC (CP) decomposition, non-negative Tucker decomposition, and generating toy model data. See Andrzej Cichock et al (2009) <doi:10.1002/9780470747278> and the reference section of GitHub README.md <https://github.com/rikenbit/nnTensor>, for details of the methods.","text_prefix":false,"timestamp":1591273702,"version":"1.4.0"},"r-nodbi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/nodbi/, https://github.com/ropensci/nodbi","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/nodbi/nodbi_0.15.0.tar.gz","https://cloud.r-project.org/src/contrib/nodbi_0.15.0.tar.gz"],"subdirs":["noarch"],"summary":"Simplified document database access and manipulation, providing a common API across supported 'NoSQL' databases 'Elasticsearch', 'CouchDB', 'MongoDB' as well as 'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.","text_prefix":false,"timestamp":1695514620,"version":"0.15.0"},"r-nodiv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mkborregaard/nodiv","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nodiv/nodiv_1.4.4.tar.gz","https://cran.r-project.org/src/contrib/nodiv_1.4.4.tar.gz"],"subdirs":["noarch"],"summary":"An implementation of the nodiv algorithm, see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. 2014. Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. <DOI:10.1111/2041-210X.12283>. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped using the SOS statistic.","text_prefix":false,"timestamp":1665734867,"version":"1.4.4"},"r-noisefiltersr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NoiseFiltersR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NoiseFiltersR/NoiseFiltersR_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/NoiseFiltersR_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"An extensive implementation of state-of-the-art and classical algorithms to preprocess label noise in classification problems.","text_prefix":false,"timestamp":1758751295,"version":"0.1.0"},"r-nomclust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=nomclust","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nomclust/nomclust_2.8.1.tar.gz","https://cran.r-project.org/src/contrib/nomclust_2.8.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Similarity measures for hierarchical clustering of objects characterized by nominal (categorical) variables. Evaluation criteria for nominal data clustering.","text_prefix":false,"timestamp":1758317372,"version":"2.8.1"},"r-nomnoml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/nomnoml","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nomnoml/nomnoml_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/nomnoml_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"A tool for drawing sassy 'UML' diagrams based on a simple syntax, see <https://www.nomnoml.com>. Supports styling, R Markdown and exporting diagrams in the PNG format.","text_prefix":false,"timestamp":1758463480,"version":"0.3.0"},"r-noncompart":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=NonCompart","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/NonCompart/NonCompart_0.8.4.tar.gz","https://cloud.r-project.org/src/contrib/NonCompart_0.8.4.tar.gz"],"subdirs":["noarch"],"summary":"Conduct a noncompartmental analysis with industrial strength. Some features are 1) Use of CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).","text_prefix":false,"timestamp":1790019777,"version":"0.8.4"},"r-nor1mix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=nor1mix","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nor1mix/nor1mix_1.3-3.tar.gz","https://cran.r-project.org/src/contrib/nor1mix_1.3-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Onedimensional Normal Mixture Models Classes, for, e.g., density estimation or clustering algorithms research and teaching; providing the widely used Marron-Wand densities.  Efficient random number generation and graphics; now fitting to data by ML (Maximum Likelihood) or EM estimation.","text_prefix":false,"timestamp":1539331617,"version":"1.3_3"},"r-norm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=norm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/norm/norm_1.0-11.1.tar.gz","https://cran.r-project.org/src/contrib/norm_1.0-11.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An integrated set of functions for the analysis of multivariate normal datasets with missing values, including implementation of the EM algorithm, data augmentation, and multiple imputation.","text_prefix":false,"timestamp":1665433752,"version":"1.0_11.1"},"r-normallaplace":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NormalLaplace","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NormalLaplace/NormalLaplace_0.3-2.tar.gz","https://cran.r-project.org/src/contrib/NormalLaplace_0.3-2.tar.gz"],"subdirs":["noarch"],"summary":"Functions for the normal Laplace distribution. The package is under development and provides only limited functionality. Density, distribution and quantile functions, random number generation, and moments are provided.","text_prefix":false,"timestamp":1758715105,"version":"0.3_2"},"r-nortest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=nortest","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nortest/nortest_1.0-4.tar.gz","https://cran.r-project.org/src/contrib/nortest_1.0-4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Five omnibus tests for testing the composite hypothesis of normality.","text_prefix":false,"timestamp":1538143911,"version":"1.0_4"},"r-nozzle.r1":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/parklab/nozzle, http://gdac.broadinstitute.org/nozzle","license":"LGPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Nozzle.R1/Nozzle.R1_1.1-1.1.tar.gz","https://cran.r-project.org/src/contrib/Nozzle.R1_1.1-1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The Nozzle package provides an API to generate HTML reports with dynamic user interface elements based on JavaScript and CSS (Cascading Style Sheets). Nozzle was designed to facilitate summarization and rapid browsing of complex results in data analysis pipelines where multiple analyses are performed frequently on big data sets. The package can be applied to any project where user-friendly reports need to be created.","text_prefix":false,"timestamp":1539701391,"version":"1.1_1.1"},"r-np":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/JeffreyRacine/R-Package-np","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/np/np_0.70-5.tar.gz","https://cran.r-project.org/src/contrib/np_0.70-5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.","text_prefix":false,"timestamp":1666151731,"version":"0.70_5"},"r-nparcomp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=nparcomp","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nparcomp/nparcomp_3.0.tar.gz","https://cran.r-project.org/src/contrib/nparcomp_3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) <doi:10.18637/jss.v064.i09> for details.","text_prefix":false,"timestamp":0,"version":"3.0"},"r-npflow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=NPflow","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/NPflow/NPflow_0.13.6.tar.gz","https://cran.r-project.org/src/contrib/NPflow_0.13.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.","text_prefix":false,"timestamp":1757948494,"version":"0.13.6"},"r-nplyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/markjrieke/nplyr, https://markjrieke.github.io/nplyr/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nplyr/nplyr_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/nplyr_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides functions for manipulating nested data frames in a list-column using 'dplyr' <https://dplyr.tidyverse.org/> syntax. Rather than unnesting, then manipulating a data frame, 'nplyr' allows users to manipulate each nested data frame directly. 'nplyr' is a wrapper for 'dplyr' functions that provide tools for common data manipulation steps: filtering rows, selecting columns, summarising grouped data, among others.","text_prefix":false,"timestamp":1665734498,"version":"0.3.0"},"r-npregfast":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=npregfast","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/npregfast/npregfast_1.6.0.tar.gz","https://cran.r-project.org/src/contrib/npregfast_1.6.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A method for obtaining nonparametric estimates of regression models with or without factor-by-curve interactions using local polynomial kernel smoothers or splines. Additionally, a parametric model (allometric model) can be estimated.","text_prefix":false,"timestamp":1636450779,"version":"1.6.0"},"r-npsurv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.stat.auckland.ac.nz/~yongwang","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/npsurv/npsurv_0.5-0.tar.gz","https://cran.r-project.org/src/contrib/npsurv_0.5-0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains functions for non-parametric survival analysis of exact and interval-censored observations.","text_prefix":false,"timestamp":1539986614,"version":"0.5_0"},"r-nsga2r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=nsga2R","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nsga2R/nsga2R_1.1.tar.gz","https://cran.r-project.org/src/contrib/nsga2R_1.1.tar.gz"],"subdirs":["noarch"],"summary":"This package provide functions for box-constrained multiobjective optimization using the elitist non-dominated sorting genetic algorithm - NSGA-II. Fast non-dominated sorting, crowding distance, tournament selection, simulated binary crossover, and polynomial mutation are called in the main program, nsga2R, to complete the search.","text_prefix":false,"timestamp":1621916024,"version":"1.1"},"r-nsprcomp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://sigg-iten.ch/research/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nsprcomp/nsprcomp_0.5.1-2.tar.gz","https://cran.r-project.org/src/contrib/nsprcomp_0.5.1-2.tar.gz"],"subdirs":["noarch"],"summary":"Two methods for performing a constrained principal component analysis (PCA), where non-negativity and/or sparsity constraints are enforced on the principal axes (PAs). The function 'nsprcomp' computes one principal component (PC) after the other. Each PA is optimized such that the corresponding PC has maximum additional variance not explained by the previous components. In contrast, the function 'nscumcomp' jointly computes all PCs such that the cumulative variance is maximal. Both functions have the same interface as the 'prcomp' function from the 'stats' package (plus some extra parameters), and both return the result of the analysis as an object of class 'nsprcomp', which inherits from 'prcomp'. See <https://sigg-iten.ch/learningbits/2013/05/27/nsprcomp-is-on-cran/> and Sigg et al. (2008) <doi:10.1145/1390156.1390277> for more details.","text_prefix":false,"timestamp":1758572039,"version":"0.5.1_2"},"r-nsyllable":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/quanteda/nsyllable","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nsyllable/nsyllable_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/nsyllable_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Counts syllables in character vectors for English words.  Imputes syllables as the number of vowel sequences for words not found.","text_prefix":false,"timestamp":1621888206,"version":"1.0.1"},"r-numbers":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=numbers","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/numbers/numbers_0.9-2.tar.gz","https://cran.r-project.org/src/contrib/numbers_0.9-2.tar.gz"],"subdirs":["noarch"],"summary":"Provides number-theoretic functions for factorization, prime  numbers, twin primes, primitive roots, modular logarithm and inverses, extended GCD, Farey series and continuous fractions. Includes Legendre and Jacobi symbols, some divisor functions, Euler's Phi function, etc.","text_prefix":false,"timestamp":1618121609,"version":"0.9_2"},"r-numderiv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://optimizer.r-forge.r-project.org/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/numDeriv/numDeriv_2016.8-1.1.tar.gz","https://cran.r-project.org/src/contrib/numDeriv_2016.8-1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Methods for calculating (usually) accurate numerical first and second order derivatives. Accurate calculations  are done using 'Richardson''s' extrapolation or, when applicable, a complex step derivative is available. A simple difference  method is also provided. Simple difference is (usually) less accurate but is much quicker than 'Richardson''s' extrapolation and provides a  useful cross-check.  Methods are provided for real scalar and vector valued functions.","text_prefix":false,"timestamp":1536513264,"version":"2016.8_1.1"},"r-nycflights13":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/hadley/nycflights13","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/nycflights13/nycflights13_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/nycflights13_1.0.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Airline on-time data for all flights departing NYC in 2013.  Also includes useful 'metadata' on airlines, airports, weather, and planes.","text_prefix":false,"timestamp":1530171265,"version":"1.0.2"},"r-oai":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/oai","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/oai/oai_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/oai_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"A general purpose client to work with any 'OAI-PMH' (Open Archives Initiative Protocol for 'Metadata' Harvesting) service. The 'OAI-PMH' protocol is described at <http://www.openarchives.org/OAI/openarchivesprotocol.html>. Functions are provided to work with the 'OAI-PMH' verbs: 'GetRecord', 'Identify', 'ListIdentifiers', 'ListMetadataFormats', 'ListRecords', and 'ListSets'.","text_prefix":false,"timestamp":1588582709,"version":"0.4.0"},"r-oaqc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=oaqc","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/oaqc/oaqc_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/oaqc_2.0.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements the efficient algorithm by Ortmann and Brandes (2017) <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., 'igraph'), the orbit-aware counts are computed respective each of the edges and nodes.","text_prefix":false,"timestamp":1758624559,"version":"2.0.0"},"r-obistools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/iobis/obistools","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/iobis/obistools/archive/0.1.0.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"Prepare data sets for submission to the Ocean Biogeographical Information System (<http://iobis.org>). It can also be used for checking the quality of a set of marine species distribution records.","text_prefix":false,"timestamp":0,"version":"0.1.0"},"r-objectproperties":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=objectProperties","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/objectProperties/objectProperties_0.6.8.tar.gz","https://cran.r-project.org/src/contrib/objectProperties_0.6.8.tar.gz"],"subdirs":["noarch"],"summary":"Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.","text_prefix":false,"timestamp":1626945461,"version":"0.6.8"},"r-objectsignals":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=objectSignals","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/objectSignals/objectSignals_0.10.3.tar.gz","https://cran.r-project.org/src/contrib/objectSignals_0.10.3.tar.gz"],"subdirs":["noarch"],"summary":"A mutable Signal object can report changes to its state, clients could register functions so that they are called whenever the signal is emited. The signal could be emited, disconnected, blocked, unblocked, and buffered.","text_prefix":false,"timestamp":1621888579,"version":"0.10.3"},"r-oce":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://dankelley.github.com/oce/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["http://cran.r-project.org/src/contrib/Archive/oce/oce_1.8-4.tar.gz","http://cran.r-project.org/src/contrib/oce_1.8-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Supports the analysis of Oceanographic data, including 'ADCP' measurements, measurements made with 'argo' floats, 'CTD' measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature.","text_prefix":false,"timestamp":1657105366,"version":"1.8_4"},"r-oceanview":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=OceanView","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OceanView/OceanView_1.0.8.tar.gz","https://cran.r-project.org/src/contrib/OceanView_1.0.8.tar.gz"],"subdirs":["linux-64","win-64"],"summary":"Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.","text_prefix":false,"timestamp":1566333300,"version":"1.0.8"},"r-ocedata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://dankelley.github.io/ocedata","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ocedata/ocedata_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/ocedata_0.2.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Several Oceanographic data sets are provided for use by the 'oce' package and for other purposes.","text_prefix":false,"timestamp":1647283605,"version":"0.2.2"},"r-odbc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-dbi/odbc","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/odbc/odbc_1.7.2.tar.gz","https://cloud.r-project.org/src/contrib/odbc_1.7.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"A DBI-compatible interface to ODBC databases.","text_prefix":false,"timestamp":1592499501,"version":"1.7.2"},"r-officedown":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://davidgohel.github.io/officedown","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/officedown/officedown_0.4.3.tar.gz","https://cloud.r-project.org/src/contrib/officedown_0.4.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Allows production of 'Microsoft' corporate documents from 'R Markdown' by reusing formatting defined in 'Microsoft Word' documents. You can reuse table styles, list styles but also add column sections, landscape oriented pages. Table and image captions as well as cross-references are transformed into 'Microsoft Word' fields, allowing documents edition and merging without issue with references; the syntax conforms to the 'bookdown' cross-reference definition. Objects generated by the 'officer' package are also supported in the 'knitr' chunks. 'Microsoft PowerPoint' presentations also benefit from this as well as the ability to produce editable vector graphics in 'PowerPoint' and also to define placeholder where content is to be added.","text_prefix":false,"timestamp":1593549712,"version":"0.4.3"},"r-officer":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/davidgohel/officer/","doc_url":"https://davidgohel.github.io/officer/reference/index.html","home":"https://davidgohel.github.io/officer","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/officer/officer_0.7.7.tar.gz","https://cloud.r-project.org/src/contrib/officer_0.7.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","win-64"],"summary":"Access and manipulate 'Microsoft Word' and 'Microsoft PowerPoint' documents from R. The package focuses on tabular and graphical reporting from R; it also provides two functions that let users get document content into data objects. A set of functions lets add and remove images, tables and paragraphs of text in new or existing documents. The package does not require any installation of Microsoft products to be able to write Microsoft files.","text_prefix":false,"timestamp":1553032682,"version":"0.7.7"},"r-olinkanalyze":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=OlinkAnalyze","license":"AGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OlinkAnalyze/OlinkAnalyze_4.5.0.tar.gz","https://cran.r-project.org/src/contrib/OlinkAnalyze_4.5.0.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.","text_prefix":false,"timestamp":1667297606,"version":"4.5.0"},"r-olsrr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/rsquaredacademy/olsrr","home":"https://olsrr.rsquaredacademy.com/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/olsrr/olsrr_0.7.0.tar.gz","https://cran.r-project.org/src/contrib/olsrr_0.7.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.","text_prefix":false,"timestamp":1722990758,"version":"0.7.0"},"r-omicnavigator":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/abbvie-external/OmicNavigator","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OmicNavigator/OmicNavigator_1.19.0.tar.gz","https://cran.r-project.org/src/contrib/OmicNavigator_1.19.0.tar.gz"],"subdirs":["noarch"],"summary":"A tool for interactive exploration of the results from 'omics' experiments to facilitate novel discoveries from high-throughput biology. The software includes R functions for the 'bioinformatician' to deposit study metadata and the outputs from statistical analyses (e.g. differential expression, enrichment). These results are then exported to an interactive JavaScript dashboard that can be interrogated on the user's local machine or deployed online to be explored by collaborators. The dashboard includes 'sortable' tables, interactive plots including network visualization, and fine-grained filtering based on statistical significance.","text_prefix":false,"timestamp":1665734203,"version":"1.19.0"},"r-onehot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=onehot","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/onehot/onehot_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/onehot_0.1.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Quickly create numeric matrices for machine learning algorithms that require them. It converts factor columns into onehot vectors.","text_prefix":false,"timestamp":1758354494,"version":"0.1.1"},"r-oner":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/vonjd/OneR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OneR/OneR_2.2.tar.gz","https://cran.r-project.org/src/contrib/OneR_2.2.tar.gz"],"subdirs":["noarch"],"summary":"Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) <doi:10.1023/A:1022631118932>) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.","text_prefix":false,"timestamp":1757946790,"version":"2.2"},"r-onesamplemr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/remlapmot/OneSampleMR, https://remlapmot.github.io/OneSampleMR/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OneSampleMR/OneSampleMR_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/OneSampleMR_0.1.8.tar.gz"],"subdirs":["noarch"],"summary":"Useful functions for one-sample (individual level data) Mendelian randomization and instrumental variable analyses. The package includes implementations of; the Sanderson and Windmeijer (2016) <doi:10.1016/j.jeconom.2015.06.004> conditional F-statistic, the multiplicative structural mean model Hern\u00e1n and Robins (2006) <doi:10.1097/01.ede.0000222409.00878.37>, and two-stage predictor substitution and two-stage residual inclusion estimators explained by Terza et al. (2008) <doi:10.1016/j.jhealeco.2007.09.009>.","text_prefix":false,"timestamp":1667637055,"version":"0.1.8"},"r-onewaytests":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=onewaytests","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/onewaytests/onewaytests_3.1.tar.gz","https://cran.r-project.org/src/contrib/onewaytests_3.1.tar.gz"],"subdirs":["noarch"],"summary":"Performs one-way tests in independent groups designs including homoscedastic and heteroscedastic tests. These are one-way analysis of variance (ANOVA), Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test, Kruskal-Wallis test, Scott-Smith test, Box F test and Johansen F test, Generalized tests equivalent to Parametric Bootstrap and Fiducial tests. The package performs pairwise comparisons and graphical approaches. Also, the package includes Student's t test, Welch's t test and Mann-Whitney U test for two samples. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots (Dag et al., 2018, <https://journal.r-project.org/archive/2018/RJ-2018-022/RJ-2018-022.pdf>).","text_prefix":false,"timestamp":1666015607,"version":"3.1"},"r-onlinepca":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=onlinePCA","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/onlinePCA/onlinePCA_1.3.2.tar.gz","https://cran.r-project.org/src/contrib/onlinePCA_1.3.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.","text_prefix":false,"timestamp":1758905090,"version":"1.3.2"},"r-ontologyindex":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ontologyIndex","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ontologyIndex/ontologyIndex_2.12.tar.gz","https://cran.r-project.org/src/contrib/ontologyIndex_2.12.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions for reading ontologies into R as lists and manipulating sets of ontological terms - 'ontologyX: A suite of R packages for working with ontological data', Greene et al 2017 <doi:10.1093/bioinformatics/btw763>.","text_prefix":false,"timestamp":1541026364,"version":"2.12"},"r-ontologysimilarity":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ontologySimilarity","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ontologySimilarity/ontologySimilarity_2.9.tar.gz","https://cran.r-project.org/src/contrib/ontologySimilarity_2.9.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions for calculating semantic similarities between ontological terms or sets of ontological terms based on term information content and assessing statistical significance of similarity in the context of a collection of sets of ontological terms.","text_prefix":false,"timestamp":1612964272,"version":"2.9"},"r-oompabase":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://oompa.r-forge.r-project.org/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/oompaBase/oompaBase_3.2.12.tar.gz","https://cloud.r-project.org/src/contrib/oompaBase_3.2.12.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides the class unions that must be preloaded in order for the basic tools in the OOMPA (Object-Oriented  Microarray and Proteomics Analysis) project to be defined and loaded. It also includes vectorized operations for row-by-row means, variances, and t-tests. Finally, it provides new color schemes. Details on the packages in the OOMPA project can be found at <http://oompa.r-forge.r-project.org/>.","text_prefix":false,"timestamp":1540072616,"version":"3.2.12"},"r-oompadata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://oompa.r-forge.r-project.org/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/oompaData/oompaData_3.1.7.tar.gz","https://cloud.r-project.org/src/contrib/oompaData_3.1.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This is a data-only package to provide example data for other packages that are part of the \"Object-Oriented Microrray and Proteomics Analysis\" suite of packages. 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Includes functions for assertions and testing, looping, and re-usable design patterns including Abstract and Decorator classes.","text_prefix":false,"timestamp":1631521575,"version":"0.2.0"},"r-opdisdownsampling":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/JornLotsch/opdisDownsampling","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/opdisDownsampling/opdisDownsampling_1.7.tar.gz","https://cloud.r-project.org/src/contrib/opdisDownsampling_1.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An optimized method for distribution-preserving class-proportional down-sampling of bio-medical data <doi:10.1371/journal.pone.0255838>.","text_prefix":false,"timestamp":1789500139,"version":"1.7"},"r-openair":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/openair-project/openair","home":"https://openair-project.github.io/openair/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/openair/openair_3.1.0.tar.gz","https://cran.r-project.org/src/contrib/openair_3.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools to analyse, interpret and understand air pollution data. Data are typically regular time series and air quality measurement, meteorological data and dispersion model output can be analysed. The package is described in Carslaw and Ropkins (2012, <doi:10.1016/j.envsoft.2011.09.008>) and subsequent papers.","text_prefix":false,"timestamp":1668578215,"version":"3.1.0"},"r-openalexr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://docs.ropensci.org/openalexR/","home":"https://github.com/ropensci/openalexR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/openalexR/openalexR_3.1.2.tar.gz","https://cloud.r-project.org/src/contrib/openalexR_3.1.2.tar.gz"],"subdirs":["noarch"],"summary":"A set of tools to extract bibliographic content from 'OpenAlex' database using API <https://docs.openalex.org>.","text_prefix":false,"timestamp":1791968364,"version":"3.1.2"},"r-opencpu":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/opencpu/opencpu","home":"https://www.opencpu.org","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/opencpu/opencpu_2.2.15.tar.gz","https://cran.r-project.org/src/contrib/opencpu_2.2.15.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"A system for embedded scientific computing and reproducible research with R. The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange with R. This provides a reliable and scalable foundation for statistical services or  building R web applications. The OpenCPU server runs either as a single-user development server within the interactive R session, or as a multi-user Linux stack based on Apache2.  The entire system is fully open source and permissively licensed. The OpenCPU website has detailed documentation and example apps.","text_prefix":false,"timestamp":1545611217,"version":"2.2.15"},"r-openeo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/Open-EO/openeo-r-client","home":"https://open-eo.github.io/openeo-r-client/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"Access data and processing functionalities of 'openEO' compliant back-ends in R.","text_prefix":false,"timestamp":1782814223,"version":"1.4.1"},"r-openimager":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/mlampros/OpenImageR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OpenImageR/OpenImageR_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/OpenImageR_1.3.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, \"CloudID: Trustworthy cloud-based and cross-enterprise biometric identification\" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) \"SLIC Superpixels Compared to State-of-the-art Superpixel Methods\", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) \"SLIC Superpixels\", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.","text_prefix":false,"timestamp":1546598086,"version":"1.3.0"},"r-openmx":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://openmx.ssri.psu.edu, https://github.com/OpenMx/OpenMx","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OpenMx/OpenMx_2.22.11.tar.gz","https://cran.r-project.org/src/contrib/OpenMx_2.22.11.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Facilitates treatment of statistical model specifications as things that can be generated and manipulated programmatically. Structural equation models may be specified with reticular action model matrices or paths, linear structural relations matrices or paths, or directly in matrix algebra. Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares. Example models include confirmatory factor, multiple group, mixture distribution, categorical threshold, modern test theory, differential equations, state space, and many others.  MacOS users can download the most up-to-date  package binaries from <http://openmx.ssri.psu.edu>. See Neale, Hunter, Pritikin, Zahery, Brick, Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.","text_prefix":false,"timestamp":1666193474,"version":"2.22.11"},"r-opennlp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=openNLP","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/openNLP/openNLP_0.2-8.tar.gz","https://cran.r-project.org/src/contrib/openNLP_0.2-8.tar.gz"],"subdirs":["noarch"],"summary":"An interface to the Apache OpenNLP tools (version 1.5.3). The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text written in Java. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. See <https://opennlp.apache.org/> for more information.","text_prefix":false,"timestamp":1784299689,"version":"0.2_8"},"r-opennlpdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://opennlp.apache.org/, http://opennlp.sourceforge.net/models-1.5/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/openNLPdata/openNLPdata_1.5.3-5.tar.gz","https://cran.r-project.org/src/contrib/openNLPdata_1.5.3-5.tar.gz"],"subdirs":["noarch"],"summary":"Apache OpenNLP jars and basic English language models.","text_prefix":false,"timestamp":1757905441,"version":"1.5.3_5"},"r-openssl":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/jeroen/openssl","home":"https://jeroen.r-universe.dev/openssl","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/openssl/openssl_2.4.2.tar.gz","https://cran.r-project.org/src/contrib/openssl_2.4.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Bindings to OpenSSL libssl and libcrypto, plus custom SSH key parsers. Supports RSA, DSA and EC curves P-256, P-384, P-521, and curve25519. Cryptographic signatures can either be created and verified manually or via x509 certificates.  AES can be used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric (public key) encryption or EC for Diffie Hellman. High-level envelope functions  combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and 'bignum' math methods for manually performing crypto  calculations on large multibyte integers.","text_prefix":false,"timestamp":1553468812,"version":"2.4.2"},"r-openxlsx":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://ycphs.github.io/openxlsx/index.html, https://github.com/ycphs/openxlsx","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/openxlsx/openxlsx_4.2.9.tar.gz","https://cloud.r-project.org/src/contrib/openxlsx_4.2.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect' packages with the added benefit of removing the dependency on Java.","text_prefix":false,"timestamp":1666762214,"version":"4.2.9"},"r-openxlsx2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/JanMarvin/openxlsx2","home":"https://janmarvin.github.io/openxlsx2/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/openxlsx2/openxlsx2_1.29.tar.gz","https://cloud.r-project.org/src/contrib/openxlsx2_1.29.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect' packages with the added benefit of removing the dependency on Java.","text_prefix":false,"timestamp":1725795929,"version":"1.29"},"r-opera":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://pierre.gaillard.me/opera.html","license":"LGPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/opera/opera_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/opera_1.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Misc methods to form online predictions, for regression-oriented time-series, by combining a finite set of forecasts provided by the user. See Cesa-Bianchi and Lugosi (2006) <doi:10.1017/CBO9780511546921> for an overview.","text_prefix":false,"timestamp":1758675211,"version":"1.2.0"},"r-operator.tools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/decisionpatterns/operator.tools","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/operator.tools/operator.tools_1.6.3.1.tar.gz","https://cran.r-project.org/src/contrib/operator.tools_1.6.3.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a collection of utilities that allow programming with  R's operators. Routines allow classifying operators,  translating to and from an operator and its underlying function, and inverting  some operators (e.g. comparison operators), etc. All methods can be extended to custom infix operators.","text_prefix":false,"timestamp":1535639456,"version":"1.6.3.1"},"r-optextras":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=optextras","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/optextras/optextras_2019-12.4.tar.gz","https://cran.r-project.org/src/contrib/optextras_2019-12.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tools to assist in safely applying user generated objective and derivative function to optimization programs. These are primarily function minimization methods with at most bounds and masks on the parameters. Provides a way to check the basic computation of objective functions that the user provides, along with proposed gradient and Hessian functions, as well as to wrap such functions to avoid failures when inadmissible parameters are provided. Check bounds and masks. Check scaling or optimality conditions. Perform an axial search to seek lower points on the objective function surface. Includes forward, central and backward gradient approximation codes.","text_prefix":false,"timestamp":1537053765,"version":"2019_12.4"},"r-optimalcutpoints":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=OptimalCutpoints","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OptimalCutpoints/OptimalCutpoints_1.1-5.tar.gz","https://cran.r-project.org/src/contrib/OptimalCutpoints_1.1-5.tar.gz"],"subdirs":["noarch"],"summary":"Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.","text_prefix":false,"timestamp":1621915772,"version":"1.1_5"},"r-optimbase":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=optimbase","license":"CeCILL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/optimbase/optimbase_1.0-10.tar.gz","https://cran.r-project.org/src/contrib/optimbase_1.0-10.tar.gz"],"subdirs":["noarch"],"summary":"Provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc...","text_prefix":false,"timestamp":1621874185,"version":"1.0_10"},"r-optimparallel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/florafauna/optimParallel-R","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/optimParallel/optimParallel_1.0-3.tar.gz","https://cran.r-project.org/src/contrib/optimParallel_1.0-3.tar.gz"],"subdirs":["noarch"],"summary":"Provides a parallel version of the L-BFGS-B method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.","text_prefix":false,"timestamp":1789499183,"version":"1.0_3"},"r-optimr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=optimr","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/optimr/optimr_2019-12.16.tar.gz","https://cran.r-project.org/src/contrib/optimr_2019-12.16.tar.gz"],"subdirs":["noarch"],"summary":"Provides a test of replacement and extension of the optim() function to unify and streamline optimization capabilities in R for smooth, possibly box constrained functions of several or many parameters. This version has a reduced set of methods and is intended to be on CRAN.","text_prefix":false,"timestamp":1758304430,"version":"2019_12.16"},"r-optimsimplex":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=optimsimplex","license":"CeCILL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/optimsimplex/optimsimplex_1.0-8.tar.gz","https://cran.r-project.org/src/contrib/optimsimplex_1.0-8.tar.gz"],"subdirs":["noarch"],"summary":"Provides a building block for optimization algorithms based on a simplex. The 'optimsimplex' package may be used in the following optimization methods: the simplex method of Spendley et al. (1962) <doi:10.1080/00401706.1962.10490033>, the method of  Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, Box's algorithm for constrained optimization (1965) <doi:10.1093/comjnl/8.1.42>, the  multi-dimensional search by Torczon (1989)  <http://www.cs.wm.edu/~va/research/thesis.pdf>, etc...","text_prefix":false,"timestamp":1621888208,"version":"1.0_8"},"r-optimx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=optimx","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/optimx/optimx_2025-4.9.tar.gz","https://cran.r-project.org/src/contrib/optimx_2025-4.9.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a replacement and extension of the optim() function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions  of several or many parameters. Note that function 'optimr()' was prepared to simplify the incorporation of minimization codes going forward. Also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here.","text_prefix":false,"timestamp":1537174638,"version":"2025_4.9"},"r-options":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://dgkf.github.io/options/, https://codeberg.org/dgkf/options","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/options/options_0.3.1.tar.gz","https://cran.r-project.org/src/contrib/options_0.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Simple mechanisms for defining and interpreting package options. Provides helpers for interpreting environment variables, global options, defining default values and more.","text_prefix":false,"timestamp":1762811697,"version":"0.3.1"},"r-optm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=OptM","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OptM/OptM_0.1.9.tar.gz","https://cran.r-project.org/src/contrib/OptM_0.1.9.tar.gz"],"subdirs":["noarch"],"summary":"The popular population genetic software 'Treemix' by 'Pickrell and Pritchard' (2012) <DOI:10.1371/journal.pgen.1002967> estimates the number of migration edges on a population tree. However, it can be difficult to determine the number of migration edges to include. Previously, it was customary to stop adding migration edges when 99.8% of variation in the data was explained, but 'OptM' automates this process using an ad hoc statistic based on the second-order rate of change in the log likelihood.  'OptM' also has added functionality for various threshold modeling to compare with the ad hoc statistic.","text_prefix":false,"timestamp":1758577410,"version":"0.1.9"},"r-optmatch":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://optmat.ch, https://github.com/markmfredrickson/optmatch","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/optmatch/optmatch_0.10.8.tar.gz","https://cran.r-project.org/src/contrib/optmatch_0.10.8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies ('Hansen' and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.","text_prefix":false,"timestamp":1758874153,"version":"0.10.8"},"r-optparse":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/trevorld/r-optparse","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/optparse/optparse_1.8.2.tar.gz","https://cran.r-project.org/src/contrib/optparse_1.8.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A command line parser inspired by Python's 'optparse' library to be used with Rscript to write \"#!\" shebang scripts that accept short and long flag/options.","text_prefix":false,"timestamp":0,"version":"1.8.2"},"r-orcme":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ORCME","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ORCME/ORCME_2.0.2.tar.gz","https://cran.r-project.org/src/contrib/ORCME_2.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Provides clustering of genes with similar  dose response (or time course) profiles. It implements the method  described by Lin et al. (2012).","text_prefix":false,"timestamp":1758261398,"version":"2.0.2"},"r-orcutt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=orcutt","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/orcutt/orcutt_2.3.tar.gz","https://cran.r-project.org/src/contrib/orcutt_2.3.tar.gz"],"subdirs":["noarch"],"summary":"Solve first order autocorrelation problems using an iterative method. This procedure estimates both autocorrelation and beta coefficients recursively until we reach the convergence (8th decimal as default). The residuals are computed after estimating Beta using EGLS approach and Rho is estimated using the previous residuals.","text_prefix":false,"timestamp":1758674498,"version":"2.3"},"r-ordinal":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/runehaubo/ordinal","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ordinal/ordinal_2026.7-26.tar.gz","https://cran.r-project.org/src/contrib/ordinal_2026.7-26.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.","text_prefix":false,"timestamp":1563418686,"version":"2026.7_26"},"r-ordinalnet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ordinalNet","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ordinalNet/ordinalNet_2.14.tar.gz","https://cran.r-project.org/src/contrib/ordinalNet_2.14.tar.gz"],"subdirs":["noarch"],"summary":"Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) <doi:10.18637/jss.v099.i06>.","text_prefix":false,"timestamp":1770977417,"version":"2.14"},"r-ordpens":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ordPens","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ordPens/ordPens_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/ordPens_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Selection and/or smoothing of ordinally scaled independent variables using a group lasso or generalized ridge penalty.","text_prefix":false,"timestamp":1621957659,"version":"1.1.0"},"r-ore":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jonclayden/ore","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ore/ore_1.8.0.tar.gz","https://cloud.r-project.org/src/contrib/ore_1.8.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides an alternative to R's built-in functionality for handling regular expressions, based on the Onigmo library. Offers first-class compiled regex objects, partial matching and function-based substitutions, amongst other features.","text_prefix":false,"timestamp":1667318869,"version":"1.8.0"},"r-orgmassspecr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://OrgMassSpec.github.io/","license":"BSD_2_clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/OrgMassSpecR/OrgMassSpecR_0.5-4.tar.gz","https://cran.r-project.org/src/contrib/OrgMassSpecR_0.5-4.tar.gz"],"subdirs":["noarch"],"summary":"Organic/biological mass spectrometry data analysis.","text_prefix":false,"timestamp":1769656491,"version":"0.5_4"},"r-oriclust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ORIClust","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ORIClust/ORIClust_1.0-2.tar.gz","https://cran.r-project.org/src/contrib/ORIClust_1.0-2.tar.gz"],"subdirs":["noarch"],"summary":"ORIClust is a user-friendly R-based software package for gene clustering. Clusters are given by genes matched to prespecified profiles across various ordered treatment groups. It is particularly useful for analyzing data obtained from short time-course or dose-response microarray experiments.","text_prefix":false,"timestamp":1621915723,"version":"1.0_2"},"r-origami":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://tlverse.org/origami","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/origami/origami_1.0.8.tar.gz","https://cran.r-project.org/src/contrib/origami_1.0.8.tar.gz"],"subdirs":["noarch"],"summary":"A general framework for the application of cross-validation schemes to particular functions. By allowing arbitrary lists of results, origami accommodates a range of cross-validation applications.","text_prefix":false,"timestamp":1665726991,"version":"1.0.8"},"r-oro.nifti":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://rigorousanalytics.blogspot.com","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/oro.nifti/oro.nifti_0.11.4.tar.gz","https://cran.r-project.org/src/contrib/oro.nifti_0.11.4.tar.gz"],"subdirs":["noarch"],"summary":"Functions for the input/output and visualization of medical imaging data that follow either the 'ANALYZE', 'NIfTI' or 'AFNI' formats.  This package is part of the Rigorous Analytics bundle.","text_prefix":false,"timestamp":1758752216,"version":"0.11.4"},"r-orthopolynom":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=orthopolynom","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/orthopolynom/orthopolynom_1.0-6.1.tar.gz","https://cran.r-project.org/src/contrib/orthopolynom_1.0-6.1.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions to construct sets of orthogonal polynomials and their recurrence relations.","text_prefix":false,"timestamp":1652092989,"version":"1.0_6.1"},"r-osd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=osd","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/osd/osd_0.1.tar.gz","https://cran.r-project.org/src/contrib/osd_0.1.tar.gz"],"subdirs":["noarch"],"summary":"Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.","text_prefix":false,"timestamp":1758482380,"version":"0.1"},"r-osfr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/osfr, https://github.com/ropensci/osfr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/osfr/osfr_0.2.9.tar.gz","https://cran.r-project.org/src/contrib/osfr_0.2.9.tar.gz"],"subdirs":["noarch"],"summary":"An interface for interacting with 'OSF' (<https://osf.io>). 'osfr' enables you to access open research materials and data, or create and manage your own private or public projects.","text_prefix":false,"timestamp":1622111555,"version":"0.2.9"},"r-osmar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://osmar.r-forge.r-project.org/","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/osmar/osmar_1.1-7.tar.gz","https://cran.r-project.org/src/contrib/osmar_1.1-7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This package provides infrastructure to access OpenStreetMap data from different sources, to work with the data in common R manner, and to convert data into available infrastructure provided by existing R packages (e.g., into sp and igraph objects).","text_prefix":false,"timestamp":1540157434,"version":"1.1_7"},"r-osmclass":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=osmclass","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/osmclass/osmclass_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/osmclass_0.1.5.tar.gz"],"subdirs":["noarch"],"summary":"Classify Open Street Map (OSM) features into meaningful functional or analytical categories. Designed for OSM PBF files, e.g. from <https://download.geofabrik.de/> imported as spatial data frames. A classification consists of a list of categories that are related to certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main osm_classify() function returns a classification data table giving, for each feature, the primary and alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. The package also contains a classification of OSM features by economic function/significance, following Krantz (2023) <https://www.ssrn.com/abstract=4537867>.","text_prefix":false,"timestamp":1779243197,"version":"0.1.5"},"r-osmdata":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://docs.ropensci.org/osmdata (website) https://github.com/ropensci/osmdata (devel)","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/osmdata/osmdata_0.4.1.tar.gz","https://cloud.r-project.org/src/contrib/osmdata_0.4.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Download and import of 'OpenStreetMap' ('OSM') data as 'sf' or 'sp' objects.  'OSM' data are extracted from the 'Overpass' web server and processed with very fast 'C++' routines for return to 'R'.","text_prefix":false,"timestamp":1627479205,"version":"0.4.1"},"r-osqp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.osqp.org","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/osqp/osqp_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/osqp_1.0.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package or solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers. See <arXiv:1711.08013> for details.","text_prefix":false,"timestamp":1638877513,"version":"1.0.0"},"r-otel":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/r-lib/otel","home":"https://otel.r-lib.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/otel/otel_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/otel_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"High-quality, ubiquitous, and portable telemetry to enable effective observability. OpenTelemetry is a collection of tools, APIs, and SDKs used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior. This package implements the OpenTelemetry API: <https://opentelemetry.io/docs/specs/otel/>. Use this package as a dependency if you want to instrument your R package for OpenTelemetry.","text_prefix":false,"timestamp":1761151056,"version":"0.2.0"},"r-ottr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ottr","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ottr/ottr_1.6.0.tar.gz","https://cran.r-project.org/src/contrib/ottr_1.6.0.tar.gz"],"subdirs":["noarch"],"summary":"An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports grading R scripts, R Markdown documents, and R Jupyter Notebooks.","text_prefix":false,"timestamp":1784683531,"version":"1.6.0"},"r-outliers":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.r-project.org, http://www.komsta.net/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/outliers/outliers_0.15.tar.gz","https://cran.r-project.org/src/contrib/outliers_0.15.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of some tests commonly used for identifying outliers.","text_prefix":false,"timestamp":1539410749,"version":"0.15"},"r-overviewr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/cosimameyer/overviewR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/overviewR/overviewR_0.0.14.tar.gz","https://cran.r-project.org/src/contrib/overviewR_0.0.14.tar.gz"],"subdirs":["noarch"],"summary":"Makes it easy to display descriptive information on a data set.  Getting an easy overview of a data set by displaying and visualizing sample information in different tables (e.g., time and scope conditions).  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It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. 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'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.","text_prefix":false,"timestamp":1638381292,"version":"0.11.1"},"r-palaeoverse":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of \"palaeoverse\" is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart>) and auxiliary functions are also provided. 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Palette extensions also are provided for data frames using 'pillar'.","text_prefix":false,"timestamp":1778239642,"version":"0.0.3"},"r-paletteer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/EmilHvitfeldt/paletteer","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/paletteer/paletteer_1.7.0.tar.gz","https://cran.r-project.org/src/contrib/paletteer_1.7.0.tar.gz"],"subdirs":["linux-64","noarch","win-64"],"summary":"The choices of color palettes in R can be quite overwhelming with  palettes spread over many packages with many different API's. This packages aims to collect all color palettes across the R ecosystem under the same package with a streamlined API.","text_prefix":false,"timestamp":1534183478,"version":"1.7.0"},"r-palmerpenguins":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/allisonhorst/palmerpenguins","home":"https://allisonhorst.github.io/palmerpenguins/","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/palmerpenguins/palmerpenguins_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/palmerpenguins_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Size measurements, clutch observations, and blood isotope ratios for adult foraging Ad\u00e9lie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program.","text_prefix":false,"timestamp":1621849755,"version":"0.1.1"},"r-palr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/AustralianAntarcticDivision/palr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/palr/palr_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/palr_0.5.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Colour palettes for data, based on some well known public data sets.","text_prefix":false,"timestamp":1534147362,"version":"0.5.0"},"r-pals":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/kwstat/pals","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pals/pals_1.10.tar.gz","https://cran.r-project.org/src/contrib/pals_1.10.tar.gz"],"subdirs":["linux-64","noarch","win-64"],"summary":"A comprehensive collection of color palettes, colormaps, and tools to evaluate them.","text_prefix":false,"timestamp":1534179668,"version":"1.10"},"r-pamr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=pamr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pamr/pamr_1.57.tar.gz","https://cran.r-project.org/src/contrib/pamr_1.57.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Some functions for sample classification in microarrays.","text_prefix":false,"timestamp":1550621605,"version":"1.57"},"r-pan":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=pan","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pan/pan_2.0.tar.gz","https://cran.r-project.org/src/contrib/pan_2.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Multiple imputation for multivariate panel or clustered data.","text_prefix":false,"timestamp":1542581076,"version":"2.0"},"r-pander":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://rapporter.github.io/pander","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pander/pander_0.6.6.tar.gz","https://cran.r-project.org/src/contrib/pander_0.6.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'.","text_prefix":false,"timestamp":0,"version":"0.6.6"},"r-panelview":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/xuyiqing/panelView","home":"https://yiqingxu.org/packages/panelview/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/panelView/panelView_1.3.1.tar.gz","https://cran.r-project.org/src/contrib/panelView_1.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Visualizes panel data. It has four main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. For details, see <doi:10.18637/jss.v107.i07>.","text_prefix":false,"timestamp":1783425558,"version":"1.3.1"},"r-paradox":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://paradox.mlr-org.com, https://github.com/mlr-org/paradox","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/paradox/paradox_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/paradox_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.","text_prefix":false,"timestamp":1643635198,"version":"1.0.1"},"r-paralleldist":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/alexeckert/parallelDist","home":"https://www.alexandereckert.com/R","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/parallelDist/parallelDist_0.2.7.tar.gz","https://cran.r-project.org/src/contrib/parallelDist_0.2.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods.","text_prefix":false,"timestamp":1621764187,"version":"0.2.7"},"r-parallellogger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://ohdsi.github.io/ParallelLogger/, https://github.com/OHDSI/ParallelLogger","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ParallelLogger/ParallelLogger_3.5.1.tar.gz","https://cran.r-project.org/src/contrib/ParallelLogger_3.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. 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The development of 'plyr' has been generously supported by 'Becton Dickinson'.","text_prefix":false,"timestamp":1563336111,"version":"1.8.9"},"r-pma":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bnaras/PMA","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PMA/PMA_1.2-4.tar.gz","https://cran.r-project.org/src/contrib/PMA_1.2-4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008> and Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data <doi:10.2202/1544-6115.1470>.","text_prefix":false,"timestamp":1758276088,"version":"1.2_4"},"r-pmcmr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PMCMR","license":"GPL (>= 3)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PMCMR/PMCMR_4.4.tar.gz","https://cran.r-project.org/src/contrib/PMCMR_4.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Note, that the 'PMCMR' package is superseded by the novel 'PMCMRplus' package. The 'PMCMRplus' package contains all functions from  'PMCMR' and many more parametric and non-parametric multiple comparison procedures, one-factorial trend tests, as well as improved method functions,  such as print, summary and plot.  The 'PMCMR' package is no longer maintained, but kept for compatibility of reverse depending packages for some time.","text_prefix":false,"timestamp":0,"version":"4.4"},"r-pmcmrplus":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PMCMRplus","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PMCMRplus/PMCMRplus_1.9.12.tar.gz","https://cran.r-project.org/src/contrib/PMCMRplus_1.9.12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons  tests (Tukey-Kramer test, Scheffe test, LSD-test)  and many-to-one tests (Dunnett test) for normally distributed  residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane's T2 test,  Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals  and heterogeneous variances are provided. Van der Waerden's normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks.  Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included.  In addition, a Friedman-test for one-way ANOVA with repeated  measures on ranks (CRBD) and Skillings-Mack test for unbalanced  CRBD is provided with consequent all-pairs tests (Nemenyi test,  Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test).  A trend can be tested with Pages's test. Durbin's test  for a two-way balanced incomplete block design (BIBD) is given  in this package as well as Gore's test for CRBD with multiple observations per cell is given.  Outlier tests, Mandel's k- and h statistic as well as functions for Type I error and Power  analysis as well as generic summary, print and plot methods  are provided.","text_prefix":false,"timestamp":1634189623,"version":"1.9.12"},"r-pmdplyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://nickch-k.github.io/pmdplyr, https://github.com/NickCH-K/pmdplyr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pmdplyr/pmdplyr_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/pmdplyr_0.3.3.tar.gz"],"subdirs":["noarch"],"summary":"Using the 'dplyr' package as a base, adds a family of functions designed to make manipulating panel data easier. Allows the addition of indexing variables to a tibble to create a pibble, and the manipulation of data based on those indexing variables.","text_prefix":false,"timestamp":1588746191,"version":"0.3.3"},"r-pmml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://softwareag.github.io/r-pmml/, https://github.com/SoftwareAG/r-pmml, https://www.softwareag.com/corporate/products/az/zementis/default.html","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pmml/pmml_2.6.1.tar.gz","https://cran.r-project.org/src/contrib/pmml_2.6.1.tar.gz"],"subdirs":["noarch"],"summary":"The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms. The package isofor (used for anomaly detection) can be installed with devtools::install_github(\"Zelazny7/isofor\").","text_prefix":false,"timestamp":1636219126,"version":"2.6.1"},"r-pmsampsize":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=pmsampsize","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pmsampsize/pmsampsize_1.1.3.tar.gz","https://cran.r-project.org/src/contrib/pmsampsize_1.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.","text_prefix":false,"timestamp":1758056006,"version":"1.1.3"},"r-png":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.rforge.net/png/","license":"GPL-2.0-only OR GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/png/png_0.1-9.tar.gz","https://cran.r-project.org/src/contrib/png_0.1-9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.","text_prefix":false,"timestamp":1667596304,"version":"0.1_9"},"r-pnwcolors":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jakelawlor/PNWColors","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PNWColors/PNWColors_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/PNWColors_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"PNW-Inspired Palettes for 'R' data visualizations. Palettes are variable in length and checked for colorblind accessibility from hue, saturation, and lightness value scaling using the 'Chroma.js  Color Palette Helper' <https://gka.github.io/palettes/>.","text_prefix":false,"timestamp":1758386487,"version":"0.1.0"},"r-poibin":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=poibin","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poibin/poibin_1.6.tar.gz","https://cran.r-project.org/src/contrib/poibin_1.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution. It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution.","text_prefix":false,"timestamp":1563631030,"version":"1.6"},"r-poiclaclu":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PoiClaClu","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PoiClaClu/PoiClaClu_1.0.2.1.tar.gz","https://cran.r-project.org/src/contrib/PoiClaClu_1.0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.","text_prefix":false,"timestamp":1758515648,"version":"1.0.2.1"},"r-poilog":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/cran/poilog","home":"https://CRAN.R-project.org/package=poilog","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poilog/poilog_0.4.2.1.tar.gz","https://cran.r-project.org/src/contrib/poilog_0.4.2.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions for obtaining the density, random deviates  and maximum likelihood estimates of the Poisson lognormal  distribution and the bivariate Poisson lognormal distribution.","text_prefix":false,"timestamp":1665433226,"version":"0.4.2.1"},"r-pointblank":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rich-iannone/pointblank","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pointblank/pointblank_0.12.4.tar.gz","https://cran.r-project.org/src/contrib/pointblank_0.12.4.tar.gz"],"subdirs":["noarch"],"summary":"Validate data in data frames, 'tibble' objects, and in database tables (e.g., 'PostgreSQL' and 'MySQL'). Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions.","text_prefix":false,"timestamp":1642978911,"version":"0.12.4"},"r-poisbinom":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=poisbinom","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poisbinom/poisbinom_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/poisbinom_1.0.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.  This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R.","text_prefix":false,"timestamp":1758080522,"version":"1.0.2"},"r-poissonbinomial":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PoissonBinomial","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PoissonBinomial/PoissonBinomial_1.2.8.tar.gz","https://cran.r-project.org/src/contrib/PoissonBinomial_1.2.8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalized Poisson binomial distribution.","text_prefix":false,"timestamp":1627371531,"version":"1.2.8"},"r-poissonreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tidymodels/poissonreg","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poissonreg/poissonreg_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/poissonreg_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Bindings for Poisson regression models for use with the 'parsnip' package. Models include simple generalized linear models, Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber, and Jackman (2008) <doi:10.18637/jss.v027.i08>).","text_prefix":false,"timestamp":1628342126,"version":"1.0.2"},"r-polca":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://dlinzer.github.com/poLCA","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/poLCA/poLCA_1.6.0.2.tar.gz","https://cloud.r-project.org/src/contrib/poLCA_1.6.0.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Latent class analysis and latent class regression models for polytomous outcome variables.  Also known as latent structure analysis.","text_prefix":false,"timestamp":1633009537,"version":"1.6.0.2"},"r-policytree":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/grf-labs/policytree","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/policytree/policytree_1.2.5.tar.gz","https://cran.r-project.org/src/contrib/policytree_1.2.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Learn optimal policies via doubly robust empirical welfare maximization over trees. This package implements the multi-action doubly robust approach of Zhou, Athey and Wager (2018) <arXiv:1810.04778> in the case where we want to learn policies that belong to the class of depth k decision trees.","text_prefix":false,"timestamp":1625640401,"version":"1.2.5"},"r-polite":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dmi3kno/polite, https://dmi3kno.github.io/polite/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/polite/polite_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/polite_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice.","text_prefix":false,"timestamp":1778486543,"version":"0.1.4"},"r-polspline":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=polspline","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/polspline/polspline_1.1.25.tar.gz","https://cran.r-project.org/src/contrib/polspline_1.1.25.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.","text_prefix":false,"timestamp":1551353781,"version":"1.1.25"},"r-polychrome":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://oompa.r-forge.r-project.org/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Polychrome/Polychrome_1.6.1.tar.gz","https://cran.r-project.org/src/contrib/Polychrome_1.6.1.tar.gz"],"subdirs":["noarch"],"summary":"Tools for creating, viewing, and assessing qualitative palettes with many (20-30 or more) colors. See Coombes and colleagues (2019) <doi:10.18637/jss.v090.c01>.","text_prefix":false,"timestamp":1650063512,"version":"1.6.1"},"r-polyclip":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.angusj.com/delphi/clipper.php, https://sourceforge.net/projects/polyclipping, https://github.com/baddstats/polyclip","license":"BSL-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/polyclip/polyclip_1.10-7.tar.gz","https://cran.r-project.org/src/contrib/polyclip_1.10-7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"R port of Angus Johnson's open source library Clipper. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.","text_prefix":false,"timestamp":0,"version":"1.10_7"},"r-polycor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://r-forge.r-project.org/projects/polycor/, https://CRAN.R-project.org/package=polycor","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/polycor/polycor_0.8-2.tar.gz","https://cran.r-project.org/src/contrib/polycor_0.8-2.tar.gz"],"subdirs":["noarch"],"summary":"Computes polychoric and polyserial correlations by quick \"two-step\" methods or ML, optionally with standard errors; tetrachoric and biserial correlations are special cases.","text_prefix":false,"timestamp":1638364600,"version":"0.8_2"},"r-polycub":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/bastistician/polyCub","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/polyCub/polyCub_0.9.4.tar.gz","https://cran.r-project.org/src/contrib/polyCub_0.9.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Numerical integration of continuously differentiable functions f(x,y) over simple closed polygonal domains. The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, <doi:10.1007/s10543-007-0131-2>), the simple two-dimensional midpoint rule (wrapping 'spatstat' functions), adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, <doi:10.1214/14-AOAS743>, Supplement B), and integration of the bivariate Gaussian density based on polygon triangulation. For simple integration along the axes, the 'cubature' package is more appropriate.","text_prefix":false,"timestamp":1665433429,"version":"0.9.4"},"r-polylabelr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jolars/polylabelr, https://jolars.github.io/polylabelr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/polylabelr/polylabelr_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/polylabelr_1.0.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A wrapper around the C++ library 'polylabel' from 'Mapbox', providing an efficient routine for finding the approximate pole of inaccessibility of a polygon, which usually serves as an excellent candidate for labeling of a polygon.","text_prefix":false,"timestamp":1566314873,"version":"1.0.0"},"r-polynom":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=polynom","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/polynom/polynom_1.4-1.tar.gz","https://cran.r-project.org/src/contrib/polynom_1.4-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of functions to implement a class for univariate polynomial manipulations.","text_prefix":false,"timestamp":1537204176,"version":"1.4_1"},"r-polynomf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PolynomF","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PolynomF/PolynomF_2.0-8.tar.gz","https://cran.r-project.org/src/contrib/PolynomF_2.0-8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials.","text_prefix":false,"timestamp":1533396111,"version":"2.0_8"},"r-polysat":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/lvclark/polysat/wiki","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/polysat/polysat_1.7-7.tar.gz","https://cran.r-project.org/src/contrib/polysat_1.7-7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A collection of tools to handle microsatellite data of any ploidy (and samples of mixed ploidy) where allele copy number is not known in partially heterozygous genotypes.  It can import and export data in ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi', 'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate pairwise distances between individuals using a stepwise mutation model or infinite alleles model, with or without taking ploidies and allele frequencies into account.  These distances can be used for the calculation of clonal diversity statistics or used for further analysis in R.  Allelic diversity statistics and Polymorphic Information Content are also available.  polysat can  assist the user in estimating the ploidy of samples, and it can estimate allele  frequencies in populations, calculate pairwise or global differentiation statistics  based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'.   Functions are also included for assigning alleles to isoloci in cases where one pair  of microsatellite primers amplifies alleles from two or more independently segregating isoloci.  polysat is described by Clark and Jasieniuk (2011) <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2017) <doi:10.1111/1755-0998.12639>.","text_prefix":false,"timestamp":1638984162,"version":"1.7_7"},"r-pool":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/pool","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pool/pool_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/pool_1.0.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are 'DBI' connections.","text_prefix":false,"timestamp":1540830395,"version":"1.0.5"},"r-poolfstat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=poolfstat","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poolfstat/poolfstat_3.1.0.tar.gz","https://cran.r-project.org/src/contrib/poolfstat_3.1.0.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in TreeMix, BayPass and SelEstim format).","text_prefix":false,"timestamp":1772133658,"version":"3.1.0"},"r-poolr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=poolr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poolr/poolr_1.2-0.tar.gz","https://cran.r-project.org/src/contrib/poolr_1.2-0.tar.gz"],"subdirs":["noarch"],"summary":"Functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.","text_prefix":false,"timestamp":1773524169,"version":"1.2_0"},"r-poorman":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://nathaneastwood.github.io/poorman/, https://github.com/nathaneastwood/poorman","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poorman/poorman_0.2.8.tar.gz","https://cran.r-project.org/src/contrib/poorman_0.2.8.tar.gz"],"subdirs":["noarch"],"summary":"A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'.","text_prefix":false,"timestamp":1645536959,"version":"0.2.8"},"r-popgenome":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PopGenome","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PopGenome/PopGenome_2.7.5.tar.gz","https://cran.r-project.org/src/contrib/PopGenome_2.7.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides efficient tools for population genomics data analysis, able to process individual loci, large sets of loci, or whole genomes. PopGenome <DOI:10.1093/molbev/msu136> not only implements a wide range of population genetics statistics, but also facilitates the easy implementation of new algorithms by other researchers. PopGenome is optimized for speed via the seamless integration of C code.","text_prefix":false,"timestamp":1687102639,"version":"2.7.5"},"r-popgenreport":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/green-striped-gecko/PopGenReport","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PopGenReport/PopGenReport_3.1.3.tar.gz","https://cran.r-project.org/src/contrib/PopGenReport_3.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Provides beginner friendly framework to analyse population genetic data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit <http://www.popgenreport.org/>.","text_prefix":false,"timestamp":1759730906,"version":"3.1.3"},"r-poppr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"doc_url":"https://grunwaldlab.github.io/Population_Genetics_in_R/","home":"https://github.com/grunwaldlab/poppr","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poppr/poppr_2.9.8.tar.gz","https://cran.r-project.org/src/contrib/poppr_2.9.8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Population genetic analyses for hierarchical analysis of partially clonal populations built upon the architecture of the 'adegenet' package. Originally described in Kamvar, Tabima, and Gr\u00fcnwald (2014) <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and Gr\u00fcnwald (2015) <doi:10.3389/fgene.2015.00208>.","text_prefix":false,"timestamp":1759775094,"version":"2.9.8"},"r-posterior":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://mc-stan.org/posterior/, https://discourse.mc-stan.org/","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/posterior/posterior_1.7.0.tar.gz","https://cran.r-project.org/src/contrib/posterior_1.7.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.","text_prefix":false,"timestamp":1641416555,"version":"1.7.0"},"r-postlogic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RDocTaskForce/postlogic","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/postlogic/postlogic_0.1.0.1.tar.gz","https://cran.r-project.org/src/contrib/postlogic_0.1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides adds postfix and infix logic operators for if, then, unless, and otherwise.","text_prefix":false,"timestamp":1757868858,"version":"0.1.0.1"},"r-powerlaw":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/csgillespie/poweRlaw","license":"GPL-2 | GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/poweRlaw/poweRlaw_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/poweRlaw_1.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.","text_prefix":false,"timestamp":1540022305,"version":"1.0.0"},"r-powersurvepi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=powerSurvEpi","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/powerSurvEpi/powerSurvEpi_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/powerSurvEpi_0.1.5.tar.gz"],"subdirs":["noarch"],"summary":"Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study.","text_prefix":false,"timestamp":1758261647,"version":"0.1.5"},"r-ppclust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ppclust","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ppclust/ppclust_1.1.0.1.tar.gz","https://cran.r-project.org/src/contrib/ppclust_1.1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.","text_prefix":false,"timestamp":1565455960,"version":"1.1.0.1"},"r-ppcor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ppcor","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ppcor/ppcor_1.1.tar.gz","https://cran.r-project.org/src/contrib/ppcor_1.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Calculates partial and semi-partial (part) correlations along with p-value.","text_prefix":false,"timestamp":1539874974,"version":"1.1"},"r-ppls":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ppls","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ppls/ppls_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/ppls_2.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Contains linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing.","text_prefix":false,"timestamp":1757867562,"version":"2.0.0"},"r-pqlseq":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PQLseq","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PQLseq/PQLseq_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/PQLseq_1.2.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq.","text_prefix":false,"timestamp":1758975993,"version":"1.2.1"},"r-prabclus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.unibo.it/sitoweb/christian.hennig/en/","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/prabclus/prabclus_2.3-5.tar.gz","https://cran.r-project.org/src/contrib/prabclus_2.3-5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Distance-based parametric bootstrap tests for clustering with  spatial neighborhood information. Some distance measures,  Clustering of presence-absence, abundance and multilocus genetical data  for species delimitation, nearest neighbor  based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview.","text_prefix":false,"timestamp":1538476807,"version":"2.3_5"},"r-pracma":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=pracma","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pracma/pracma_2.4.6.tar.gz","https://cran.r-project.org/src/contrib/pracma_2.4.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.","text_prefix":false,"timestamp":1539329717,"version":"2.4.6"},"r-prais":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/franzmohr/prais","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/prais/prais_1.2.0.tar.gz","https://cloud.r-project.org/src/contrib/prais_1.2.0.tar.gz"],"subdirs":["noarch"],"summary":"The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.","text_prefix":false,"timestamp":1789844729,"version":"1.2.0"},"r-praise":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gaborcsardi/praise","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/praise/praise_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/praise_1.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Build friendly R packages that praise their users if they have done something good, or they just need it to feel better.","text_prefix":false,"timestamp":1536305015,"version":"1.0.0"},"r-praznik":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://gitlab.com/mbq/praznik","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/praznik/praznik_13.0.0.tar.gz","https://cloud.r-project.org/src/contrib/praznik_13.0.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <http://www.jmlr.org/papers/v13/brown12a.html>. Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <http://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi  (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.","text_prefix":false,"timestamp":1636496965,"version":"13.0.0"},"r-pre":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/marjoleinF/pre","license":"GPL-2.0-only and GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/pre/pre_1.1.1.tar.gz","https://cloud.r-project.org/src/contrib/pre_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Derives prediction rule ensembles (PREs). Largely follows the procedure for deriving PREs as described in Friedman & Popescu (2008; <DOI:10.1214/07-AOAS148>), with adjustments and improvements. The main function pre() derives prediction rule ensembles consisting of rules and/or linear terms for continuous, binary, count, multinomial, and multivariate continuous responses. Function gpe() derives generalized prediction ensembles, consisting of rules, hinge and linear functions of the predictor variables.","text_prefix":false,"timestamp":1789838613,"version":"1.1.1"},"r-precisesums":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PreciseSums","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PreciseSums/PreciseSums_0.7.tar.gz","https://cran.r-project.org/src/contrib/PreciseSums_0.7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Most of the time floating point arithmetic does approximately the right thing.  When adding sums or having products of numbers that greatly differ in magnitude, the floating point arithmetic may be incorrect.  This package implements the Kahan (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy', See Castaldo (2008) <doi:10.1137/070679946> for a discussion of accuracy), and arbitrary precision sum (adapted from the fsum in 'Python' ; Shewchuk (1997) <http://www.cs.berkeley.edu/~jrs/papers/robustr.pdf>).  In addition, products are changed to long double precision for accuracy, or changed into a log-sum for accuracy.","text_prefix":false,"timestamp":1665426938,"version":"0.7"},"r-precommit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://lorenzwalthert.github.io/precommit/, https://github.com/lorenzwalthert/precommit","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/precommit/precommit_0.4.3.tar.gz","https://cran.r-project.org/src/contrib/precommit_0.4.3.tar.gz"],"subdirs":["noarch"],"summary":"Useful git hooks for R building on top of the multi-language framework 'pre-commit' for hook management. 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The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar() or progress::progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beep::beepr(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce APIs like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. 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The intention is for this package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5 pathways be provided by the 'proj4' package.","text_prefix":false,"timestamp":1789844552,"version":"0.7.0"},"r-proj4":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.rforge.net/proj4/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/proj4/proj4_1.0-15.tar.gz","https://cloud.r-project.org/src/contrib/proj4_1.0-15.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. 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Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using  factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis.   Functions for simulating and testing particular item and test structures are included. Several functions  serve as a useful front end for structural equation modeling.  Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. 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Intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package \"psychomix\" and trees based on psychometric models in package \"psychotree\".","text_prefix":false,"timestamp":1621762483,"version":"0.7_7"},"r-psychotree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://zeileis.codeberg.page/psychotree/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/psychotree/psychotree_0.16-3.tar.gz","https://cran.r-project.org/src/contrib/psychotree_0.16-3.tar.gz"],"subdirs":["noarch"],"summary":"Recursive partitioning based on psychometric models, employing the general MOB algorithm (from package partykit) to obtain Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and MPT trees, trees for 1PL, 2PL, 3PL and 4PL models and generalized partial credit models.","text_prefix":false,"timestamp":1637683178,"version":"0.16_3"},"r-psychtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://personality-project.org/r/psych/ https://personality-project.org/r/psych-manual.pdf","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/psychTools/psychTools_2.6.4.tar.gz","https://cran.r-project.org/src/contrib/psychTools_2.6.4.tar.gz"],"subdirs":["noarch"],"summary":"Support functions,  data sets, and vignettes for the 'psych' package. 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Both warping of peak profiles and of peak lists are supported.","text_prefix":false,"timestamp":1563623978,"version":"1.9_17"},"r-ptxqc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/cbielow/PTXQC","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/PTXQC/PTXQC_1.1.6.tar.gz","https://cloud.r-project.org/src/contrib/PTXQC_1.1.6.tar.gz"],"subdirs":["noarch"],"summary":"Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the  MaxQuant software suite. Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format. Published in J. 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Christian L. M\u00fcller, Richard Bonneau, Zachary Kurtz (2016) <arXiv:1605.07072>.","text_prefix":false,"timestamp":1665712504,"version":"0.3.13"},"r-puniform":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RobbievanAert/puniform","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/puniform/puniform_0.2.8.tar.gz","https://cran.r-project.org/src/contrib/puniform_0.2.8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides meta-analysis methods that correct for publication bias and outcome reporting bias. Four methods and a visual tool are currently included in the package. The p-uniform method as described in van Assen, van Aert, and Wicherts (2015) <https:psycnet.apa.org/record/2014-48759-001> can be used for estimating the average effect size, testing the null hypothesis of no effect, and testing for publication bias using only the statistically significant effect sizes of primary studies. The second method in the package is the p-uniform* method as described in van Aert and van Assen (2019) <doi:10.31222/osf.io/zqjr9>. This method is an extension of the p-uniform method that allows for estimation of the average effect size and the between-study variance in a meta-analysis, and uses both the statistically significant and nonsignificant effect sizes. The third method in the package is the hybrid method as described in van Aert and van Assen (2017) <doi:10.3758/s13428-017-0967-6>. The hybrid method is a meta-analysis method for combining an original study and replication and while taking into account statistical significance of the  original study. The p-uniform and hybrid method are based on the statistical theory that the distribution of p-values is uniform conditional on the population effect size. The fourth method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in van Aert and van Assen (2018) <doi:10.1371/journal.pone.0175302>. This method computes posterior probabilities for four true effect sizes (no, small, medium, and large) based on an original study and replication while taking into account publication bias in the original study. The method can also be used for computing the required sample size of the replication akin to power analysis in null hypothesis significance testing. The meta-plot is a visual tool for meta-analysis that provides information on the primary studies in the meta-analysis, the results of the meta-analysis, and characteristics of the research on the effect under study (van Assen et al., 2021). Helper functions to apply the Correcting for Outcome Reporting Bias (CORB) method to correct for outcome reporting bias in a meta-analysis (van Aert & Wicherts, 2021).","text_prefix":false,"timestamp":1758723028,"version":"0.2.8"},"r-purbayes":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=PurBayes","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/PurBayes/PurBayes_1.3.tar.gz","https://cran.r-project.org/src/contrib/PurBayes_1.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"PurBayes is an MCMC-based algorithm that uses next-generation sequencing data to estimate tumor purity and clonality for paired tumor-normal data.","text_prefix":false,"timestamp":0,"version":"1.3"},"r-purrr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/tidyverse/purrr","doc_url":"https://purrr.tidyverse.org/reference/index.html","home":"https://purrr.tidyverse.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/purrr/purrr_1.2.2.tar.gz","https://cran.r-project.org/src/contrib/purrr_1.2.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A complete and consistent functional programming toolkit for R.","text_prefix":false,"timestamp":1551858402,"version":"1.2.2"},"r-purrrlyr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/hadley/purrrlyr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/purrrlyr/purrrlyr_0.0.10.tar.gz","https://cran.r-project.org/src/contrib/purrrlyr_0.0.10.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Some functions at the intersection of 'dplyr' and 'purrr' that  formerly lived in 'purrr'.","text_prefix":false,"timestamp":1551858989,"version":"0.0.10"},"r-purrrogress":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/halpo/purrrogress","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/purrrogress/purrrogress_0.1.1.tar.gz","https://cran.r-project.org/src/contrib/purrrogress_0.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides functions to easily add progress bars to apply calls.","text_prefix":false,"timestamp":1758584507,"version":"0.1.1"},"r-pushoverr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://briandconnelly.github.io/pushoverr/, https://github.com/briandconnelly/pushoverr","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/pushoverr/pushoverr_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/pushoverr_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Send push notifications to mobile devices or the desktop using 'Pushover' <https://pushover.net>. 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The procedure is described in detail in Pena, D., & Yohai, V. 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Extends 'quanteda' and related packages with plot methods designed specifically for text data, textual statistics, and models fit to textual data. Plot types include word clouds, lexical dispersion plots, scaling plots, network visualisations, and word 'keyness' plots.","text_prefix":false,"timestamp":1766467691,"version":"0.96.1"},"r-quanteda.textstats":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://quanteda.io","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/quanteda.textstats/quanteda.textstats_0.97.2.tar.gz","https://cran.r-project.org/src/contrib/quanteda.textstats_0.97.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Textual statistics functions formerly in the 'quanteda' package. Textual statistics for characterizing and comparing textual data. Includes functions for measuring term and document frequency, the co-occurrence of words, similarity and distance between features and documents, feature entropy, keyword occurrence, readability, and lexical diversity.  These functions extend the 'quanteda' package and are specially designed for sparse textual data.","text_prefix":false,"timestamp":1637743577,"version":"0.97.2"},"r-quantmod":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/joshuaulrich/quantmod","home":"http://www.quantmod.com","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/quantmod/quantmod_0.4.29.tar.gz","https://cran.r-project.org/src/contrib/quantmod_0.4.29.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Specify, build, trade, and analyse quantitative financial trading strategies.","text_prefix":false,"timestamp":1539811711,"version":"0.4.29"},"r-quantpsyc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=QuantPsyc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/QuantPsyc/QuantPsyc_1.6.tar.gz","https://cran.r-project.org/src/contrib/QuantPsyc_1.6.tar.gz"],"subdirs":["noarch"],"summary":"Contains tools useful for data screening, testing moderation (Cohen et. al. 2003)<doi:10.4324/9780203774441>, mediation (MacKinnon et. al. 2002)<doi:10.1037/1082-989x.7.1.83> and estimating power (Murphy & Myors 2014)<ISBN:9781315773155>.","text_prefix":false,"timestamp":1758603691,"version":"1.6"},"r-quantreg":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.r-project.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/quantreg/quantreg_6.1.tar.gz","https://cran.r-project.org/src/contrib/quantreg_6.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Estimation and inference methods for models of conditional quantiles:  Linear and nonlinear parametric and non-parametric (total variation penalized) models  for conditional quantiles of a univariate response and several methods for handling censored survival data.  Portfolio selection methods based on expected shortfall risk are also included.","text_prefix":false,"timestamp":1573315643,"version":"6.1"},"r-quarto":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/quarto-dev/quarto-r","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/quarto/quarto_1.5.1.tar.gz","https://cran.r-project.org/src/contrib/quarto_1.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Convert R Markdown documents and 'Jupyter' notebooks to a variety of output formats using 'Quarto'.","text_prefix":false,"timestamp":1656401311,"version":"1.5.1"},"r-queryup":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/VoisinneG/queryup","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/queryup/queryup_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/queryup_1.0.5.tar.gz"],"subdirs":["noarch"],"summary":"Retrieve protein information from the 'UniProtKB' REST API (see <https://www.uniprot.org/help/api_queries>).","text_prefix":false,"timestamp":1773517648,"version":"1.0.5"},"r-questionr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://juba.github.io/questionr/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/questionr/questionr_0.8.2.tar.gz","https://cran.r-project.org/src/contrib/questionr_0.8.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions.","text_prefix":false,"timestamp":1539840323,"version":"0.8.2"},"r-quickjsr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://bellard.org/quickjs/","home":"https://github.com/andrjohns/QuickJSR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/QuickJSR/QuickJSR_1.10.0.tar.gz","https://cran.r-project.org/src/contrib/QuickJSR_1.10.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An 'R' interface to the 'QuickJS' portable 'JavaScript' engine. The engine is bundled entirely within the package, requiring no external system dependencies beyond a 'C' compiler.","text_prefix":false,"timestamp":1776426604,"version":"1.10.0"},"r-quickmatch":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/fsavje/quickmatch","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/quickmatch/quickmatch_0.2.3.tar.gz","https://cran.r-project.org/src/contrib/quickmatch_0.2.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides functions for constructing near-optimal generalized full matching. Generalized full matching is an extension of the original full matching method to situations with more intricate study designs. The package is made with large data sets in mind and derives matches more than an order of magnitude quicker than other methods.","text_prefix":false,"timestamp":1758690964,"version":"0.2.3"},"r-quitefastmst":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/gagolews/quitefastmst","home":"https://quitefastmst.gagolewski.com/","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/quitefastmst/quitefastmst_0.9.2.tar.gz","https://cloud.r-project.org/src/contrib/quitefastmst_0.9.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions to compute Euclidean minimum spanning trees using single-, sesqui-, and dual-tree Boruvka algorithms.  Thanks to K-d trees, they are fast in spaces of low intrinsic dimensionality.  Mutual reachability distances (used in the definition of the 'HDBSCAN*' algorithm) are also supported.  The package also features relatively fast fallback minimum spanning tree and nearest-neighbours algorithms for spaces of higher dimensionality.  The 'Python' version of 'quitefastmst' is available via 'PyPI'.","text_prefix":false,"timestamp":1790600441,"version":"0.9.2"},"r-qvcalc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://davidfirth.github.io/qvcalc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/qvcalc/qvcalc_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/qvcalc_1.0.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions to compute quasi variances and associated measures of approximation error.","text_prefix":false,"timestamp":1538217511,"version":"1.0.4"},"r-r.cache":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/R.cache","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R.cache/R.cache_0.17.0.tar.gz","https://cran.r-project.org/src/contrib/R.cache_0.17.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).","text_prefix":false,"timestamp":0,"version":"0.17.0"},"r-r.devices":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/R.devices","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R.devices/R.devices_2.17.4.tar.gz","https://cran.r-project.org/src/contrib/R.devices_2.17.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types. Switching output format requires minimal changes in code. This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.","text_prefix":false,"timestamp":0,"version":"2.17.4"},"r-r.filesets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/R.filesets, http://www.aroma-project.org/","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R.filesets/R.filesets_2.15.1.tar.gz","https://cran.r-project.org/src/contrib/R.filesets_2.15.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.","text_prefix":false,"timestamp":0,"version":"2.15.1"},"r-r.huge":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/R.huge","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R.huge/R.huge_0.10.1.tar.gz","https://cran.r-project.org/src/contrib/R.huge_0.10.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)","text_prefix":false,"timestamp":1540182429,"version":"0.10.1"},"r-r.matlab":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/R.matlab","license":"LGPL-2.1-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/R.matlab/R.matlab_3.8.0.tar.gz","https://cloud.r-project.org/src/contrib/R.matlab_3.8.0.tar.gz"],"subdirs":["noarch"],"summary":"Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.","text_prefix":false,"timestamp":1621884017,"version":"3.8.0"},"r-r.methodss3":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=R.methodsS3","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R.methodsS3/R.methodsS3_1.8.2.tar.gz","https://cran.r-project.org/src/contrib/R.methodsS3_1.8.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.","text_prefix":false,"timestamp":1537102207,"version":"1.8.2"},"r-r.oo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/R.oo","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R.oo/R.oo_1.27.1.tar.gz","https://cran.r-project.org/src/contrib/R.oo_1.27.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.","text_prefix":false,"timestamp":1537135216,"version":"1.27.1"},"r-r.rsp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/R.rsp","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R.rsp/R.rsp_0.46.0.tar.gz","https://cran.r-project.org/src/contrib/R.rsp_0.46.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The RSP markup language makes any text-based document come alive.  RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'.  Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries.  RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things.  Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'.  RSP is ideal for self-contained scientific reports and R package vignettes.  It's easy to use - if you know how to write an R script, you'll be up and running within minutes.","text_prefix":false,"timestamp":1540037220,"version":"0.46.0"},"r-r.utils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/HenrikBengtsson/R.utils","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R.utils/R.utils_2.13.0.tar.gz","https://cran.r-project.org/src/contrib/R.utils_2.13.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Utility functions useful when programming and developing R packages.","text_prefix":false,"timestamp":0,"version":"2.13.0"},"r-r2d3":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/r2d3","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/r2d3/r2d3_0.2.6.tar.gz","https://cran.r-project.org/src/contrib/r2d3_0.2.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Suite of tools for using 'D3', a library for producing dynamic, interactive data visualizations. Supports translating objects into 'D3' friendly data structures, rendering 'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R packages.","text_prefix":false,"timestamp":1540830752,"version":"0.2.6"},"r-r2dii.analysis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/2DegreesInvesting/r2dii.analysis","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/r2dii.analysis/r2dii.analysis_0.5.3.tar.gz","https://cran.r-project.org/src/contrib/r2dii.analysis_0.5.3.tar.gz"],"subdirs":["noarch"],"summary":"These tools help you to assess if a corporate lending portfolio aligns with climate goals. They summarize key climate indicators attributed to the portfolio (e.g. production, emission factors), and calculate alignment targets based on climate scenarios. They implement in R the last step of the free software 'PACTA' (Paris Agreement Capital Transition Assessment; <https://2degrees-investing.org/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals.","text_prefix":false,"timestamp":1643704859,"version":"0.5.3"},"r-r2dii.data":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://2degreesinvesting.github.io/r2dii.data/, https://github.com/2DegreesInvesting/r2dii.data","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/r2dii.data/r2dii.data_0.6.1.tar.gz","https://cran.r-project.org/src/contrib/r2dii.data_0.6.1.tar.gz"],"subdirs":["noarch"],"summary":"These datasets support the implementation in R of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between corporate lending portfolios and climate scenarios (<https://2degrees-investing.org/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Because both financial institutions and market data providers keep their data private, this package provides fake, public data to enable the development and use of 'PACTA' in R.","text_prefix":false,"timestamp":1643645528,"version":"0.6.1"},"r-r2dii.match":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://2degreesinvesting.github.io/r2dii.match/, https://github.com/2DegreesInvesting/r2dii.match","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/r2dii.match/r2dii.match_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/r2dii.match_0.4.1.tar.gz"],"subdirs":["noarch"],"summary":"These tools implement in R a fundamental part of the software 'PACTA' (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (<https://2degrees-investing.org/>). Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals.","text_prefix":false,"timestamp":1651804822,"version":"0.4.1"},"r-r2html":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/nalimilan/R2HTML","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R2HTML/R2HTML_2.3.4.tar.gz","https://cran.r-project.org/src/contrib/R2HTML_2.3.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Includes HTML function and methods to write in an HTML file. Thus, making HTML reports is easy. Includes a function that allows redirection on the fly, which appears to be very useful for teaching purpose, as the student can keep a copy of the produced output to keep all that he did during the course. Package comes with a vignette describing how to write HTML reports for statistical analysis. Finally, a driver for 'Sweave' allows to parse HTML flat files containing R code and to automatically write the corresponding outputs (tables and graphs).","text_prefix":false,"timestamp":1539917944,"version":"2.3.4"},"r-r2jags":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/suyusung/R2jags/","home":"https://CRAN.R-project.org/package=R2jags","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R2jags/R2jags_0.8-9.tar.gz","https://cran.r-project.org/src/contrib/R2jags_0.8-9.tar.gz"],"subdirs":["noarch"],"summary":"Providing wrapper functions to implement Bayesian analysis in JAGS.  Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.","text_prefix":false,"timestamp":1621952294,"version":"0.8_9"},"r-r2r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/vgherard/r2r","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/r2r/r2r_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/r2r_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Implementation of hash tables (hash sets and hash maps) in R, featuring arbitrary R objects as keys, arbitrary hash and key-comparison functions, and customizable behaviour upon queries of missing keys.","text_prefix":false,"timestamp":1772606622,"version":"0.1.2"},"r-r2rtf":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://merck.github.io/r2rtf/, https://github.com/Merck/r2rtf","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/r2rtf/r2rtf_1.3.1.tar.gz","https://cran.r-project.org/src/contrib/r2rtf_1.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Create production-ready Rich Text Format (RTF) table and figure with flexible format.","text_prefix":false,"timestamp":1781086113,"version":"1.3.1"},"r-r2winbugs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=R2WinBUGS","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R2WinBUGS/R2WinBUGS_2.1-24.tar.gz","https://cran.r-project.org/src/contrib/R2WinBUGS_2.1-24.tar.gz"],"subdirs":["noarch"],"summary":"Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class \"bugs\" for 'BUGS'  results and functions to work with that class. Function write.model() allows a 'BUGS' model file to be written.   The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.","text_prefix":false,"timestamp":1772006222,"version":"2.1_24"},"r-r6":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://r6.r-lib.org, https://github.com/r-lib/R6/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R6/R6_2.6.1.tar.gz","https://cran.r-project.org/src/contrib/R6_2.6.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Creates classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. These classes allow public and private members, and they support inheritance, even when the classes are defined in different packages.","text_prefix":false,"timestamp":1535638467,"version":"2.6.1"},"r-r62s3":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/RaphaelS1/R62S3/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R62S3/R62S3_1.4.1.tar.gz","https://cran.r-project.org/src/contrib/R62S3_1.4.1.tar.gz"],"subdirs":["noarch"],"summary":"After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects.","text_prefix":false,"timestamp":1758518337,"version":"1.4.1"},"r-r6p":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://tidylab.github.io/R6P/, https://github.com/tidylab/R6P","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/R6P/R6P_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/R6P_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. 'R6P' provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess.","text_prefix":false,"timestamp":1633352612,"version":"0.4.0"},"r-raceid":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RaceID","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RaceID/RaceID_0.4.2.tar.gz","https://cran.r-project.org/src/contrib/RaceID_0.4.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Application of 'RaceID' allows inference of cell types and prediction of lineage trees by he StemID2 algorithm. Herman, J.S., Sagar, Gr\u00fcn D. 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This package provides an R interface specifically to the radar chart, sometimes called a spider chart, for visualising multivariate data.","text_prefix":false,"timestamp":1758601784,"version":"0.3.1"},"r-radiant.data":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/radiant-rstats/radiant.data","doc_url":"https://radiant-rstats.github.io/docs","home":"https://radiant-rstats.github.io/radiant.data","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/radiant.data/radiant.data_1.6.9.tar.gz","https://cloud.r-project.org/src/contrib/radiant.data_1.6.9.tar.gz"],"subdirs":["noarch"],"summary":"The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.","text_prefix":false,"timestamp":1636707169,"version":"1.6.9"},"r-radix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/radix","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/radix/radix_0.6.tar.gz","https://cran.r-project.org/src/contrib/radix_0.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Scientific and technical article format for the web. 'Radix' articles  feature attractive, reader-friendly typography, flexible layout options for visualizations, and full support for footnotes and citations.","text_prefix":false,"timestamp":1538894360,"version":"0.6"},"r-rafalib":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rafalib","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rafalib/rafalib_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/rafalib_1.0.4.tar.gz"],"subdirs":["noarch"],"summary":"A series of shortcuts for routine tasks originally developed by Rafael A. 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The 'ragg' package provides a set of graphic devices based on AGG to use as alternative to the raster devices provided through the 'grDevices' package.","text_prefix":false,"timestamp":1602057754,"version":"1.5.2"},"r-ragtop":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ragtop","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ragtop/ragtop_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/ragtop_2.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Algorithms to price American and European equity options, convertible bonds and a variety of other financial derivatives. It uses an extension of the usual Black-Scholes model in which jump to default may occur at a probability specified by a power-law link between stock price and hazard rate as found in the paper by Takahashi, Kobayashi, and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>.  We use ideas and techniques from Andersen and Buffum (2002) <doi:10.2139/ssrn.355308> and Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.","text_prefix":false,"timestamp":1783561704,"version":"2.0.0"},"r-rainbow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://sites.google.com/site/hanlinshangswebsite/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rainbow/rainbow_3.8.tar.gz","https://cran.r-project.org/src/contrib/rainbow_3.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Visualizing functional data and identifying functional outliers.","text_prefix":false,"timestamp":1541475083,"version":"3.8"},"r-ramcharts":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://datastorm-open.github.io/introduction_ramcharts/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rAmCharts/rAmCharts_2.1.16.tar.gz","https://cran.r-project.org/src/contrib/rAmCharts_2.1.16.tar.gz"],"subdirs":["noarch"],"summary":"Provides an R interface for using 'AmCharts' Library. Based on 'htmlwidgets', it provides a global architecture to generate 'JavaScript' source code for charts. Most of classes in the library have their equivalent in R with S4 classes; for those classes, not all properties have been referenced but can easily be added in the constructors. Complex properties (e.g. 'JavaScript' object) can be passed as named list. See examples at <http://datastorm-open.github.io/introduction_ramcharts/> and <http://www.amcharts.com/> for more information about the library. The package includes the free version of 'AmCharts' Library. Its only limitation is a small link to the web site displayed on your charts. If you enjoy this library, do not hesitate to refer to this page <http://www.amcharts.com/online-store/> to purchase a licence, and thus support its creators and get a period of Priority Support. See also <http://www.amcharts.com/about/> for more information about 'AmCharts' company.","text_prefix":false,"timestamp":1622146926,"version":"2.1.16"},"r-rams":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/wkumler/RaMS","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RaMS/RaMS_1.4.3.tar.gz","https://cran.r-project.org/src/contrib/RaMS_1.4.3.tar.gz"],"subdirs":["noarch"],"summary":"R-based access to mass-spectrometry (MS) data. While many packages exist to process MS data, many of these make it difficult to access the underlying mass-to-charge ratio (m/z), intensity, and retention time of the files themselves. This package is designed to format MS data in a tidy fashion and allows the user perform the plotting and analysis.","text_prefix":false,"timestamp":1773519041,"version":"1.4.3"},"r-randomcolor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=randomcoloR","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/randomcoloR/randomcoloR_1.1.0.1.tar.gz","https://cran.r-project.org/src/contrib/randomcoloR_1.1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Simple methods to generate attractive random colors. The random colors are from a wrapper of 'randomColor.js' <https://github.com/davidmerfield/randomColor>. In addition, it also generates optimally distinct colors based on k-means (inspired by 'IWantHue' <https://github.com/medialab/iwanthue>).","text_prefix":false,"timestamp":1564573259,"version":"1.1.0.1"},"r-randomfields":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://ms.math.uni-mannheim.de/de/publications/software/randomfields","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RandomFields/RandomFields_3.3.14.tar.gz","https://cran.r-project.org/src/contrib/RandomFields_3.3.14.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Methods for the inference on and the simulation of Gaussian fields are provided, as well as methods for the simulation of extreme value random fields.","text_prefix":false,"timestamp":1635888457,"version":"3.3.14"},"r-randomfieldsutils":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://ms.math.uni-mannheim.de/de/publications/software","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RandomFieldsUtils/RandomFieldsUtils_1.2.5.tar.gz","https://cran.r-project.org/src/contrib/RandomFieldsUtils_1.2.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions.","text_prefix":false,"timestamp":1633466594,"version":"1.2.5"},"r-randomforest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.stat.berkeley.edu/~breiman/RandomForests/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/randomForest/randomForest_4.7-1.2.tar.gz","https://cran.r-project.org/src/contrib/randomForest_4.7-1.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.","text_prefix":false,"timestamp":1533311756,"version":"4.7_1.2"},"r-randomforestsrc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://web.ccs.miami.edu/~hishwaran http://www.kogalur.com https://github.com/kogalur/randomForestSRC","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/randomForestSRC/randomForestSRC_3.9.0.tar.gz","https://cloud.r-project.org/src/contrib/randomForestSRC_3.9.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fast OpenMP parallel computing of Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package.  Handles missing data and now includes multivariate, unsupervised forests, quantile regression and solutions for class imbalanced data.  New fast interface using subsampling and confidence regions for variable importance.","text_prefix":false,"timestamp":1617177504,"version":"3.9.0"},"r-randomizr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://declaredesign.org/r/randomizr/, https://github.com/DeclareDesign/randomizr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/randomizr/randomizr_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/randomizr_1.0.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Generates random assignments for common experimental designs and  random samples for common sampling designs.","text_prefix":false,"timestamp":1563620497,"version":"1.0.1"},"r-randtests":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=randtests","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/randtests/randtests_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/randtests_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Several non parametric randomness tests for numeric sequences","text_prefix":false,"timestamp":1621923748,"version":"1.0.2"},"r-randtoolbox":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=randtoolbox","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/randtoolbox/randtoolbox_2.0.5.tar.gz","https://cran.r-project.org/src/contrib/randtoolbox_2.0.5.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Provides (1) pseudo random generators - general linear congruential generators,  multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some generator tests - the gap test, the serial test, the poker test. See e.g. Gentle (2003) <doi:10.1007/b97336>. The package can be provided without the rngWELL dependency on demand. Take a look at the Distribution task view of types and tests of random number generators. Version in Memoriam of Diethelm and Barbara Wuertz.","text_prefix":false,"timestamp":1667936249,"version":"2.0.5"},"r-rang":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/chainsawriot/rang","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rang/rang_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/rang_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.","text_prefix":false,"timestamp":1758594776,"version":"0.3.0"},"r-rangebuilder":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ptitle/rangeBuilder","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rangeBuilder/rangeBuilder_2.2.tar.gz","https://cran.r-project.org/src/contrib/rangeBuilder_2.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.","text_prefix":false,"timestamp":1730331357,"version":"2.2"},"r-rangemodelmetadata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rangeModelMetadata","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rangeModelMetadata/rangeModelMetadata_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/rangeModelMetadata_0.1.5.tar.gz"],"subdirs":["noarch"],"summary":"Range Modeling Metadata Standards (RMMS) address three challenges: they (i) are designed for convenience to encourage use, (ii) accommodate a wide variety of applications, and (iii) are extensible to allow the community of range modelers to steer it as needed. RMMS are based on a data dictionary that specifies a hierarchical structure to catalog different aspects of the range modeling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to provide their own values. Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in more detail. Note that users who prefer to use the R package 'ecospat' can obtain it from <https://github.com/ecospat/ecospat>.","text_prefix":false,"timestamp":1759447015,"version":"0.1.5"},"r-ranger":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/imbs-hl/ranger","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ranger/ranger_0.18.0.tar.gz","https://cran.r-project.org/src/contrib/ranger_0.18.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix')  can be directly analyzed.","text_prefix":false,"timestamp":1578602836,"version":"0.18.0"},"r-rankaggreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RankAggreg","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RankAggreg/RankAggreg_0.6.6.tar.gz","https://cran.r-project.org/src/contrib/RankAggreg_0.6.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Performs aggregation of ordered lists based on the ranks using several different algorithms: Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm (for small problems).","text_prefix":false,"timestamp":1758023158,"version":"0.6.6"},"r-rann":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jefferis/RANN","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RANN/RANN_2.6.2.tar.gz","https://cran.r-project.org/src/contrib/RANN_2.6.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L2 (Euclidean) metric. Please see package 'RANN.L1' for the same functionality using the L1 (Manhattan, taxicab) metric.","text_prefix":false,"timestamp":1542570849,"version":"2.6.2"},"r-rann.l1":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jefferis/RANN/tree/master-L1","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RANN.L1/RANN.L1_2.5.2.tar.gz","https://cran.r-project.org/src/contrib/RANN.L1_2.5.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L1 (Manhattan, taxicab) metric. Please see package 'RANN' for the same functionality using the L2 (Euclidean) metric.","text_prefix":false,"timestamp":1758624064,"version":"2.5.2"},"r-rapiclient":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bergant/rapiclient","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rapiclient/rapiclient_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/rapiclient_0.1.8.tar.gz"],"subdirs":["noarch"],"summary":"Access services specified in OpenAPI (formerly Swagger) format. It is not a code generator. 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The R Core group is the original author of the code made available with slight modifications by this package.","text_prefix":false,"timestamp":1628260936,"version":"0.0.11"},"r-rapidjsonr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rapidjsonr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rapidjsonr/rapidjsonr_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/rapidjsonr_1.2.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.","text_prefix":false,"timestamp":1567635235,"version":"1.2.1"},"r-rapidoc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/meztez/rapidoc","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rapidoc/rapidoc_9.3.4.tar.gz","https://cran.r-project.org/src/contrib/rapidoc_9.3.4.tar.gz"],"subdirs":["noarch"],"summary":"A collection of 'HTML', 'JavaScript', 'CSS' and fonts assets that generate 'RapiDoc' documentation from an 'OpenAPI' Specification: <https://mrin9.github.io/RapiDoc/>.","text_prefix":false,"timestamp":1758084370,"version":"9.3.4"},"r-rapiserialize":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RApiSerialize","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RApiSerialize/RApiSerialize_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/RApiSerialize_0.1.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"This package provides other packages with access to the internal R serialization code. Access to this code is provided at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package. . This packages builds on the Rhpc package by Junji Nakano and Ei-ji Nakama which also includes a (partial) copy of the file src/main/serialize.c from R itself. . The R Core group is the original author of the serialization code made available by this package.","text_prefix":false,"timestamp":1659907339,"version":"0.1.4"},"r-rapp":{"activate.d":true,"binary_prefix":false,"deactivate.d":true,"home":"https://github.com/r-lib/Rapp","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rapp/Rapp_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/Rapp_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Run simple 'R' scripts as command line applications, with automatic robust and convenient support for command line arguments. This package provides 'Rapp', an alternative 'R' front-end similar to 'Rscript', that enables this.","text_prefix":false,"timestamp":1777299185,"version":"0.3.0"},"r-rappdirs":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/hadley/rappdirs https://github.com/ActiveState/appdirs","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rappdirs/rappdirs_0.3.4.tar.gz","https://cran.r-project.org/src/contrib/rappdirs_0.3.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' (\\url{https://github.com/ActiveState/appdirs}) to R.","text_prefix":false,"timestamp":1611763350,"version":"0.3.4"},"r-rapport":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://rapport-package.info/","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rapport/rapport_1.2.tar.gz","https://cran.r-project.org/src/contrib/rapport_1.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.","text_prefix":false,"timestamp":0,"version":"1.2"},"r-rapportools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rapportools","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rapportools/rapportools_1.2.tar.gz","https://cran.r-project.org/src/contrib/rapportools_1.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Helper functions that act as wrappers to more advanced statistical methods with the advantage of having sane defaults for quick reporting.","text_prefix":false,"timestamp":0,"version":"1.2"},"r-rarpack":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/yixuan/rARPACK","license":"BSD_3_clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rARPACK/rARPACK_0.11-0.tar.gz","https://cran.r-project.org/src/contrib/rARPACK_0.11-0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Previously an R wrapper of the 'ARPACK' library <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the R package 'RSpectra', an R interface to the 'Spectra' library <http://yixuan.cos.name/spectra/> for solving large scale eigenvalue/vector problems. The current version of 'rARPACK' simply imports and exports the functions provided by 'RSpectra'. New users of 'rARPACK' are advised to switch to the 'RSpectra' package.","text_prefix":false,"timestamp":1539851265,"version":"0.11_0"},"r-raster":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/rspatial/raster","home":"https://CRAN.R-project.org/package=raster","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/raster/raster_3.6-32.tar.gz","https://cran.r-project.org/src/contrib/raster_3.6-32.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported. There is a also support for vector data operations such as intersections. See the manual and tutorials on <https://rspatial.org/> to get started.","text_prefix":false,"timestamp":1594972192,"version":"3.6_32"},"r-rasterdiv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rasterdiv","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rasterdiv/rasterdiv_0.3.8.tar.gz","https://cran.r-project.org/src/contrib/rasterdiv_0.3.8.tar.gz"],"subdirs":["noarch"],"summary":"Providing functions to calculate indices of diversity on numerical matrices based on information theory. The rationale behind the package is described in Rocchini, Marcantonio and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039> and Rocchini, Marcantonio,..., Ricotta (2021) <doi:10.1101/2021.01.23.427872>.","text_prefix":false,"timestamp":1665704499,"version":"0.3.8"},"r-rastervis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://oscarperpinan.github.io/rastervis","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rasterVis/rasterVis_0.51.7.tar.gz","https://cran.r-project.org/src/contrib/rasterVis_0.51.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.","text_prefix":false,"timestamp":0,"version":"0.51.7"},"r-ratelimitr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tarakc02/ratelimitr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ratelimitr/ratelimitr_0.4.2.tar.gz","https://cran.r-project.org/src/contrib/ratelimitr_0.4.2.tar.gz"],"subdirs":["noarch"],"summary":"Allows to limit the rate at which one or more functions can be called.","text_prefix":false,"timestamp":1760373256,"version":"0.4.2"},"r-raters":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=raters","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/raters/raters_2.1.1.tar.gz","https://cran.r-project.org/src/contrib/raters_2.1.1.tar.gz"],"subdirs":["noarch"],"summary":"The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.","text_prefix":false,"timestamp":1621923774,"version":"2.1.1"},"r-rathena":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/DyfanJones/RAthena","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RAthena/RAthena_2.6.3.tar.gz","https://cran.r-project.org/src/contrib/RAthena_2.6.3.tar.gz"],"subdirs":["noarch"],"summary":"Designed to be compatible with the R package 'DBI' (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this 'Python' 'Boto3' Software Development Kit ('SDK') <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html> is used as a driver.","text_prefix":false,"timestamp":1759328501,"version":"2.6.3"},"r-rationalexp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RationalExp","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RationalExp/RationalExp_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/RationalExp_0.2.2.tar.gz"],"subdirs":["noarch"],"summary":"We implement a test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs from D'Haultfoeuille, Gaillac, and Maurel (2018) <doi:10.3386/w25274>. This test can be used in cases where realizations and subjective beliefs are observed in two different datasets that cannot be matched, or when they are observed in the same dataset. The package also computes the estimator of the minimal deviations from rational expectations than can be rationalized by the data.","text_prefix":false,"timestamp":1546352365,"version":"0.2.2"},"r-rattle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://togaware.com/projects/rattle/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rattle/rattle_5.6.2.tar.gz","https://cran.r-project.org/src/contrib/rattle_5.6.2.tar.gz"],"subdirs":["noarch"],"summary":"The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. A Gnome (RGtk2) based graphical interface is included with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file (or via ODBC), transform and explore the data, build and evaluate models, and export models as PMML (predictive modelling markup language) or as scores. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself.","text_prefix":false,"timestamp":1622071144,"version":"5.6.2"},"r-ravedash":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://dipterix.org/ravedash/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ravedash/ravedash_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/ravedash_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Dashboard system to display the analysis results produced by 'RAVE' (Magnotti J.F., Wang Z., Beauchamp M.S. (2020), R analysis and visualizations of 'iEEG' <doi:10.1016/j.neuroimage.2020.117341>). Provides infrastructure to integrate customized analysis pipelines into dashboard modules, including file structures, front-end widgets, and event handlers.","text_prefix":false,"timestamp":1758944627,"version":"0.1.2"},"r-raveio":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://beauchamplab.github.io/raveio/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/raveio/raveio_0.9.0.tar.gz","https://cran.r-project.org/src/contrib/raveio_0.9.0.tar.gz"],"subdirs":["noarch"],"summary":"Includes multiple cross-platform read/write interfaces for 'RAVE' project. 'RAVE' stands for \"R analysis and visualization of human intracranial electroencephalography data\". The whole project aims at providing powerful free-source package that analyze brain recordings from patients with electrodes placed on the cortical surface or inserted into the brain. 'raveio' as part of this project provides tools to read/write neurophysiology data from/to 'RAVE' file structure, as well as several popular formats including  'EDF(+)', 'Matlab', 'BIDS-iEEG', and 'HDF5', etc. Documentation and examples about 'RAVE' project are provided at <https://openwetware.org/wiki/RAVE>, and the paper by John F. Magnotti, Zhengjia Wang, Michael S. Beauchamp (2020) <doi:10.1016/j.neuroimage.2020.117341>; see 'citation(\"raveio\")' for details.","text_prefix":false,"timestamp":1667290315,"version":"0.9.0"},"r-ravetools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://dipterix.org/ravetools/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ravetools/ravetools_0.4.0.tar.gz","https://cloud.r-project.org/src/contrib/ravetools_0.4.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implemented fast and memory-efficient Notch-filter, Welch-periodogram, discrete wavelet spectrogram for minutes of high-resolution signals, fast 3D convolution, image registration, 3D mesh manipulation; providing fundamental toolbox for intracranial Electroencephalography (iEEG) pipelines. Documentation and examples about 'RAVE' project are provided at <https://openwetware.org/wiki/RAVE>, and the paper by John F. Magnotti, Zhengjia Wang, Michael S. Beauchamp (2020) <doi:10.1016/j.neuroimage.2020.117341>; see 'citation(\"ravetools\")' for details.","text_prefix":false,"timestamp":1667248331,"version":"0.4.0"},"r-rayimage":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/tylermorganwall/rayimage","home":"https://www.rayimage.dev","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rayimage/rayimage_0.15.1.tar.gz","https://cran.r-project.org/src/contrib/rayimage_0.15.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image overlays, generate camera vignette effects, and add titles to images.","text_prefix":false,"timestamp":1665488769,"version":"0.15.1"},"r-rayrender":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.rayrender.net, https://github.com/tylermorganwall/rayrender","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rayrender/rayrender_0.38.10.tar.gz","https://cran.r-project.org/src/contrib/rayrender_0.38.10.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the \"Ray Tracing in One Weekend\" book series. 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Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, 'Wavefront' OBJ file export, and the ability to save 3D visualizations to a 3D printable format.","text_prefix":false,"timestamp":1618167973,"version":"0.41.2"},"r-rayvertex":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.rayvertex.com, https://github.com/tylermorganwall/rayvertex","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rayvertex/rayvertex_0.12.0.tar.gz","https://cran.r-project.org/src/contrib/rayvertex_0.12.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. 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The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. 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A full ready-to-work structure is set up with some additional features: version control, remote repository creation, CI/CD configuration (check package integrity under several OS, test code with 'testthat', and build and deploy website using 'pkgdown'). This package heavily relies on the R packages 'devtools' and 'usethis' and follows recommendations made by Wickham H. (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) <doi:10.7287/peerj.preprints.3192v2>.","text_prefix":false,"timestamp":1758690766,"version":"1.4"},"r-rcpp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/RcppCore/Rcpp","doc_url":"http://dirk.eddelbuettel.com/code/rcpp.html","home":"http://www.rcpp.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rcpp/Rcpp_1.1.2.tar.gz","https://cran.r-project.org/src/contrib/Rcpp_1.1.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation(\"Rcpp\")' for details.","text_prefix":false,"timestamp":0,"version":"1.1.2"},"r-rcppalgos":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jwood000/RcppAlgos, https://gmplib.org/, https://github.com/kimwalisch/primesieve, http://libdivide.com, https://github.com/kimwalisch/primecount, http://ridiculousfish.com/, http://sweet.ua.pt/tos/software/prime_sieve.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/RcppAlgos/RcppAlgos_2.10.2.tar.gz","https://cloud.r-project.org/src/contrib/RcppAlgos_2.10.2.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Provides optimized functions and flexible combinatorial iterators implemented in C++ with 'Rcpp' for solving problems in combinatorics and computational mathematics. Utilizes parallel programming via 'RcppThread' for maximal performance. Also makes use of the RMatrix class from the 'RcppParallel' library. There are combination/permutation functions with constraint parameters that allow for generation of all results of a vector meeting specific criteria (e.g. generating integer partitions/compositions or finding all combinations such that the sum is between two bounds). Capable of generating specific combinations/permutations (e.g. retrieve only the nth lexicographical result) which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large (e.g. comboSample(10000, 500, n = 4)). Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics. Some of these functions make use of the fast integer division library 'libdivide'. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is also efficient for large numbers by using the cache friendly improvements originally developed by Tom\u00e1s Oliveira. Finally, there is a prime counting function that implements Legendre's formula based on the work of Kim Walisch.","text_prefix":false,"timestamp":1648766586,"version":"2.10.2"},"r-rcppannoy":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RcppAnnoy","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppAnnoy/RcppAnnoy_0.0.23.tar.gz","https://cran.r-project.org/src/contrib/RcppAnnoy_0.0.23.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Annoy' is a small C++ library for Approximate Nearest Neighbors  written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license.","text_prefix":false,"timestamp":1550570214,"version":"0.0.23"},"r-rcpparmadillo":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/RcppCore/RcppArmadillo","home":"https://dirk.eddelbuettel.com/code/rcpp.armadillo.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/RcppArmadillo/RcppArmadillo_15.6.2-1.tar.gz","https://cloud.r-project.org/src/contrib/RcppArmadillo_15.6.2-1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that Armadillo requires a fairly recent compiler; for the g++ family at least version 4.6.* is required.","text_prefix":false,"timestamp":1556555848,"version":"15.6.2_1"},"r-rcppcctz":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/eddelbuettel/rcppcctz, https://dirk.eddelbuettel.com/code/rcpp.cctz.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppCCTZ/RcppCCTZ_0.2.14.tar.gz","https://cran.r-project.org/src/contrib/RcppCCTZ_0.2.14.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is a C++ library for translating between absolute and civil times using the rules of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License, is included in this package. See <https://github.com/google/cctz> for more details.","text_prefix":true,"timestamp":1665387497,"version":"0.2.14"},"r-rcppclassic":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RcppClassic","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppClassic/RcppClassic_0.9.14.tar.gz","https://cran.r-project.org/src/contrib/RcppClassic_0.9.14.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The 'RcppClassic' package provides a deprecated C++ library which facilitates the integration of R and C++. New projects should use the new 'Rcpp' 'API' in the 'Rcpp' package.","text_prefix":false,"timestamp":1564424020,"version":"0.9.14"},"r-rcppclock":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RcppClock","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppClock/RcppClock_1.1.tar.gz","https://cran.r-project.org/src/contrib/RcppClock_1.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Time the execution of overlapping or unique 'Rcpp' code chunks using convenient methods, seamlessly write timing results to an 'RcppClock' object in the R global environment, and summarize and/or plot the results in R.","text_prefix":false,"timestamp":1759049901,"version":"1.1"},"r-rcppcnpy":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://dirk.eddelbuettel.com/code/rcpp.cnpy.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppCNPy/RcppCNPy_0.2.15.tar.gz","https://cran.r-project.org/src/contrib/RcppCNPy_0.2.15.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The 'cnpy' library written by Carl Rogers provides read and write facilities for files created with (or for) the 'NumPy' extension for 'Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with -std=c++11 which should be the default on all platforms since the release of R 3.3.0.","text_prefix":false,"timestamp":1621762244,"version":"0.2.15"},"r-rcppdate":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/eddelbuettel/rcppdate","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppDate/RcppDate_0.0.7.tar.gz","https://cran.r-project.org/src/contrib/RcppDate_0.0.7.tar.gz"],"subdirs":["noarch"],"summary":"'date' is a C++ header library offering extensive date and time functionality for the C++11, C++14 and C++17 standards written by Howard Hinnant and released under the MIT license. A slightly modified version has been accepted (along with 'tz.h') as part of C++20. This package regroups all header files from the upstream repository by Howard Hinnant so that other R packages can use them in their C++ code. At present, few of the types have explicit 'Rcpp' wrapper though these may be added as needed.","text_prefix":false,"timestamp":1611943364,"version":"0.0.7"},"r-rcppde":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RcppDE","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppDE/RcppDE_0.1.9.tar.gz","https://cran.r-project.org/src/contrib/RcppDE_0.1.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation \"easier, shorter, faster: pick any two\" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++.  The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'.","text_prefix":false,"timestamp":1665495406,"version":"0.1.9"},"r-rcppdist":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/duckmayr/RcppDist","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppDist/RcppDist_0.1.1.1.tar.gz","https://cran.r-project.org/src/contrib/RcppDist_0.1.1.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The 'Rcpp' package provides a C++ library to make it easier to use C++ with R. R and 'Rcpp' provide functions for a variety of statistical distributions. Several R packages make functions available to R for additional statistical distributions. However, to access these functions from C++ code, a costly call to the R functions must be made. 'RcppDist' provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'. Functions are available that return a 'NumericVector' as well as doubles, and for multivariate or matrix distributions, 'Armadillo' vectors and matrices. 'RcppDist' provides functions for the following distributions: the four parameter beta distribution; the location- scale t distribution; the truncated normal distribution; the truncated t distribution; a truncated location-scale t distribution; the triangle distribution; the multivariate normal distribution*; the multivariate t distribution*; the Wishart distribution*; and the inverse Wishart distribution*. Distributions marked with an asterisk rely on 'RcppArmadillo'.","text_prefix":false,"timestamp":1766499247,"version":"0.1.1.1"},"r-rcppdpr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RcppDPR","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppDPR/RcppDPR_0.1.10.tar.gz","https://cran.r-project.org/src/contrib/RcppDPR_0.1.10.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.","text_prefix":false,"timestamp":1758404067,"version":"0.1.10"},"r-rcppeigen":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/cran/RcppEigen","home":"http://dirk.eddelbuettel.com/code/rcpp.eigen.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppEigen/RcppEigen_0.3.4.0.2.tar.gz","https://cran.r-project.org/src/contrib/RcppEigen_0.3.4.0.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms.  It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library (currently version 3.3.4). Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.","text_prefix":false,"timestamp":0,"version":"0.3.4.0.2"},"r-rcppensmallen":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/coatless/rcppensmallen, https://github.com/mlpack/ensmallen, http://ensmallen.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppEnsmallen/RcppEnsmallen_0.3.11.0.1.tar.gz","https://cran.r-project.org/src/contrib/RcppEnsmallen_0.3.11.0.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++11' and 'Armadillo' 8.400 or later.","text_prefix":false,"timestamp":1604281712,"version":"0.3.11.0.1"},"r-rcppgsl":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"doc_url":"https://dirk.eddelbuettel.com/code/rcpp.gsl.html","home":"https://github.com/eddelbuettel/rcppgsl","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppGSL/RcppGSL_0.3.14.tar.gz","https://cran.r-project.org/src/contrib/RcppGSL_0.3.14.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to  write a similar package against another library.","text_prefix":false,"timestamp":1533114146,"version":"0.3.14"},"r-rcpphnsw":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jlmelville/rcpphnsw","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppHNSW/RcppHNSW_0.7.0.tar.gz","https://cran.r-project.org/src/contrib/RcppHNSW_0.7.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R interface by relying on the 'Rcpp' package. See <https://github.com/nmslib/hnswlib> for more on 'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache License.","text_prefix":false,"timestamp":1588696702,"version":"0.7.0"},"r-rcpphungarian":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jsilve24/RcppHungarian","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppHungarian/RcppHungarian_0.3.tar.gz","https://cran.r-project.org/src/contrib/RcppHungarian_0.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Header library and R functions to solve minimum cost bipartite matching problem using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>; Kuhn (1955) <doi:10.1002/nav.3800020109>). This is a repackaging of code written by Cong Ma in the GitHub repo <https://github.com/mcximing/hungarian-algorithm-cpp>.","text_prefix":false,"timestamp":1773523509,"version":"0.3"},"r-rcppint64":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/eddelbuettel/rcppint64","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppInt64/RcppInt64_0.0.4.tar.gz","https://cran.r-project.org/src/contrib/RcppInt64_0.0.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Int64' values can be created and accessed via the 'bit64' package and its 'integer64' class which package the 'int64' representation cleverly into a 'double'. This packages helps conversions between 'R' and 'C++' via several helper functions provided via a single header file. A complete example client package is included as an illustration.","text_prefix":false,"timestamp":1757946962,"version":"0.0.4"},"r-rcppml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/zdebruine/RcppML","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppML/RcppML_0.3.7.1.tar.gz","https://cran.r-project.org/src/contrib/RcppML_0.3.7.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.","text_prefix":false,"timestamp":1773971845,"version":"0.3.7.1"},"r-rcppnumerical":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/yixuan/RcppNumerical","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RcppNumerical/RcppNumerical_0.7-0.tar.gz","https://cran.r-project.org/src/contrib/RcppNumerical_0.7-0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A collection of open source libraries for numerical computing (numerical integration, optimization, etc.) and their integration with 'Rcpp'.","text_prefix":false,"timestamp":1563617085,"version":"0.7_0"},"r-rcppparallel":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/RcppCore/RcppParallel","home":"http://rcppcore.github.io/RcppParallel","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"5.1.11_1":{"weak":["tbb-devel >=2022.3.0,<2022.4.0a0"]},"5.1.11_2":{"weak":["tbb-devel >=2023.0.0,<2023.1.0a0"]},"6.2.0":{"weak":["tbb-devel >=2023.1.0,<2023.2.0a0"]},"6.2.1":{"weak":["tbb-devel >=2023.1.0,<2023.2.0a0"]}},"source_url":["https://cloud.r-project.org/src/contrib/Archive/RcppParallel/RcppParallel_6.2.1.tar.gz","https://cloud.r-project.org/src/contrib/RcppParallel_6.2.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"High level functions for parallel programming with 'Rcpp'. For example, the 'parallelFor()' function can be used to convert the work of a standard serial \"for\" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values.","text_prefix":false,"timestamp":1555452251,"version":"6.2.1"},"r-rcppplanc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/welch-lab/RcppPlanc/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/welch-lab/RcppPlanc/archive/refs/tags/1.0.0-rc3.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Rcpp' bindings for 'PLANC', a highly parallel and extensible NMF/NTF  (Non-negative Matrix/Tensor Factorization) library. Wraps algorithms described in Kannan et. al (2018) <doi:10.1109/TKDE.2017.2767592> and Eswar et. al (2021) <doi:10.1145/3432185>. Implements algorithms described in Welch et al. (2019) <doi:10.1016/j.cell.2019.05.006>, Gao et al. 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As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. d Numerous examples are provided in the eight subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a \"web-application\" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo') and 'eigen' (for 'RInside' use with 'RcppEigen').  The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well.","text_prefix":false,"timestamp":1545959342,"version":"0.2.19"},"r-rintrojs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/carlganz/rintrojs","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rintrojs/rintrojs_0.3.4.tar.gz","https://cran.r-project.org/src/contrib/rintrojs_0.3.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A wrapper for the 'Intro.js' library (For more info: <http://www.introjs.com>).  This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny'  application. It supports both static introductions in the UI, and programmatic introductions from  the server-side.","text_prefix":false,"timestamp":1540160429,"version":"0.3.4"},"r-rio":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/leeper/rio","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rio/rio_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/rio_1.3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data structure from the file extension, reasonable defaults are used for data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly without explicit decompression, and fast import packages are used where appropriate. An additional convenience function, 'convert()', provides a simple method for converting between file types.","text_prefix":false,"timestamp":1536499634,"version":"1.3.0"},"r-rioja":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/nsj3/rioja","home":"http://www.staff.ncl.ac.uk/stephen.juggins/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rioja/rioja_1.0-7.tar.gz","https://cran.r-project.org/src/contrib/rioja_1.0-7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Stratigraphic diagrams, constrained clustering, transfer functions, and other methods for analysing Quaternary science data.","text_prefix":false,"timestamp":1546342248,"version":"1.0_7"},"r-riskclustr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.emilyzabor.com/riskclustr/, https://github.com/zabore/riskclustr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/riskclustr/riskclustr_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/riskclustr_0.4.1.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. 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Includes functions to work with the 'ITIS' REST API methods (<https://www.itis.gov/ws_description.html>), as well as the 'Solr' web service (<https://www.itis.gov/solr_documentation.html>).","text_prefix":false,"timestamp":0,"version":"1.0.0"},"r-riverplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://logfc.wordpress.com","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/riverplot/riverplot_0.10.tar.gz","https://cran.r-project.org/src/contrib/riverplot_0.10.tar.gz"],"subdirs":["noarch"],"summary":"Sankey plots are a type of diagram that is convenient to illustrate how flow of information, resources etc. separates and joins, much like observing how rivers split and merge. 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This allows R to connect to any DBMS that has a JDBC driver.","text_prefix":false,"timestamp":1574944569,"version":"0.2_10"},"r-rje":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rje","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rje/rje_1.12.1.tar.gz","https://cran.r-project.org/src/contrib/rje_1.12.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A series of functions in some way considered useful to the author.  These include functions for subsetting tables and generating indices for arrays, conditioning and intervening in probability distributions, generating combinations and more...","text_prefix":false,"timestamp":1578846363,"version":"1.12.1"},"r-rjson":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rjson","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rjson/rjson_0.2.23.tar.gz","https://cran.r-project.org/src/contrib/rjson_0.2.23.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Converts R object into JSON objects and vice-versa.","text_prefix":false,"timestamp":1533355161,"version":"0.2.23"},"r-rjsoncons":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mtmorgan/rjsoncons","home":"https://mtmorgan.github.io/rjsoncons/","license":"BSL-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rjsoncons/rjsoncons_1.3.3.tar.gz","https://cran.r-project.org/src/contrib/rjsoncons_1.3.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The 'jsoncons' <https://danielaparker.github.io/jsoncons/> 'C++' header-only library constructs representations from a 'JSON' character vector, and provides extensions for flexible queries and other operations on 'JSON' objects. This package has simple 'R' wrappers to support 'JSONpath' and 'JMESpath' queries into 'JSON' strings or 'R' objects. The 'jsoncons' library is also be easily linked to other packages for direct access to 'C++' functionality.","text_prefix":false,"timestamp":1780539918,"version":"1.3.3"},"r-rjsonio":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RJSONIO","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RJSONIO/RJSONIO_2.0.5.tar.gz","https://cran.r-project.org/src/contrib/RJSONIO_2.0.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"This is a package that allows conversion to and from  data in Javascript object notation (JSON) format. This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code and allows R programmers to read and convert JSON content to R objects. This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases. This package uses methods and is readily extensible by defining methods for different classes,  vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R.  The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional options to allow customizing the generation and processing of JSON content. This package uses libjson rather than implementing yet another JSON parser. The aim is to support other general projects by building on their work, providing feedback and benefit from their ongoing development.","text_prefix":false,"timestamp":1546627393,"version":"2.0.5"},"r-rknn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rknn","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rknn/rknn_1.2-1.tar.gz","https://cran.r-project.org/src/contrib/rknn_1.2-1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Random knn classification and regression are implemented. Random knn based feature selection methods are also included. The approaches are mainly developed for high-dimensional data with small sample size.","text_prefix":false,"timestamp":1758572417,"version":"1.2_1"},"r-rlab":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.r-project.org, http://courses.ncsu.edu/ST370/distance/rlab","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rlab/Rlab_4.5.1.tar.gz","https://cran.r-project.org/src/contrib/Rlab_4.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Functions and Datasets Required for ST370 class","text_prefix":false,"timestamp":1621906889,"version":"4.5.1"},"r-rlabkey":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Rlabkey","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rlabkey/Rlabkey_3.5.0.tar.gz","https://cran.r-project.org/src/contrib/Rlabkey_3.5.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The 'LabKey' client library for R makes it easy for R users to load live data from a 'LabKey' Server, <http://www.labkey.com/>, into the R environment for analysis, provided users have permissions to read the data. 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The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html>. 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The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) <doi:10.1074/mcp.O114.037879>) for more details.","text_prefix":false,"timestamp":1758724496,"version":"1.0.1"},"r-rmtstat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RMTstat","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RMTstat/RMTstat_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/RMTstat_0.3.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions for working with the Tracy-Widom laws and other distributions  related to the eigenvalues of large Wishart matrices. 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(2006) <doi:10.1016/j.parco.2005.07.004>.","home":"http://mumps.enseeiht.fr/, https://github.com/sgsokol/rmumps/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rmumps/rmumps_5.2.1-43.tar.gz","https://cran.r-project.org/src/contrib/rmumps_5.2.1-43.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"R wrapper for MUMPS (Multifrontal Massively Parallel sparse direct Solver) library","text_prefix":false,"timestamp":1666283297,"version":"5.2.1_43"},"r-rmutil":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.commanster.eu/rcode.html","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rmutil/rmutil_1.1.10.tar.gz","https://cran.r-project.org/src/contrib/rmutil_1.1.10.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event'  (available at <http://www.commanster.eu/rcode.html>).","text_prefix":false,"timestamp":1568930212,"version":"1.1.10"},"r-rmvp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/XiaoleiLiuBio/rMVP","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rMVP/rMVP_1.4.6.tar.gz","https://cran.r-project.org/src/contrib/rMVP_1.4.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and 'SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).","text_prefix":false,"timestamp":1569326567,"version":"1.4.6"},"r-rmysql":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-dbi/rmysql","home":"https://downloads.mariadb.org/connector-c/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RMySQL/RMySQL_0.11.3.tar.gz","https://cran.r-project.org/src/contrib/RMySQL_0.11.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code ported from S-PLUS. A modern 'MySQL' client based on 'Rcpp' is available  from the 'RMariaDB' package.","text_prefix":false,"timestamp":1576350110,"version":"0.11.3"},"r-rmzqc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/MS-Quality-hub/rmzqc","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rmzqc/rmzqc_0.7.0.tar.gz","https://cran.r-project.org/src/contrib/rmzqc_0.7.0.tar.gz"],"subdirs":["noarch"],"summary":"Reads, writes and validates 'mzQC' files. The 'mzQC' format is a standardized file format for the exchange, transmission, and archiving of quality metrics derived from biological mass spectrometry data, as defined by the HUPO-PSI (Human Proteome Organisation - Proteomics Standards Initiative) Quality Control working group. See <https://hupo-psi.github.io/mzQC/> for details.","text_prefix":false,"timestamp":1758516641,"version":"0.7.0"},"r-rnames":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rnames","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rnames/rnames_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/rnames_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Recursive display of names and paths of all the items nested within sublists of a list object.","text_prefix":false,"timestamp":1758690751,"version":"1.0.1"},"r-rnanoflann":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ManosPapadakis95/Rnanoflann","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rnanoflann/Rnanoflann_0.0.3.tar.gz","https://cran.r-project.org/src/contrib/Rnanoflann_0.0.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Finds the k nearest neighbours for every point in a given dataset using Jose Luis' 'nanoflann' library. There is support for exact searches, fixed radius searches with 'kd' trees and two distances, the 'Euclidean' and 'Manhattan'. For more information see <https://github.com/jlblancoc/nanoflann>. Also, the 'nanoflann' library is exported and ready to be used via the linking to mechanism.","text_prefix":false,"timestamp":1767192000,"version":"0.0.3"},"r-rnaturalearth":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropenscilabs/rnaturalearth","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rnaturalearth/rnaturalearth_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/rnaturalearth_1.2.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Facilitates mapping by making natural earth map data from <http://www.naturalearthdata.com/> more easily available to R users.","text_prefix":false,"timestamp":1665547823,"version":"1.2.0"},"r-rnaturalearthdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropenscilabs/rnaturalearthdata","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rnaturalearthdata/rnaturalearthdata_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/rnaturalearthdata_1.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Vector map data from <http://www.naturalearthdata.com/>. Access functions are provided in the accompanying package 'rnaturalearth'.","text_prefix":false,"timestamp":1707506471,"version":"1.0.0"},"r-rncl":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/fmichonneau/rncl","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rncl/rncl_0.8.10.tar.gz","https://cran.r-project.org/src/contrib/rncl_0.8.10.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"An interface to the Nexus Class Library which allows parsing of NEXUS, Newick and other phylogenetic tree file formats. It provides elements of the file that can be used to build phylogenetic objects such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.","text_prefix":false,"timestamp":1665309176,"version":"0.8.10"},"r-rneos":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rneos","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rneos/rneos_0.4-1.tar.gz","https://cran.r-project.org/src/contrib/rneos_0.4-1.tar.gz"],"subdirs":["noarch"],"summary":"Within this package the XML-RPC API to NEOS <https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.","text_prefix":false,"timestamp":1769429687,"version":"0.4_1"},"r-rnetcdf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/mjwoods/RNetCDF","home":"https://www.unidata.ucar.edu/software/netcdf/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RNetCDF/RNetCDF_2.11-2.tar.gz","https://cran.r-project.org/src/contrib/RNetCDF_2.11-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An interface to the 'NetCDF' file formats designed by Unidata for efficient storage of array-oriented scientific data and descriptions. Most capabilities of 'NetCDF' version 4 are supported. Optional conversions of time units are enabled by 'UDUNITS' version 2, also from Unidata.","text_prefix":false,"timestamp":1571244261,"version":"2.11_2"},"r-rnexml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/RNeXML, https://github.com/ropensci/RNeXML","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RNeXML/RNeXML_2.4.11.tar.gz","https://cran.r-project.org/src/contrib/RNeXML_2.4.11.tar.gz"],"subdirs":["noarch"],"summary":"Provides access to phyloinformatic data in 'NeXML' format.  The package should add new functionality to R such as the possibility to manipulate 'NeXML' objects in more various and refined way and compatibility with 'ape' objects.","text_prefix":false,"timestamp":1580471899,"version":"2.4.11"},"r-rngtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://renozao.github.io/rngtools","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rngtools/rngtools_1.5.2.tar.gz","https://cran.r-project.org/src/contrib/rngtools_1.5.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a set of functions for working with Random Number Generators (RNGs). In particular, a generic S4 framework is defined for getting/setting the current RNG, or RNG data that are embedded into objects for reproducibility. Notably, convenient default methods greatly facilitate the way current RNG settings can be changed.","text_prefix":false,"timestamp":1536954017,"version":"1.5.2"},"r-rngwell":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rngWELL","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rngWELL/rngWELL_0.10-10.tar.gz","https://cran.r-project.org/src/contrib/rngWELL_0.10-10.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software. But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package.","text_prefix":false,"timestamp":1665364517,"version":"0.10_10"},"r-rnifti":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jonclayden/RNifti","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/RNifti/RNifti_1.10.0.tar.gz","https://cloud.r-project.org/src/contrib/RNifti_1.10.0.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Provides very fast read and write access to images stored in the NIfTI-1 and ANALYZE-7.5 formats, with seamless synchronisation between compiled C and interpreted R code. Also provides a C/C++ API that can be used by other packages. Not to be confused with 'RNiftyReg', which performs image registration.","text_prefix":false,"timestamp":1549183516,"version":"1.10.0"},"r-rniftyreg":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jonclayden/RNiftyReg","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/RNiftyReg/RNiftyReg_2.8.6.tar.gz","https://cloud.r-project.org/src/contrib/RNiftyReg_2.8.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides an 'R' interface to the 'NiftyReg' image registration tools <http://sourceforge.net/projects/niftyreg/>. Linear and nonlinear registration are supported, in two and three dimensions.","text_prefix":false,"timestamp":1568194004,"version":"2.8.6"},"r-rnomni":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RNOmni","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RNOmni/RNOmni_1.0.1.2.tar.gz","https://cran.r-project.org/src/contrib/RNOmni_1.0.1.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests dominate standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. \"Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies\" <doi:10.1111/biom.13214>.","text_prefix":false,"timestamp":1622806717,"version":"1.0.1.2"},"r-rnpn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/usa-npn/rnpn/","home":"https://usa-npn.github.io/rnpn/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rnpn/rnpn_1.4.2.tar.gz","https://cran.r-project.org/src/contrib/rnpn_1.4.2.tar.gz"],"subdirs":["noarch"],"summary":"Programmatic interface to the Web Service methods provided by the National 'Phenology' Network (<https://usanpn.org/>), which includes data on various life history events that occur at specific times.","text_prefix":false,"timestamp":1781691929,"version":"1.4.2"},"r-robcompositions":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=robCompositions","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/robCompositions/robCompositions_2.6.0.tar.gz","https://cran.r-project.org/src/contrib/robCompositions_2.6.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Methods for analysis of compositional data including robust methods  (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.","text_prefix":false,"timestamp":1550649379,"version":"2.6.0"},"r-robis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/iobis/robis","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/robis/robis_2.12.0.tar.gz","https://cran.r-project.org/src/contrib/robis_2.12.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Client for the Ocean Biogeographic Information System (<https://obis.org>).","text_prefix":false,"timestamp":0,"version":"2.12.0"},"r-robma":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://fbartos.github.io/RoBMA/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RoBMA/RoBMA_4.0.0.tar.gz","https://cran.r-project.org/src/contrib/RoBMA_4.0.0.tar.gz"],"subdirs":["linux-64"],"summary":"A framework for Bayesian meta-analysis, including model estimation, prior specification, model comparison, prediction, summaries, visualizations, and diagnostics. The package fits single and model-averaged meta-analytic, meta-regression, multilevel, publication bias adjusted, and generalized linear mixed models The model-averaged meta-analytic models combine competing models based on their predictive performance, weight inference by posterior model probabilities, and test model components using Bayes factors (e.g., effect vs. no effect; Barto\u0161 et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Barto\u0161 & Wagenmakers, 2022, <doi:10.1037/met0000405>; Barto\u0161 et al., 2025, <doi:10.1037/met0000737>). Users can specify flexible prior distributions for effect sizes, heterogeneity, publication bias (including selection models and PET-PEESE), and moderators.","text_prefix":false,"timestamp":1635943911,"version":"4.0.0"},"r-robotstxt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/robotstxt/, https://github.com/ropensci/robotstxt","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/robotstxt/robotstxt_0.7.15.tar.gz","https://cran.r-project.org/src/contrib/robotstxt_0.7.15.tar.gz"],"subdirs":["noarch"],"summary":"Provides functions to download and parse 'robots.txt' files. Ultimately the package makes it easy to check if bots (spiders, crawler, scrapers, ...) are allowed to access specific resources on a domain.","text_prefix":false,"timestamp":1758454941,"version":"0.7.15"},"r-robslopes":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=robslopes","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/robslopes/robslopes_1.1.4.tar.gz","https://cran.r-project.org/src/contrib/robslopes_1.1.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fast algorithms for the Theil-Sen estimator, Siegel's repeated median slope estimator, and Passing-Bablok regression. The implementation is based on algorithms by Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998)  <doi:10.1007/PL00009190>. The implementations are detailed in Raymaekers (2023) <doi:10.32614/RJ-2023-012> and Raymaekers J., Dufey F. (2022) <arXiv:2202.08060>. All algorithms run in quasilinear time.","text_prefix":false,"timestamp":1766526363,"version":"1.1.4"},"r-robstattm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RobStatTM","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RobStatTM/RobStatTM_1.0.11.tar.gz","https://cran.r-project.org/src/contrib/RobStatTM_1.0.11.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Companion package for the book: \"Robust Statistics: Theory and Methods, second edition\", <http://www.wiley.com/go/maronna/robust>.  This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.","text_prefix":false,"timestamp":1758010962,"version":"1.0.11"},"r-robust":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=robust","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/robust/robust_0.7-5.tar.gz","https://cran.r-project.org/src/contrib/robust_0.7-5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis.","text_prefix":false,"timestamp":1637094975,"version":"0.7_5"},"r-robustbase":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://robustbase.r-forge.r-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/robustbase/robustbase_0.99-7.tar.gz","https://cran.r-project.org/src/contrib/robustbase_0.99-7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"\"Essential\" Robust Statistics. 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It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.","text_prefix":false,"timestamp":1758203684,"version":"0.6.8"},"r-robusthd":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=robustHD","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/robustHD/robustHD_0.8.4.tar.gz","https://cran.r-project.org/src/contrib/robustHD_0.8.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression.","text_prefix":false,"timestamp":1632382856,"version":"0.8.4"},"r-robustrankaggreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RobustRankAggreg","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RobustRankAggreg/RobustRankAggreg_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/RobustRankAggreg_1.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Methods for aggregating ranked lists, especially lists of genes. It implements the Robust Rank Aggregation (Kolde et. al in preparation) and some other simple algorithms for the task. RRA method uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking.","text_prefix":false,"timestamp":1540182363,"version":"1.2.1"},"r-rocit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ROCit","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ROCit/ROCit_2.1.2.tar.gz","https://cran.r-project.org/src/contrib/ROCit_2.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.","text_prefix":false,"timestamp":1589406315,"version":"2.1.2"},"r-rockchalk":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rockchalk","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rockchalk/rockchalk_1.8.164.tar.gz","https://cran.r-project.org/src/contrib/rockchalk_1.8.164.tar.gz"],"subdirs":["noarch"],"summary":"A collection of functions for interpretation and presentation of regression analysis.  These functions are used to produce the statistics lectures in <http://pj.freefaculty.org/guides>. Includes regression diagnostics, regression tables, and plots of interactions and \"moderator\" variables. The emphasis is on \"mean-centered\" and \"residual-centered\" predictors. The vignette 'rockchalk' offers a fairly comprehensive overview.  The vignette 'Rstyle' has advice about coding in R.  The package title 'rockchalk' refers to our school motto, 'Rock Chalk Jayhawk, Go K.U.'.","text_prefix":false,"timestamp":1653049360,"version":"1.8.164"},"r-rocr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://rocr.bioinf.mpi-sb.mpg.de/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ROCR/ROCR_1.0-12.tar.gz","https://cran.r-project.org/src/contrib/ROCR_1.0-12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parameterized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parameterization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.","text_prefix":false,"timestamp":1536553396,"version":"1.0_12"},"r-rocs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ROCS","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ROCS/ROCS_1.4.tar.gz","https://cran.r-project.org/src/contrib/ROCS_1.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. 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The user can select and create different lists of colour names and different colour metrics for the conversion.","text_prefix":false,"timestamp":1758164420,"version":"0.1_2"},"r-rolocisccnbs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rolocISCCNBS","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rolocISCCNBS/rolocISCCNBS_0.1.tar.gz","https://cran.r-project.org/src/contrib/rolocISCCNBS_0.1.tar.gz"],"subdirs":["noarch"],"summary":"A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names.","text_prefix":false,"timestamp":1758705829,"version":"0.1"},"r-rook":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Rook","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rook/Rook_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/Rook_1.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An HTTP web server for R with a documented API to interface between R and the server. The documentation contains the Rook specification and details for building and running Rook applications. To get started, be sure and read the 'Rook' help file first.","text_prefix":false,"timestamp":1665481257,"version":"1.2.1"},"r-rootsolve":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rootSolve","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rootSolve/rootSolve_1.8.2.4.tar.gz","https://cran.r-project.org/src/contrib/rootSolve_1.8.2.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE).  Includes routines that: (1) generate gradient and jacobian matrices (full and banded), (2) find roots of non-linear equations by the 'Newton-Raphson' method,  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running, (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations by numerical differencing (using the method-of-lines approach). Includes fortran code.","text_prefix":false,"timestamp":1573690222,"version":"1.8.2.4"},"r-roptim":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/ypan1988/roptim/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/roptim/roptim_0.1.7.tar.gz","https://cran.r-project.org/src/contrib/roptim_0.1.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Perform general purpose optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying optim().","text_prefix":false,"timestamp":1635848622,"version":"0.1.7"},"r-rorcid":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/rorcid (devel), https://docs.ropensci.org/rorcid/ (docs)","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rorcid/rorcid_0.7.0.tar.gz","https://cran.r-project.org/src/contrib/rorcid_0.7.0.tar.gz"],"subdirs":["noarch"],"summary":"Client for the 'Orcid.org' API (<https://orcid.org/>). Functions included for searching for people, searching by 'DOI', and searching by 'Orcid' 'ID'.","text_prefix":false,"timestamp":1758592017,"version":"0.7.0"},"r-rose":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=ROSE","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ROSE/ROSE_0.0-4.tar.gz","https://cran.r-project.org/src/contrib/ROSE_0.0-4.tar.gz"],"subdirs":["noarch"],"summary":"The package provides functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods.","text_prefix":false,"timestamp":1621883611,"version":"0.0_4"},"r-rosm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/paleolimbot/rosm","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/rosm/rosm_0.3.2.tar.gz","https://cloud.r-project.org/src/contrib/rosm_0.3.2.tar.gz"],"subdirs":["noarch"],"summary":"Download and plot Open Street Map <http://www.openstreetmap.org/>, Bing Maps <http://www.bing.com/maps> and other tiled map sources. 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The 'Open Tree of Life' aims at assembling a comprehensive phylogenetic tree for all named species.","text_prefix":false,"timestamp":0,"version":"3.1.1"},"r-roughsets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/janusza/RoughSets","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RoughSets/RoughSets_1.3-8.tar.gz","https://cran.r-project.org/src/contrib/RoughSets_1.3-8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzis\u0142aw Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.","text_prefix":false,"timestamp":1758677708,"version":"1.3_8"},"r-roxygen2":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-lib/roxygen2","doc_url":"https://roxygen2.r-lib.org/reference/index.html","home":"https://roxygen2.r-lib.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/roxygen2/roxygen2_8.1.0.tar.gz","https://cran.r-project.org/src/contrib/roxygen2_8.1.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Generate your Rd documentation, 'NAMESPACE' file, and collation  field using specially formatted comments. Writing documentation in-line with code makes it easier to keep your documentation up-to-date as your requirements change. 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'Selenium 2.0 WebDriver' allows driving a web browser natively as a user would either locally or on a remote machine using the Selenium server it marks a leap forward in terms of web browser automation. Selenium automates web browsers (commonly referred to as browsers). Using RSelenium you can automate browsers locally or remotely.","text_prefix":false,"timestamp":1758933478,"version":"1.7.9"},"r-rserve":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/s-u/Rserve","home":"https://www.rforge.net/Rserve/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://www.rforge.net/Rserve/snapshot/Rserve_1.8-14.tar.gz","https://www.rforge.net/src/contrib/Rserve_1.8-14.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Rserve acts as a socket server (TCP/IP or local sockets)  which allows binary requests to be sent to R. Every connection has a separate workspace and working directory. Client-side implementations are available for popular languages such as C/C++ and Java, allowing any application to use facilities of R without the need of linking to R code. Rserve supports remote connection, user authentication and file transfer. A simple R client is included in this package as well.","text_prefix":true,"timestamp":1597828668,"version":"1.8_14"},"r-rskc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RSKC","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RSKC/RSKC_2.4.2.tar.gz","https://cran.r-project.org/src/contrib/RSKC_2.4.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.","text_prefix":false,"timestamp":1773525302,"version":"2.4.2"},"r-rslurm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SESYNC-ci/rslurm","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rslurm/rslurm_0.6.2.tar.gz","https://cran.r-project.org/src/contrib/rslurm_0.6.2.tar.gz"],"subdirs":["noarch"],"summary":"Functions that simplify submitting R scripts to a 'Slurm' workload manager, in part by automating the division of embarrassingly parallel calculations across cluster nodes.","text_prefix":false,"timestamp":1621906577,"version":"0.6.2"},"r-rsm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rsm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rsm/rsm_2.10.6.tar.gz","https://cran.r-project.org/src/contrib/rsm_2.10.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides functions to generate response-surface designs,  fit first- and second-order response-surface models,  make surface plots, obtain the path of steepest ascent,  and do canonical analysis. A good reference on these methods  is Chapter 10 of Wu, C-F J and Hamada, M (2009)  \"Experiments: Planning, Analysis, and Parameter Design Optimization\" ISBN 978-0-471-69946-0.","text_prefix":false,"timestamp":1539043472,"version":"2.10.6"},"r-rsnns":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/cbergmeir/RSNNS","license":"LGPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RSNNS/RSNNS_0.4-18.tar.gz","https://cran.r-project.org/src/contrib/RSNNS_0.4-18.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.","text_prefix":false,"timestamp":1628494728,"version":"0.4_18"},"r-rsnps":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/rsnps","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rsnps/rsnps_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/rsnps_0.6.0.tar.gz"],"subdirs":["noarch"],"summary":"A programmatic interface to various 'SNP' 'datasets' on the web: 'OpenSNP' (<https://opensnp.org>), and 'NBCIs' 'dbSNP' database (<https://www.ncbi.nlm.nih.gov/projects/SNP>). Functions are included for searching for 'NCBI'. For 'OpenSNP', functions are included for getting 'SNPs', and data for 'genotypes', 'phenotypes', annotations, and bulk downloads of data by user.","text_prefix":false,"timestamp":1589880226,"version":"0.6.0"},"r-rsolnp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Rsolnp","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rsolnp/Rsolnp_2.0.1.tar.gz","https://cran.r-project.org/src/contrib/Rsolnp_2.0.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"General Non-linear Optimization Using Augmented Lagrange Multiplier Method.","text_prefix":false,"timestamp":1539788977,"version":"2.0.1"},"r-rsparse":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/dselivanov/rsparse","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rsparse/rsparse_0.5.3.tar.gz","https://cran.r-project.org/src/contrib/rsparse_0.5.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>) 2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, <arXiv:1410.2596>) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, <https://www.aclweb.org/anthology/D14-1162>) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems.","text_prefix":false,"timestamp":1652639044,"version":"0.5.3"},"r-rspectra":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/yixuan/RSpectra","license":"MPL-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RSpectra/RSpectra_0.16-2.tar.gz","https://cran.r-project.org/src/contrib/RSpectra_0.16-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"R interface to the 'Spectra' library <https://spectralib.org/> for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user.","text_prefix":false,"timestamp":1554538836,"version":"0.16_2"},"r-rsq":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rsq","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rsq/rsq_2.7.tar.gz","https://cran.r-project.org/src/contrib/rsq_2.7.tar.gz"],"subdirs":["noarch"],"summary":"Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear models (including quasi models with well defined variance functions).","text_prefix":false,"timestamp":1606721203,"version":"2.7"},"r-rsqlite":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-dbi/RSQLite","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RSQLite/RSQLite_3.53.3.tar.gz","https://cran.r-project.org/src/contrib/RSQLite_3.53.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Embeds the 'SQLite' database engine in R and provides an interface compliant with the 'DBI' package. The source for the 'SQLite' engine is included.","text_prefix":false,"timestamp":1576698249,"version":"3.53.3"},"r-rstac":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/brazil-data-cube/rstac","home":"https://brazil-data-cube.github.io/rstac/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rstac/rstac_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/rstac_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides functions to access, search and download spacetime earth observation data via SpatioTemporal Asset Catalog (STAC). This package supports the version 0.8.1 or higher of the STAC specification (<http://stacspec.org>).","text_prefix":false,"timestamp":1665479289,"version":"1.0.1"},"r-rstackdeque":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/oneilsh/rstackdeque","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rstackdeque/rstackdeque_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/rstackdeque_1.1.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides fast, persistent (side-effect-free) stack, queue and deque (double-ended-queue) data structures. While deques include a superset of functionality provided by queues, in these implementations queues are more efficient in some specialized situations. See the documentation for rstack, rdeque, and rpqueue for details.","text_prefix":false,"timestamp":1758709336,"version":"1.1.1"},"r-rstan":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://discourse.mc-stan.org, http://mc-stan.org","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rstan/rstan_2.32.7.tar.gz","https://cran.r-project.org/src/contrib/rstan_2.32.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.","text_prefix":false,"timestamp":0,"version":"2.32.7"},"r-rstanarm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/stan-dev/rstanarm","doc_url":"http://mc-stan.org/rstanarm/","home":"http://discourse.mc-stan.org, http://mc-stan.org/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rstanarm/rstanarm_2.32.2.tar.gz","https://cran.r-project.org/src/contrib/rstanarm_2.32.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.","text_prefix":false,"timestamp":1766595669,"version":"2.32.2"},"r-rstantools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://mc-stan.org/rstantools/, https://discourse.mc-stan.org/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/rstantools/rstantools_2.7.1.tar.gz","https://cloud.r-project.org/src/contrib/rstantools_2.7.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Provides various tools for developers of R packages interfacing with 'Stan' <https://mc-stan.org>, including functions to set up the required package structure, S3 generics and default methods to unify function naming across 'Stan'-based R packages, and vignettes with recommendations for developers.","text_prefix":false,"timestamp":1591296003,"version":"2.7.1"},"r-rstatix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://rpkgs.datanovia.com/rstatix/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rstatix/rstatix_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/rstatix_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix. Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. It's also possible to compute several effect size metrics, including \"eta squared\" for ANOVA, \"Cohen's d\" for t-test and 'Cramer V' for the association between categorical variables. The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.","text_prefix":false,"timestamp":1667996452,"version":"1.1.0"},"r-rstoolbox":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/bleutner/RStoolbox","home":"https://bleutner.github.io/RStoolbox/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RStoolbox/RStoolbox_1.0.2.3.tar.gz","https://cran.r-project.org/src/contrib/RStoolbox_1.0.2.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Toolbox for remote sensing image processing and analysis such as calculating spectral indices, principal component transformation, unsupervised and supervised classification or fractional cover analyses.","text_prefix":false,"timestamp":1688022196,"version":"1.0.2.3"},"r-rstpm2":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://github.com/mclements/rstpm2","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rstpm2/rstpm2_1.7.1.tar.gz","https://cran.r-project.org/src/contrib/rstpm2_1.7.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.","text_prefix":false,"timestamp":1650377989,"version":"1.7.1"},"r-rstudioapi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/rstudioapi","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rstudioapi/rstudioapi_0.19.0.tar.gz","https://cran.r-project.org/src/contrib/rstudioapi_0.19.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Access the RStudio API (if available) and provide informative error messages when it's not.","text_prefix":false,"timestamp":1535921742,"version":"0.19.0"},"r-rsvd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/erichson/rSVD","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/rsvd/rsvd_1.0.6.tar.gz","https://cloud.r-project.org/src/contrib/rsvd_1.0.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate  deterministic matrix algorithms are used for this task. However, the emergence of  large-scale data has severely challenged our computational ability to analyze big data.  The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD).  The rsvd package provides several randomized matrix algorithms such as the randomized  singular value decomposition (rsvd), randomized principal component analysis (rpca),  randomized robust principal component analysis (rrpca), randomized interpolative  decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot  functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.","text_prefix":false,"timestamp":0,"version":"1.0.6"},"r-rsvg":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"doc_url":"https://docs.ropensci.org/rsvg/","home":"https://github.com/jeroen/rsvg","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rsvg/rsvg_2.6.1.tar.gz","https://cran.r-project.org/src/contrib/rsvg_2.6.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Renders vector-based svg images into high-quality custom-size bitmap arrays using 'librsvg2'. The resulting bitmap can be written to e.g. png, jpeg or webp format. In addition, the package can convert images directly to various formats such as pdf or postscript.","text_prefix":false,"timestamp":1758658935,"version":"2.6.1"},"r-rsvgtipsdevice":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RSVGTipsDevice","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://cran.r-project.org/src/contrib/Archive/RSVGTipsDevice/RSVGTipsDevice_1.0-7.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"A graphics device for R that uses the w3.org xml standard for Scalable Vector Graphics.  This version supports tooltips with 1 to 3 lines, hyperlinks, and line styles.","text_prefix":false,"timestamp":1720477031,"version":"1.0_7"},"r-rsyncrosim":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/syncrosim/rsyncrosim","home":"https://syncrosim.github.io/rsyncrosim/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://github.com/syncrosim/rsyncrosim/archive/v2.1.13.tar.gz","https://github.com/syncrosim/rsyncrosim/releases/download/2.1.13/rsyncrosim_2.1.13.tar.gz"],"subdirs":["noarch"],"summary":"'SyncroSim' is a generalized framework for managing scenario-based datasets (<https://syncrosim.com/>). 'rsyncrosim' provides an interface to 'SyncroSim'. Simulation models can be added to 'SyncroSim' to transform these datasets, taking advantage of general features such as defining scenarios of model inputs, running Monte Carlo simulations, and summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 3.0.00 or higher (API documentation: <https://docs.syncrosim.com/>).","text_prefix":false,"timestamp":1636049418,"version":"2.1.13"},"r-rtables":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/insightsengineering/rtables, https://insightsengineering.github.io/rtables/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/rtables/rtables_0.6.17.tar.gz","https://cloud.r-project.org/src/contrib/rtables_0.6.17.tar.gz"],"subdirs":["noarch"],"summary":"Reporting tables often have structure that goes beyond simple rectangular data. The 'rtables' package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.","text_prefix":false,"timestamp":1721298945,"version":"0.6.17"},"r-rtables.officer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/insightsengineering/rtables.officer, https://insightsengineering.github.io/rtables.officer/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rtables.officer/rtables.officer_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/rtables.officer_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Designed to create and display complex tables with R, the 'rtables' R package allows cells in an 'rtables' object to contain any high-dimensional data structure, which can then be displayed with cell-specific formatting instructions. Additionally, the 'rtables.officer' package supports export formats related to the Microsoft Office software suite, including Microsoft Word ('docx') and Microsoft PowerPoint ('pptx').","text_prefix":false,"timestamp":1767896930,"version":"0.1.2"},"r-rtensor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rikenbit/rTensor","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rTensor/rTensor_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/rTensor_1.5.0.tar.gz"],"subdirs":["noarch"],"summary":"A set of tools for creation, manipulation, and modeling of tensors with arbitrary number of modes. 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Because the resulting object can be very large for even modestly sized TIFF images, images can be reduced as they are read for improved performance.  This package is a wrapper around libtiff (www.libtiff.org), on which it depends (i.e. the libtiff shared library must be on your PATH for the binary to work, and tiffio.h must be on your system to build the package from source). By using libtiff's highlevel TIFFReadRGBAImage function, this package inherently supports a wide range of image formats and compression schemes. This package also provides an implementation of the Ridler Autothresholding algorithm for easy generation of binary masks as described in Ridler & Calvard (1978) <doi:10.1109/TSMC.1978.4310039>.","text_prefix":false,"timestamp":1574258226,"version":"1.4.8"},"r-rtriangle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/davidcsterratt/RTriangle","home":"https://github.com/davidcsterratt/RTriangle, http://www.cs.cmu.edu/~quake/triangle.html","license":"CC-BY-NC-SA-4.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RTriangle/RTriangle_1.6-0.15.tar.gz","https://cran.r-project.org/src/contrib/RTriangle_1.6-0.15.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This is a port of Jonathan Shewchuk's Triangle library to R. From his description: \"Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis.\"","text_prefix":false,"timestamp":1668587211,"version":"1.6_0.15"},"r-rtsne":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jkrijthe/Rtsne","license":"LicenseRef-FOSS AND LicenseRef-BSDLike","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rtsne/Rtsne_0.17.tar.gz","https://cran.r-project.org/src/contrib/Rtsne_0.17.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An R wrapper around the fast T-distributed Stochastic Neighbor Embedding implementation by Van der Maaten  (see <https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).","text_prefix":false,"timestamp":1542618643,"version":"0.17"},"r-rttf2pt1":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/wch/Rttf2pt1","license":"LicenseRef-BSDLike","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rttf2pt1/Rttf2pt1_1.3.14.tar.gz","https://cran.r-project.org/src/contrib/Rttf2pt1_1.3.14.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Contains the program 'ttf2pt1', for use with the 'extrafont' package. This product includes software developed by the 'TTF2PT1' Project and its contributors.","text_prefix":false,"timestamp":1758944309,"version":"1.3.14"},"r-rtweet":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ropensci/rtweet/","doc_url":"https://docs.ropensci.org/rtweet/reference/index.html","home":"https://docs.ropensci.org/rtweet/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rtweet/rtweet_2.0.0.tar.gz","https://cran.r-project.org/src/contrib/rtweet_2.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An implementation of calls designed to collect and organize Twitter data via Twitter's REST and stream Application Program Interfaces (API), which can be found at the following URL: <https://developer.twitter.com/en/docs>.","text_prefix":false,"timestamp":1563523517,"version":"2.0.0"},"r-runit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RUnit","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RUnit/RUnit_0.4.33.1.tar.gz","https://cran.r-project.org/src/contrib/RUnit_0.4.33.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"R functions implementing a standard Unit Testing framework, with additional code inspection and report generation tools.","text_prefix":false,"timestamp":0,"version":"0.4.33.1"},"r-runjags":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ku-awdc/runjags","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/runjags/runjags_2.2.2-5.tar.gz","https://cran.r-project.org/src/contrib/runjags_2.2.2-5.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"User-friendly interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax.  A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.","text_prefix":false,"timestamp":1626119833,"version":"2.2.2_5"},"r-runner":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=runner","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/runner/runner_0.4.6.tar.gz","https://cran.r-project.org/src/contrib/runner_0.4.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Lightweight library for rolling windows operations. Package enables full control over the window length, window lag and a time indices. With a runner one can apply any R function on a rolling windows. The package eases work with equally and unequally spaced time series.","text_prefix":false,"timestamp":1665687445,"version":"0.4.6"},"r-runonce":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/privefl/runonce","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/runonce/runonce_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/runonce_0.3.3.tar.gz"],"subdirs":["noarch"],"summary":"Package 'runonce' helps automating the saving of long-running code to help running the same code multiple times. If you run some long-running code once, it saves the result in a file on disk. Then, if the result already exists, i.e. if the code has already been run and its output has already been saved, it just reads the result from the stored file instead of running the code again.","text_prefix":false,"timestamp":1759849229,"version":"0.3.3"},"r-ruv":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www-personal.umich.edu/~johanngb/ruv/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/ruv/ruv_0.9.7.2.tar.gz","https://cloud.r-project.org/src/contrib/ruv_0.9.7.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Implements the 'RUV' (Remove Unwanted Variation) algorithms.  These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>.  The algorithms require the user to specify a set of negative control variables, as described in the references.  The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.","text_prefix":false,"timestamp":1563602474,"version":"0.9.7.2"},"r-rvaidememoire":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RVAideMemoire","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RVAideMemoire/RVAideMemoire_0.9-83-12.tar.gz","https://cran.r-project.org/src/contrib/RVAideMemoire_0.9-83-12.tar.gz"],"subdirs":["linux-64","noarch","win-64"],"summary":"Contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as many methods from different packages as possible.","text_prefix":false,"timestamp":1534864938,"version":"0.9_83_12"},"r-rvcg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/zarquon42b/Rvcg","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rvcg/Rvcg_0.24.tar.gz","https://cran.r-project.org/src/contrib/Rvcg_0.24.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Operations on triangular meshes based on 'VCGLIB'. This package integrates nicely with the R-package 'rgl' to render the meshes processed by 'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is an open source portable C++ templated library for manipulation, processing and displaying with OpenGL of triangle and tetrahedral meshes. The library, composed by more than 100k lines of code, is released under the GPL license, and it is the base of most of the software tools of the Visual Computing Lab of the Italian National Research Council Institute ISTI <http://vcg.isti.cnr.it>, like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk <https://github.com/cnr-isti-vclab/vcglib> and patched to work with options determined by the configure script as well as to work with the header files included by 'RcppEigen'.","text_prefix":false,"timestamp":1758646912,"version":"0.24"},"r-rvcheck":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rvcheck","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rvcheck/rvcheck_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/rvcheck_0.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Check latest release version of R and R package (both in 'CRAN', 'Bioconductor' or 'Github').","text_prefix":false,"timestamp":1539352550,"version":"0.2.1"},"r-rvenn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RVenn","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RVenn/RVenn_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/RVenn_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Set operations for many sets. The base functions for set operations in R can be used for only two sets. This package uses 'purr' to find the union, intersection and difference of three or more sets. This package also provides functions for pairwise set operations among several sets. Further, based on 'ggplot2' and 'ggforce', a Venn diagram can be drawn for two or three sets. For bigger data sets, a clustered heatmap showing presence/absence of the elements of the sets can be drawn based on the 'pheatmap' package. Finally, enrichment test can be applied to two sets whether an overlap is statistically significant or not.","text_prefix":false,"timestamp":1758592547,"version":"1.1.0"},"r-rversions":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-hub/rversions, https://r-hub.github.io/rversions","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rversions/rversions_3.0.0.tar.gz","https://cran.r-project.org/src/contrib/rversions_3.0.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Query the main 'R' 'SVN' repository to find the versions 'r-release' and 'r-oldrel' refer to, and also all previous 'R' versions and their release dates.","text_prefix":false,"timestamp":1540110206,"version":"3.0.0"},"r-rvertnet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/rvertnet","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rvertnet/rvertnet_0.8.4.tar.gz","https://cran.r-project.org/src/contrib/rvertnet_0.8.4.tar.gz"],"subdirs":["noarch"],"summary":"Retrieve, map and summarize data from the 'VertNet.org'  archives (<http://vertnet.org/>).  Functions allow searching by many  parameters, including 'taxonomic' names, places, and dates. In addition,  there is an interface for conducting spatially delimited searches, and  another for requesting large 'datasets' via email.","text_prefix":false,"timestamp":1571818649,"version":"0.8.4"},"r-rvest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://rvest.tidyverse.org/, https://github.com/tidyverse/rvest","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rvest/rvest_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/rvest_1.0.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Wrappers around the 'xml2' and 'httr' packages to make it easy to download, then manipulate, HTML and XML.","text_prefix":false,"timestamp":1537134916,"version":"1.0.5"},"r-rvg":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/davidgohel/rvg","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rvg/rvg_0.4.2.tar.gz","https://cran.r-project.org/src/contrib/rvg_0.4.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Vector Graphics devices for Microsoft PowerPoint and Excel. Functions extending package 'officer' are provided to  embed 'DrawingML' graphics into 'Microsoft PowerPoint' presentations and  'Microsoft Excel' workbooks.","text_prefix":false,"timestamp":1542615125,"version":"0.4.2"},"r-rvinecopulib":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rvinecopulib","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/rvinecopulib/rvinecopulib_1.0.0.1.0.tar.gz","https://cloud.r-project.org/src/contrib/rvinecopulib_1.0.0.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides an interface to 'vinecopulib', a C++ library for vine copula modeling. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.6.1). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.","text_prefix":false,"timestamp":1666008905,"version":"1.0.0.1.0"},"r-rvmmin":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=Rvmmin","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rvmmin/Rvmmin_2018-4.17.1.tar.gz","https://cran.r-project.org/src/contrib/Rvmmin_2018-4.17.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Variable metric nonlinear function minimization with bounds constraints.","text_prefix":false,"timestamp":1537083555,"version":"2018_4.17.1"},"r-rwave":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.orfe.princeton.edu/~rcarmona/TFbook/tfbook.html, http://r-forge.r-project.org/projects/rwave/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Rwave/Rwave_2.6-5.tar.gz","https://cran.r-project.org/src/contrib/Rwave_2.6-5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A set of R functions which provide an environment for the Time-Frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals). It was originally written for Splus by Rene Carmona, Bruno Torresani, and Wen L. Hwang, first at the University of California at Irvine and then at Princeton University.  Credit should also be given to Andrea Wang whose functions on the dyadic wavelet transform are included. Rwave is based on the book: \"Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S\", by Rene Carmona, Wen L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press.","text_prefix":false,"timestamp":1666218768,"version":"2.6_5"},"r-rweka":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RWeka","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RWeka/RWeka_0.4-50.tar.gz","https://cran.r-project.org/src/contrib/RWeka_0.4-50.tar.gz"],"subdirs":["noarch"],"summary":"An R interface to Weka (Version 3.9.3). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization.  Package 'RWeka' contains the interface code, the Weka jar is in a separate package 'RWekajars'.  For more information on Weka see <http://www.cs.waikato.ac.nz/ml/weka/>.","text_prefix":false,"timestamp":1621870175,"version":"0.4_50"},"r-rwekajars":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=RWekajars","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RWekajars/RWekajars_3.9.3-2.tar.gz","https://cran.r-project.org/src/contrib/RWekajars_3.9.3-2.tar.gz"],"subdirs":["noarch"],"summary":"External jars required for package 'RWeka'.","text_prefix":false,"timestamp":1563479156,"version":"3.9.3_2"},"r-rworkflows":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/neurogenomics/rworkflows","home":"https://neurogenomics.github.io/rworkflows/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rworkflows/rworkflows_1.0.12.tar.gz","https://cran.r-project.org/src/contrib/rworkflows_1.0.12.tar.gz"],"subdirs":["noarch"],"summary":"Reproducibility is essential to the progress of research, yet achieving it remains elusive even in computational fields. Continuous Integration (CI) platforms offer a powerful way to launch automated workflows to check and document code, but often require considerable time, effort, and technical expertise to setup. We therefore developed the rworkflows suite to make robust CI workflows easy and freely accessible to all R package developers. rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 2) an R package to quickly implement a standardised workflow, and 3) a centrally maintained GitHub Action.","text_prefix":false,"timestamp":1768035374,"version":"1.0.12"},"r-rworldmap":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://groups.google.com/forum/#!forum/rworldmap","home":"https://github.com/AndySouth/rworldmap/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rworldmap/rworldmap_1.3-8.tar.gz","https://cran.r-project.org/src/contrib/rworldmap_1.3-8.tar.gz"],"subdirs":["noarch"],"summary":"Enables mapping of country level and gridded user datasets.","text_prefix":false,"timestamp":1758291358,"version":"1.3_8"},"r-rworldxtra":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=rworldxtra","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rworldxtra/rworldxtra_1.01.tar.gz","https://cran.r-project.org/src/contrib/rworldxtra_1.01.tar.gz"],"subdirs":["noarch"],"summary":"High resolution vector country boundaries derived from Natural Earth data, can be plotted in rworldmap.","text_prefix":false,"timestamp":1758509391,"version":"1.01"},"r-rxode":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://nlmixrdevelopment.github.io/RxODE/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/RxODE/RxODE_1.1.5.tar.gz","https://cran.r-project.org/src/contrib/RxODE_1.1.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Facilities for running simulations from ordinary differential equation (ODE) models, such as pharmacometrics and other compartmental models.  A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency.  An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules.  NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the \"R Administration and Installation\" manual. Also the code is mostly released under GPL.  The VODE and LSODA are in the public domain.  The information is available in the inst/COPYRIGHTS. You can also obtain the archived SnakeCharmR for python integration from CRAN archives <https://cran.r-project.org/src/contrib/Archive/SnakeCharmR/> or <https://github.com/nlmixrdevelopment/SnakeCharmR>.","text_prefix":false,"timestamp":1579851335,"version":"1.1.5"},"r-rxode2":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/nlmixr2/rxode2/","home":"https://nlmixr2.github.io/rxode2/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rxode2/rxode2_5.0.2.tar.gz","https://cran.r-project.org/src/contrib/rxode2_5.0.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models.  A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency.  An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules.  NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the \"R Administration and Installation\" manual. Also the code is mostly released under GPL.  The 'VODE' and 'LSODA' are in the public domain.  The information is available in the inst/COPYRIGHTS.","text_prefix":false,"timestamp":1785006954,"version":"5.0.2"},"r-rxode2et":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/nlmixr2/rxode2et/","home":"https://nlmixr2.github.io/rxode2et/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rxode2et/rxode2et_2.0.13.tar.gz","https://cran.r-project.org/src/contrib/rxode2et_2.0.13.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides the event table and support functions needed for 'rxode2' (Wang, Hallow and  James (2016) <doi:10.1002/psp4.12052>) and 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This split will reduce computational burden of recompiling 'rxode2'.","text_prefix":false,"timestamp":1706638396,"version":"2.0.13"},"r-rxode2ll":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/nlmixr2/rxode2ll/","home":"https://nlmixr2.github.io/rxode2ll/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/rxode2ll/rxode2ll_2.0.18.tar.gz","https://cloud.r-project.org/src/contrib/rxode2ll_2.0.18.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides the log-likelihoods with gradients from 'stan' (Carpenter et al (2015), <arXiv:1509.07164>) needed for generalized log-likelihood estimation in 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is split of to reduce computational burden of recompiling 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs the 'nlmixr2' models during estimation.","text_prefix":false,"timestamp":1789484357,"version":"2.0.18"},"r-rxode2parse":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/nlmixr2/rxode2parse/","home":"https://nlmixr2.github.io/rxode2parse/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rxode2parse/rxode2parse_2.0.19.tar.gz","https://cran.r-project.org/src/contrib/rxode2parse_2.0.19.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides the parsing needed for 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>). It also provides the 'stan' based advan linear compartment model solutions with gradients (Carpenter et al (2015), <arXiv:1509.07164>) needed in 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This split will reduce computational burden of recompiling 'rxode2'.","text_prefix":false,"timestamp":1706588554,"version":"2.0.19"},"r-rxode2random":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/nlmixr2/rxode2random/","home":"https://nlmixr2.github.io/rxode2random/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rxode2random/rxode2random_2.1.1.tar.gz","https://cran.r-project.org/src/contrib/rxode2random_2.1.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides the random number generation (in parallel) needed for 'rxode2' (Wang, Hallow and  James (2016) <doi:10.1002/psp4.12052>) and 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This split will reduce computational burden of recompiling 'rxode2'.","text_prefix":false,"timestamp":1706609537,"version":"2.1.1"},"r-ryacas":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-cas/ryacas, http://www.yacas.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Ryacas/Ryacas_1.1.6.tar.gz","https://cran.r-project.org/src/contrib/Ryacas_1.1.6.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).","text_prefix":false,"timestamp":1665419907,"version":"1.1.6"},"r-rzmq":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"http://github.com/ropensci/rzmq","home":"http://www.zeromq.org","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/rzmq/rzmq_0.9.16.tar.gz","https://cran.r-project.org/src/contrib/rzmq_0.9.16.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Interface to the 'ZeroMQ' lightweight messaging kernel (see <http://www.zeromq.org/> for more information).","text_prefix":false,"timestamp":0,"version":"0.9.16"},"r-s2":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-spatial/s2","doc_url":"https://s2geometry.io/","home":"https://r-spatial.github.io/s2/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/s2/s2_1.1.11.tar.gz","https://cran.r-project.org/src/contrib/s2_1.1.11.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides R bindings for Google's s2 library for geometric calculations on the sphere. High-performance constructors and exporters provide high compatibility with existing spatial packages, transformers construct new geometries from existing geometries, predicates provide a means to select geometries based on spatial relationships, and accessors extract information about geometries.","text_prefix":false,"timestamp":1623942406,"version":"1.1.11"},"r-s2dv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://earth.bsc.es/gitlab/es/s2dv/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/s2dv/s2dv_2.3.0.tar.gz","https://cran.r-project.org/src/contrib/s2dv_2.3.0.tar.gz"],"subdirs":["noarch"],"summary":"An advanced version of package 's2dverification'. Intended for seasonal to decadal (s2d) climate forecast verification, but also applicable to other types of forecasts or general climate analysis. This package is specifically designed for comparing experimental and observational datasets. It provides functionality for data retrieval, post-processing, skill score computation against observations, and visualization. Compared to 's2dverification', 's2dv' is more compatible with the package 'startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The Climate Data Operators (CDO) version used in development is 1.9.8. Implements methods described in Wilks (2011) <doi:10.1016/B978-0-12-385022-5.00008-7>, DelSole and Tippett (2016) <doi:10.1175/MWR-D-15-0218.1>, Kharin et al. (2012) <doi:10.1029/2012GL052647>, Doblas-Reyes et al. (2003) <doi:10.1007/s00382-003-0350-4>.","text_prefix":false,"timestamp":1665546984,"version":"2.3.0"},"r-s2dverification":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://earth.bsc.es/gitlab/es/s2dverification/wikis/home","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/s2dverification/s2dverification_2.10.3.tar.gz","https://cran.r-project.org/src/contrib/s2dverification_2.10.3.tar.gz"],"subdirs":["noarch"],"summary":"Set of tools to verify forecasts through the computation of typical prediction scores against one or more observational datasets or reanalyses (a reanalysis being a physical extrapolation of observations that relies on the equations from a model, not a pure observational dataset). Intended for seasonal to decadal climate forecasts although can be useful to verify other kinds of forecasts. The package can be helpful in climate sciences for other purposes than forecasting.","text_prefix":false,"timestamp":1597618331,"version":"2.10.3"},"r-s4vd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=s4vd","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/s4vd/s4vd_1.1-1.tar.gz","https://cran.r-project.org/src/contrib/s4vd_1.1-1.tar.gz"],"subdirs":["noarch"],"summary":"The main function s4vd() performs a biclustering via sparse singular value decomposition with a nested stability selection. The results is an biclust object and thus all methods of the biclust package can be applied.","text_prefix":false,"timestamp":1758266195,"version":"1.1_1"},"r-s7":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rconsortium/S7/, https://rconsortium.github.io/S7/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/S7/S7_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/S7_0.2.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A new object oriented programming system designed to be a successor to S3 and S4. It includes formal class, generic, and method specification, and a limited form of multiple dispatch. It has been designed and implemented collaboratively by the R Consortium Object-Oriented Programming Working Group, which includes representatives from R-Core, 'Bioconductor', 'Posit'/'tidyverse', and the wider R community.","text_prefix":false,"timestamp":1776861780,"version":"0.2.2"},"r-sads":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/piklprado/sads","home":"http://piLaboratory.github.io/sads","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sads/sads_0.6.5.tar.gz","https://cran.r-project.org/src/contrib/sads_0.6.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.","text_prefix":false,"timestamp":1758787106,"version":"0.6.5"},"r-safetensors":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/mlverse/safetensors","home":"https://mlverse.github.io/safetensors/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/safetensors/safetensors_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/safetensors_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"A file format for storing tensors that is secure (doesn't allow for code execution), fast and simple to implement. 'safetensors' also enables cross language and cross frameworks compatibility making it an ideal format for storing machine learning model weights.","text_prefix":false,"timestamp":1777300298,"version":"0.2.1"},"r-salso":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=salso","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/salso/salso_0.3.78.tar.gz","https://cran.r-project.org/src/contrib/salso_0.3.78.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, M\u00fcller (2022) <doi:10.1080/10618600.2022.2069779>.","text_prefix":false,"timestamp":1776332580,"version":"0.3.78"},"r-sampling":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sampling","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sampling/sampling_2.11.tar.gz","https://cran.r-project.org/src/contrib/sampling_2.11.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions for drawing and calibrating samples.","text_prefix":false,"timestamp":1588659511,"version":"2.11"},"r-sampsurf":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://sampsurf.r-forge.r-project.org/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sampSurf/sampSurf_0.7-6.tar.gz","https://cran.r-project.org/src/contrib/sampSurf_0.7-6.tar.gz"],"subdirs":["noarch"],"summary":"Sampling surface simulation is useful in the comparison of different areal sampling methods in forestry, ecology and natural resources. The sampSurf package allows the simulation  of numerous sampling methods for standing trees and downed woody debris in a spatial context. It also provides an S4 class and method structure that facilitates the addition of new sampling methods.","text_prefix":false,"timestamp":1572812841,"version":"0.7_6"},"r-sandpaper":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The {sandpaper} package was created by [The Carpentries](https://carpentries.org) to re-imagine our method of creating lesson websites for our workshops. This package will take a series of [Markdown](https://www.markdownguide.org/getting-started/) or [RMarkdown](https://rmarkdown.rstudio.com/) files and generate a static website with the features and styling of The Carpentries lessons including customized layouts and callout blocks. Much of the functionality in this package is inspired by [Jenny Bryan's](https://jennybryan.org/) work with the [{usethis}](https://usethis.r-lib.org/) package.","dev_url":"https://github.com/carpentries/sandpaper/","doc_url":"https://carpentries.github.io/sandpaper/","home":"https://carpentries.github.io/sandpaper/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/carpentries/sandpaper/archive/refs/tags/0.20.2.tar.gz","subdirs":["noarch"],"summary":"Create and Curate Carpentries Lessons","text_prefix":false,"timestamp":1790784910,"version":"0.21.1"},"r-sandwich":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://sandwich.R-Forge.R-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/sandwich/sandwich_3.1-3.tar.gz","https://cloud.r-project.org/src/contrib/sandwich_3.1-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC) covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC) covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators); clustered covariances (one-way and multi-way); panel and panel-corrected covariances; outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>, Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.","text_prefix":false,"timestamp":0,"version":"3.1_3"},"r-santoku":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hughjonesd/santoku, https://hughjonesd.github.io/santoku/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/santoku/santoku_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/santoku_1.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A tool for cutting data into intervals. Allows singleton intervals. Always includes the whole range of data by default. Flexible labelling. Convenience functions for cutting by quantiles etc. Handles dates, times, units and other vectors.","text_prefix":false,"timestamp":1654678529,"version":"1.2.0"},"r-sass":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/rstudio/sass","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sass/sass_0.4.10.tar.gz","https://cran.r-project.org/src/contrib/sass_0.4.10.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An 'SCSS' compiler, powered by the 'LibSass' library. With this, R developers can use variables, inheritance, and functions to generate dynamic style sheets. The package uses the 'Sass CSS' extension language, which is stable, powerful, and CSS compatible.","text_prefix":false,"timestamp":1648112479,"version":"0.4.10"},"r-satellite":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=satellite","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/satellite/satellite_1.0.6.tar.gz","https://cran.r-project.org/src/contrib/satellite_1.0.6.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Herein, we provide a broad variety of functions which are useful for handling, manipulating, and visualizing satellite-based remote sensing  data. These operations range from mere data import and layer handling (eg  subsetting), over Raster* typical data wrangling (eg crop, extend), to more  sophisticated (pre-)processing tasks typically applied to satellite imagery  (eg atmospheric and topographic correction). This functionality is  complemented by a full access to the satellite layers' metadata at any  stage and the documentation of performed actions in a separate log file.  Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8  (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat  Global Land Survey data set. Moreover, support for Terra and Aqua-MODIS as  well as PROBA-V is expected to arrive shortly.","text_prefix":false,"timestamp":1633539183,"version":"1.0.6"},"r-saver":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mohuangx/SAVER","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SAVER/SAVER_1.1.2.tar.gz","https://cran.r-project.org/src/contrib/SAVER_1.1.2.tar.gz"],"subdirs":["noarch"],"summary":"An implementation of a regularized regression prediction and  empirical Bayes method to recover the true gene expression profile in  noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018)  <doi:10.1038/s41592-018-0033-z> for more details.","text_prefix":false,"timestamp":1563601789,"version":"1.1.2"},"r-sbl":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sbl","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sbl/sbl_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/sbl_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Implements sparse Bayesian learning method for QTL mapping and genome-wide association studies.","text_prefix":false,"timestamp":1758089481,"version":"0.1.0"},"r-sbtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/DOI-USGS/sbtools","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sbtools/sbtools_1.4.1.tar.gz","https://cran.r-project.org/src/contrib/sbtools_1.4.1.tar.gz"],"subdirs":["noarch"],"summary":"Tools for interacting with U.S. Geological Survey ScienceBase <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and collaborative data management platform. Functions included for querying ScienceBase, and creating and fetching datasets.","text_prefix":false,"timestamp":1758651774,"version":"1.4.1"},"r-scales":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://scales.r-lib.org, https://github.com/r-lib/scales","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scales/scales_1.4.0.tar.gz","https://cran.r-project.org/src/contrib/scales_1.4.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.","text_prefix":false,"timestamp":1533215431,"version":"1.4.0"},"r-scam":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=scam","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scam/scam_1.2-22.tar.gz","https://cran.r-project.org/src/contrib/scam_1.2-22.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>. Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of gam() in package 'mgcv' are used for setting up the model matrix, printing and plotting the results.  Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with multiple smoothing parameter selection by GCV or UBRE/AIC.","text_prefix":false,"timestamp":1665686246,"version":"1.2_22"},"r-scatterd3":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://juba.github.io/scatterD3/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scatterD3/scatterD3_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/scatterD3_1.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive features : panning, zooming, tooltips, etc.","text_prefix":false,"timestamp":1621906487,"version":"1.0.2"},"r-scattermore":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"doc_url":"https://exaexa.github.io/scattermore/","home":"https://github.com/exaexa/scattermore","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scattermore/scattermore_1.2.tar.gz","https://cran.r-project.org/src/contrib/scattermore_1.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"C-based conversion of large scatterplot data to rasters. Speeds up plotting of data with millions of points.","text_prefix":false,"timestamp":1665540699,"version":"1.2"},"r-scatterpie":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=scatterpie","license":"Artistic-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scatterpie/scatterpie_0.2.6.tar.gz","https://cran.r-project.org/src/contrib/scatterpie_0.2.6.tar.gz"],"subdirs":["noarch"],"summary":"Creates scatterpie plots, especially useful for plotting pies on a map.","text_prefix":false,"timestamp":1599635410,"version":"0.2.6"},"r-scatterplot3d":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=scatterplot3d","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scatterplot3d/scatterplot3d_0.3-45.tar.gz","https://cran.r-project.org/src/contrib/scatterplot3d_0.3-45.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Plots a three dimensional (3D) point cloud.","text_prefix":false,"timestamp":0,"version":"0.3_45"},"r-scatterplotmatrix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://ifpen-gitlab.appcollaboratif.fr/detocs/scatterplotmatrix","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scatterPlotMatrix/scatterPlotMatrix_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/scatterPlotMatrix_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Create a scatter plot matrix, using `htmlwidgets` package and `d3.js`.","text_prefix":false,"timestamp":1785845240,"version":"0.3.0"},"r-scclust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/fsavje/scclust-R","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scclust/scclust_0.2.5.tar.gz","https://cran.r-project.org/src/contrib/scclust_0.2.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides wrappers for 'scclust', a C library for computationally efficient size-constrained clustering with near-optimal performance. See <https://github.com/fsavje/scclust> for more information.","text_prefix":false,"timestamp":1758188959,"version":"0.2.5"},"r-sccore":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/kharchenkolab/sccore","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sccore/sccore_1.0.7.tar.gz","https://cran.r-project.org/src/contrib/sccore_1.0.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.","text_prefix":false,"timestamp":1665548882,"version":"1.0.7"},"r-sccs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SCCS","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SCCS/SCCS_1.7.tar.gz","https://cran.r-project.org/src/contrib/SCCS_1.7.tar.gz"],"subdirs":["noarch"],"summary":"Various self-controlled case series models used to investigate associations between time-varying exposures such as vaccines or other drugs or non drug exposures and an adverse event can be fitted. Detailed information on the self-controlled case series method and its extensions with more examples can be found in Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y. (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies: A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) and <https://sccs-studies.info/index.html>.","text_prefix":false,"timestamp":1758267973,"version":"1.7"},"r-sccustomize":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/samuel-marsh/scCustomize","home":"https://samuel-marsh.github.io/scCustomize/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scCustomize/scCustomize_3.3.0.tar.gz","https://cran.r-project.org/src/contrib/scCustomize_3.3.0.tar.gz"],"subdirs":["linux-64","noarch"],"summary":"Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'.  'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions.  For citation please use: Marsh SE (2021) \"Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing\" <doi:10.5281/zenodo.5706430> RRID:SCR_024675.","text_prefix":false,"timestamp":1778658952,"version":"3.3.0"},"r-scholar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=scholar","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scholar/scholar_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/scholar_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides functions to extract citation data from Google Scholar.  Convenience functions are also provided for comparing multiple scholars and predicting future h-index values.","text_prefix":false,"timestamp":1655992037,"version":"1.0.0"},"r-schoolmath":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=schoolmath","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/schoolmath/schoolmath_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/schoolmath_0.5.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains functions and datasets for math taught in school. A main focus is set to prime-calculation.","text_prefix":false,"timestamp":1784102734,"version":"0.5.0"},"r-schtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=schtools","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/schtools/schtools_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/schtools_0.4.1.tar.gz"],"subdirs":["noarch"],"summary":"A collection of useful functions and example code created and used by the Schloss Lab for reproducible microbiome research. Perform common tasks like read files created by mothur <https://mothur.org/>, tidy up your microbiome data, and format R Markdown documents for publication. See the website <http://www.schlosslab.org/schtools/> for more information, documentation, and examples.","text_prefix":false,"timestamp":1668621510,"version":"0.4.1"},"r-scico":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/thomasp85/scico","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scico/scico_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/scico_1.5.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Colour choice in information visualisation is important in order to avoid being mislead by inherent bias in the used colour palette. The 'scico' package provides access to the perceptually uniform and colour-blindness  friendly palettes developed by Fabio Crameri and released under the  \"Scientific Colour-Maps\" moniker. The package contains 24 different palettes  and includes both diverging and sequential types.","text_prefix":false,"timestamp":1535672889,"version":"1.5.0"},"r-sciplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sciplot","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sciplot/sciplot_1.2-0.tar.gz","https://cran.r-project.org/src/contrib/sciplot_1.2-0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of functions that creates graphs with error bars for data collected from one-way or higher factorial designs.","text_prefix":false,"timestamp":0,"version":"1.2_0"},"r-scorecard":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ShichenXie/scorecard","doc_url":"http://shichen.name/scorecard/reference/index.html","home":"http://shichen.name/scorecard/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scorecard/scorecard_0.4.6.tar.gz","https://cran.r-project.org/src/contrib/scorecard_0.4.6.tar.gz"],"subdirs":["noarch"],"summary":"The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.","text_prefix":false,"timestamp":1628136575,"version":"0.4.6"},"r-scpubr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://enblacar.github.io/SCpubr-book/","home":"https://github.com/enblacar/SCpubr/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SCpubr/SCpubr_3.0.1.tar.gz","https://cran.r-project.org/src/contrib/SCpubr_3.0.1.tar.gz"],"subdirs":["noarch"],"summary":"A system that provides a streamlined way of generating publication-ready plots for known single-cell transcriptomics data in a \"publication ready\" format. The goal is to automatically generate plots with the highest quality possible so they can be used right away or with minimal modifications for a research article.","text_prefix":false,"timestamp":1773525786,"version":"3.0.1"},"r-scrime":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=scrime","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scrime/scrime_1.3.7.tar.gz","https://cran.r-project.org/src/contrib/scrime_1.3.7.tar.gz"],"subdirs":["noarch"],"summary":"Tools for the analysis of high-dimensional data developed/implemented at the group \"Statistical Complexity Reduction In Molecular Epidemiology\" (SCRIME). Main focus is on SNP data. But most of the functions can also be applied to other types of categorical data.","text_prefix":false,"timestamp":1769418055,"version":"1.3.7"},"r-scrm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/scrm/scrm-r","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scrm/scrm_1.7.5.tar.gz","https://cran.r-project.org/src/contrib/scrm_1.7.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"A coalescent simulator that allows the rapid simulation of biological sequences under neutral models of evolution. Different to other coalescent based simulations, it has an optional approximation parameter that allows for high accuracy while maintaining a linear run time cost for long sequences. It is optimized for simulating massive data sets as produced by Next- Generation Sequencing technologies for up to several thousand sequences.","text_prefix":false,"timestamp":1542857421,"version":"1.7.5"},"r-scrt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SCRT","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SCRT/SCRT_1.3.1.tar.gz","https://cran.r-project.org/src/contrib/SCRT_1.3.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs.","text_prefix":false,"timestamp":1538297633,"version":"1.3.1"},"r-scrutiny":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/lhdjung/scrutiny/","home":"https://lhdjung.github.io/scrutiny/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scrutiny/scrutiny_0.6.1.tar.gz","https://cran.r-project.org/src/contrib/scrutiny_0.6.1.tar.gz"],"subdirs":["noarch"],"summary":"Test published summary statistics for consistency (Brown and Heathers, 2017, <doi:10.1177/1948550616673876>; Allard, 2018, <https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>; Heathers and Brown, 2019, <https://osf.io/5vb3u/>). The package also provides infrastructure for implementing new error detection techniques.","text_prefix":false,"timestamp":1780139095,"version":"0.6.1"},"r-scrypt":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/rstudio/rscrypt","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scrypt/scrypt_0.1.6.tar.gz","https://cran.r-project.org/src/contrib/scrypt_0.1.6.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Functions for working with the scrypt key derivation functions originally described by Colin Percival <https://www.tarsnap.com/scrypt/scrypt.pdf> and in Percival and Josefsson (2016) <doi:10.17487/RFC7914>. Scrypt is a password-based key derivation function created by Colin Percival. The algorithm was specifically designed to make it costly to perform large-scale custom hardware attacks by requiring large amounts of memory.","text_prefix":false,"timestamp":1665480950,"version":"0.1.6"},"r-scs":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/FlorianSchwendinger/scs","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/scs/scs_3.2.7.tar.gz","https://cran.r-project.org/src/contrib/scs_3.2.7.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Solves convex cone programs via operator splitting. Can solve: linear programs ('LPs'), second-order cone programs ('SOCPs'), semidefinite programs ('SDPs'), exponential cone programs ('ECPs'), and power cone programs ('PCPs'), or problems with any combination of those cones. 'SCS' uses 'AMD' (a set of routines for permuting sparse matrices prior to factorization) and 'LDL' (a sparse 'LDL' factorization and solve package) from 'SuiteSparse' (<https://people.engr.tamu.edu/davis/suitesparse.html>).","text_prefix":false,"timestamp":1637744865,"version":"3.2.7"},"r-sctransform":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/satijalab/sctransform","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sctransform/sctransform_0.4.3.tar.gz","https://cran.r-project.org/src/contrib/sctransform_0.4.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A normalization method for single-cell UMI count data using a  variance stabilizing transformation. The transformation is based on a  negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija 2019  <doi:10.1101/576827> for more details.","text_prefix":false,"timestamp":1604192820,"version":"0.4.3"},"r-sda":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://strimmerlab.org/software/sda/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sda/sda_1.3.9.tar.gz","https://cran.r-project.org/src/contrib/sda_1.3.9.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides an efficient framework for  high-dimensional linear and diagonal discriminant analysis with  variable selection.  The classifier is trained using James-Stein-type  shrinkage estimators and predictor variables are ranked using  correlation-adjusted t-scores (CAT scores).  Variable selection error  is controlled using false non-discovery rates or higher criticism.","text_prefix":false,"timestamp":1540029156,"version":"1.3.9"},"r-sdcmicro":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sdcTools/sdcMicro","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sdcMicro/sdcMicro_5.8.2.tar.gz","https://cran.r-project.org/src/contrib/sdcMicro_5.8.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Data from statistical agencies and other institutions are mostly confidential. This package (see also Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>) can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>. Various risk estimation and anonymisation methods are included. Note that the package includes a graphical user interface (Meindl and Templ, 2019 <doi:10.3390/a12090191>) that allows to use various methods of this package.","text_prefix":false,"timestamp":1660073030,"version":"5.8.2"},"r-sdmpredictors":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://lifewatch.github.io/sdmpredictors/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/sdmpredictors_0.2.15.tar.gz"],"subdirs":["noarch"],"summary":"Terrestrial and marine predictors for species distribution modelling from multiple sources, including WorldClim <https://www.worldclim.org/>,, ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/> and MARSPEC <http://www.marspec.org/>.","text_prefix":false,"timestamp":1758721831,"version":"0.2.15"},"r-sdmtools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.rforge.net/SDMTools/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SDMTools/SDMTools_1.1-221.2.tar.gz","https://cran.r-project.org/src/contrib/SDMTools_1.1-221.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This packages provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc.. This package was made possible in part by financial support from the Australian Research Council & ARC Research Network for Earth System Science.","text_prefix":false,"timestamp":1542845218,"version":"1.1_221.2"},"r-sdtm.oak":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pharmaverse.github.io/sdtm.oak/, https://github.com/pharmaverse/sdtm.oak","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sdtm.oak/sdtm.oak_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/sdtm.oak_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"An Electronic Data Capture system (EDC) and Data Standard agnostic solution that enables the pharmaceutical programming community to develop Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) datasets in R. The reusable algorithms concept in 'sdtm.oak' provides a framework for modular programming and can potentially automate the conversion of raw clinical data to SDTM through standardized SDTM specifications. SDTM is one of the required standards for data submission to the Food and Drug Administration (FDA) in the United States and Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. SDTM standards are implemented following the SDTM Implementation Guide as defined by CDISC <https://www.cdisc.org/standards/foundational/sdtmig>.","text_prefix":false,"timestamp":1761946500,"version":"0.2.0"},"r-sdtmchecks":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pharmaverse.github.io/sdtmchecks/, https://github.com/pharmaverse/sdtmchecks","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sdtmchecks/sdtmchecks_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/sdtmchecks_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"A series of checks to identify common issues in Study Data Tabulation Model (SDTM) datasets. These checks are intended to be generalizable, actionable, and meaningful for analysis.","text_prefix":false,"timestamp":1758314737,"version":"1.0.0"},"r-seacarb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=seacarb","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/seacarb/seacarb_3.4.1.tar.gz","https://cran.r-project.org/src/contrib/seacarb_3.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.","text_prefix":false,"timestamp":1527924273,"version":"3.4.1"},"r-searcher":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-assist/searcher","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/searcher/searcher_0.0.7.tar.gz","https://cran.r-project.org/src/contrib/searcher_0.0.7.tar.gz"],"subdirs":["noarch"],"summary":"Provides a search interface to look up terms on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'. Upon searching, a browser window will open with the aforementioned search results.","text_prefix":false,"timestamp":1621869823,"version":"0.0.7"},"r-seasonal":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.seasonal.website","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/seasonal/seasonal_1.11.0.tar.gz","https://cloud.r-project.org/src/contrib/seasonal_1.11.0.tar.gz"],"subdirs":["noarch"],"summary":"Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the 'seasonalview' package. Uses the X-13-binaries from the 'x13binary' package.","text_prefix":false,"timestamp":1623755941,"version":"1.11.0"},"r-secret":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gaborcsardi/secret","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/secret/secret_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/secret_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Allow sharing sensitive information, for example passwords, 'API' keys, etc., in R packages, using public key cryptography.","text_prefix":false,"timestamp":1758559179,"version":"1.1.0"},"r-secretbase":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/shikokuchuo/secretbase/","home":"https://shikokuchuo.net/secretbase/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/secretbase/secretbase_1.3.1.tar.gz","https://cloud.r-project.org/src/contrib/secretbase_1.3.1.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"SHA-3 cryptographic hash and SHAKE256 extendable-output functions (XOF). The SHA-3 Secure Hash Standard was published by the National Institute of Standards and Technology (NIST) in 2015 at <doi:10.6028/NIST.FIPS.202>. Fast and memory-efficient implementation using the core algorithm from 'Mbed TLS' under the Trusted Firmware Project <https://www.trustedfirmware.org/projects/mbed-tls/>.","text_prefix":false,"timestamp":1790383052,"version":"1.3.1"},"r-secretsprovider":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SecretsProvider","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SecretsProvider/SecretsProvider_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/SecretsProvider_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Facilitates secret management by storing credentials in a dedicated file, keeping them out of your code base. The secrets are stored without encryption. This package is compatible with secrets stored by the 'SecretsProvider' 'Python' package <https://pypi.org/project/SecretsProvider/>.","text_prefix":false,"timestamp":1758564170,"version":"1.0.1"},"r-secutrialr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/SwissClinicalTrialOrganisation/secuTrialR","home":"https://github.com/SwissClinicalTrialOrganisation/secuTrialR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/secuTrialR/secuTrialR_1.3.3.tar.gz","https://cran.r-project.org/src/contrib/secuTrialR_1.3.3.tar.gz"],"subdirs":["noarch"],"summary":"Seamless and standardized interaction with data exported from the clinical data management system (CDMS) 'secuTrial'<https://www.secutrial.com>. The primary data export the package works with is a standard non-rectangular export.","text_prefix":false,"timestamp":1603708177,"version":"1.3.3"},"r-see":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://easystats.github.io/see/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/see/see_0.15.0.tar.gz","https://cloud.r-project.org/src/contrib/see_0.15.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides plotting utilities supporting easystats-packages (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for 'ggplot2'. Color scales are based on <https://www.materialui.co/colors>.","text_prefix":false,"timestamp":1622285645,"version":"0.15.0"},"r-seeds":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Newmi1988/seeds","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/seeds/seeds_0.9.1.tar.gz","https://cran.r-project.org/src/contrib/seeds_0.9.1.tar.gz"],"subdirs":["noarch"],"summary":"Algorithms to calculate the hidden inputs of systems of differential equations. These hidden inputs can be interpreted as a control that tries to minimize the discrepancies between a given model and taken measurements. The idea is also called the Dynamic Elastic Net, as proposed in the paper \"Learning (from) the errors of a systems biology model\" (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>. To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.","text_prefix":false,"timestamp":1758651202,"version":"0.9.1"},"r-segclust2d":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rpatin/segclust2d","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/segclust2d/segclust2d_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/segclust2d_0.3.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of  Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) <doi:10.1101/444794>.","text_prefix":false,"timestamp":1773518934,"version":"0.3.3"},"r-segmented":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=segmented","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/segmented/segmented_2.2-2.tar.gz","https://cloud.r-project.org/src/contrib/segmented_2.2-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Given a regression model, segmented `updates' it by adding one or more segmented (i.e., piece-wise linear) relationships. Several variables with multiple breakpoints are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>).","text_prefix":false,"timestamp":0,"version":"2.2_2"},"r-segregation":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/elbersb/segregation/","home":"https://elbersb.github.io/segregation/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/segregation/segregation_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/segregation_1.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Computes entropy-based segregation indices, as developed by Theil (1971) <isbn:978-0471858454>, with a focus on  the Mutual Information Index (M) and Theil's Information Index (H).  The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>,  is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation),  and by within and between terms. Includes standard error estimation by bootstrapping.","text_prefix":false,"timestamp":1758475014,"version":"1.1.0"},"r-sel":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SEL","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SEL/SEL_1.0-4.tar.gz","https://cran.r-project.org/src/contrib/SEL_1.0-4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"This package implements a novel method for fitting a bounded probability distribution to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009) for details.  For this purpose B-splines are used, and the density is obtained by penalized least squares based on a Brier entropy penalty.  The package provides methods for fitting the distribution as well as methods for evaluating the underlying density and cdf. In addition methods for plotting the distribution, drawing random numbers and calculating quantiles of the obtained distribution are provided.","text_prefix":false,"timestamp":1622089957,"version":"1.0_4"},"r-selectr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://sjp.co.nz/projects/selectr","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/selectr/selectr_0.6-0.tar.gz","https://cran.r-project.org/src/contrib/selectr_0.6-0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Translates a CSS3 selector into an equivalent XPath expression. This allows us to use CSS selectors when working with the XML package as it can only evaluate XPath expressions. Also provided are convenience functions useful for using CSS selectors on XML nodes. This package is a port of the Python package 'cssselect' (<https://cssselect.readthedocs.io/>).","text_prefix":false,"timestamp":1537099197,"version":"0.6_0"},"r-selenider":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ashbythorpe/selenider, https://ashbythorpe.github.io/selenider/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/selenider/selenider_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/selenider_0.4.1.tar.gz"],"subdirs":["noarch"],"summary":"A user-friendly wrapper for web automation, using either 'chromote' or 'selenium'. Provides a simple and consistent API to make web scraping and testing scripts easy to write and understand. Elements are lazy, and automatically wait for the website to be valid, resulting in reliable and reproducible code, with no visible impact on the experience of the programmer.","text_prefix":false,"timestamp":1758721697,"version":"0.4.1"},"r-sem":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://socserv.socsci.mcmaster.ca/jfox/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sem/sem_3.1-16.tar.gz","https://cran.r-project.org/src/contrib/sem_3.1-16.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares.","text_prefix":false,"timestamp":1633435167,"version":"3.1_16"},"r-semantic.assets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Appsilon/semantic.assets","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/semantic.assets/semantic.assets_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/semantic.assets_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Style sheets and JavaScript assets for 'shiny.semantic' package.","text_prefix":false,"timestamp":1757909237,"version":"1.1.0"},"r-semaphore":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cmmr.github.io/semaphore/, https://github.com/cmmr/semaphore","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/semaphore/semaphore_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/semaphore_1.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements named semaphores from the 'boost' 'C++' library <https://www.boost.org/>. A semaphore object is shared amongst several processes. This integer value can be safely incremented or decremented by each processes. Processes can also wait (blocking) for the value to become non-zero.","text_prefix":false,"timestamp":1758722206,"version":"1.2.0"},"r-semnetcleaner":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/AlexChristensen/SemNetCleaner","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SemNetCleaner/SemNetCleaner_1.3.7.tar.gz","https://cran.r-project.org/src/contrib/SemNetCleaner_1.3.7.tar.gz"],"subdirs":["noarch"],"summary":"Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible.","text_prefix":false,"timestamp":1595531079,"version":"1.3.7"},"r-semnetdictionaries":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/AlexChristensen/SemNetDictionaries","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/SemNetDictionaries/SemNetDictionaries_0.3.0.tar.gz","https://cloud.r-project.org/src/contrib/SemNetDictionaries_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to <https://github.com/AlexChristensen/SemNetDictionaries>.","text_prefix":false,"timestamp":1601127183,"version":"0.3.0"},"r-semplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SachaEpskamp/semPlot","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/semPlot/semPlot_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/semPlot_1.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Path diagrams and visual analysis of various SEM packages' output.","text_prefix":false,"timestamp":1784326688,"version":"1.2.0"},"r-semtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/simsem/semTools/wiki","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/semTools/semTools_0.5-9.tar.gz","https://cran.r-project.org/src/contrib/semTools_0.5-9.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides miscellaneous tools for structural equation modeling, many of which extend the 'lavaan' package. For example, latent interactions can be estimated using product indicators (Lin et al., 2010, <doi:10.1080/10705511.2010.488999>) and simple effects probed; analytical power analyses can be conducted (Jak et al., 2021, <doi:10.3758/s13428-020-01479-0>); and scale reliability can be estimated based on estimated factor-model parameters.","text_prefix":false,"timestamp":1538308036,"version":"0.5_9"},"r-semver":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/johndharrison/semver","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/semver/semver_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/semver_0.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools and functions for parsing, rendering and operating on semantic version strings. Semantic versioning is a simple set of rules and requirements that dictate how version numbers are assigned and incremented as outlined at <http://semver.org>.","text_prefix":false,"timestamp":1769601144,"version":"0.2.1"},"r-sen2r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://sen2r.ranghetti.info","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sen2r/sen2r_1.6.0.tar.gz","https://cran.r-project.org/src/contrib/sen2r_1.6.0.tar.gz"],"subdirs":["noarch"],"summary":"Functions to download Sentinel-2 optical images and perform preliminary processing operations. 'sen2r' provides the instruments required to easily perform (and eventually automate) the steps necessary to build a complete Sentinel-2 processing chain. A Graphical User Interface to facilitate data processing is also provided. For additional documentation refer to the following article: Ranghetti et al. (2020) <doi:10.1016/j.cageo.2020.104473>.","text_prefix":false,"timestamp":1654238216,"version":"1.6.0"},"r-sendmailr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/olafmersmann/sendmailR","home":"https://CRAN.R-project.org/package=sendmailR","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sendmailR/sendmailR_1.4-1.tar.gz","https://cran.r-project.org/src/contrib/sendmailR_1.4-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Package contains a simple SMTP client with minimal dependencies which provides a portable solution for sending email, including file attachments and inline html reports, from within R. SMTP Authentication and SSL/STARTTLS is implemented using curl.","text_prefix":false,"timestamp":1540067947,"version":"1.4_1"},"r-sensemakr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/carloscinelli/sensemakr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sensemakr/sensemakr_0.1.6.tar.gz","https://cran.r-project.org/src/contrib/sensemakr_0.1.6.tar.gz"],"subdirs":["noarch"],"summary":"Implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), \"Making Sense of Sensitivity: Extending Omitted Variable Bias.\" Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.","text_prefix":false,"timestamp":1758474320,"version":"0.1.6"},"r-sentimentanalysis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sfeuerriegel/SentimentAnalysis","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SentimentAnalysis/SentimentAnalysis_1.3-5.tar.gz","https://cran.r-project.org/src/contrib/SentimentAnalysis_1.3-5.tar.gz"],"subdirs":["noarch"],"summary":"Performs a sentiment analysis of textual contents in R. This implementation utilizes various existing dictionaries, such as Harvard IV, or finance-specific dictionaries. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogenous response variable.","text_prefix":false,"timestamp":1758626438,"version":"1.3_5"},"r-sentimentr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/trinker/sentimentr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sentimentr/sentimentr_2.9.0.tar.gz","https://cran.r-project.org/src/contrib/sentimentr_2.9.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).","text_prefix":false,"timestamp":1525333064,"version":"2.9.0"},"r-sepkoski":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Stratigraphic ranges of fossil marine animal genera from Sepkoski's (2002) published compendium. No changes have been made to any taxonomic names. However, first and last appearance intervals have been updated to be consistent with stages of the International Geological Timescale. Functionality for generating a plot of Sepkoski's evolutionary fauna is also included. For specific details on the compendium see: Sepkoski, J. J. (2002). A compendium of fossil marine animal genera. Bulletins of American Paleontology, 363, pp. 1\u2013560 (ISBN 0-87710-450-6). Access: <https://www.biodiversitylibrary.org/item/40634#page/5/mode/1up>.","dev_url":"https://github.com/palaeoverse/sepkoski","doc_url":"https://sepkoski.palaeoverse.org/","home":"https://sepkoski.palaeoverse.org/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"Sepkoski's Fossil Marine Animal Genera Compendium","text_prefix":false,"timestamp":1788181562,"version":"0.0.1"},"r-seqinr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://seqinr.r-forge.r-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/seqinr/seqinr_4.2-44.tar.gz","https://cran.r-project.org/src/contrib/seqinr_4.2-44.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Seqinr includes  utilities for sequence data management under the ACNUC system described in Gouy, M. et al. (1984) Nucleic Acids Res. 12:121-127 <doi:10.1093/nar/12.1Part1.121>.","text_prefix":false,"timestamp":1602318598,"version":"4.2_44"},"r-seqminer":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://seqminer.genomic.codes","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/seqminer/seqminer_9.4.tar.gz","https://cran.r-project.org/src/contrib/seqminer_9.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.","text_prefix":false,"timestamp":1636463471,"version":"9.4"},"r-seqnet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SeqNet","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/SeqNet/SeqNet_1.1.4.tar.gz","https://cloud.r-project.org/src/contrib/SeqNet_1.1.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) <doi:10.18637/jss.v098.i12>. Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks.","text_prefix":false,"timestamp":1791640949,"version":"1.1.4"},"r-seriation":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/mhahsler/seriation","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/seriation/seriation_1.5.8.tar.gz","https://cran.r-project.org/src/contrib/seriation_1.5.8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT).","text_prefix":false,"timestamp":1666669256,"version":"1.5.8"},"r-servr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/yihui/servr","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/servr/servr_0.33.tar.gz","https://cran.r-project.org/src/contrib/servr_0.33.tar.gz"],"subdirs":["noarch"],"summary":"Start an HTTP server in R to serve static files, or dynamic documents that can be converted to HTML files (e.g., R Markdown) under a given directory.","text_prefix":false,"timestamp":1595972209,"version":"0.33"},"r-session":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=session","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/session/session_1.0.3.tar.gz","https://cran.r-project.org/src/contrib/session_1.0.3.tar.gz"],"subdirs":["noarch"],"summary":"Utility functions for interacting with R processes from external programs. This package includes functions to save and restore session information (including loaded packages, and attached data objects), as well as functions to evaluate strings containing R commands and return the printed results or an execution transcript.","text_prefix":false,"timestamp":1758626426,"version":"1.0.3"},"r-sessioninfo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/sessioninfo#readme","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sessioninfo/sessioninfo_1.2.4.tar.gz","https://cran.r-project.org/src/contrib/sessioninfo_1.2.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Query and print information about the current R session. It is similar to 'utils::sessionInfo()', but includes more information about packages, and where they were installed from.","text_prefix":false,"timestamp":1525229190,"version":"1.2.4"},"r-set6":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/xoopR/set6","home":"https://xoopR.github.io/set6/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://raphaels1.r-universe.dev/src/contrib/set6_0.2.6.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"An object-oriented package for mathematical sets, upgrading the current gold-standard {sets}. Many forms of mathematical sets are implemented, including (countably finite) sets, tuples, intervals (countably infinite or uncountable), and fuzzy variants. Wrappers extend functionality by allowing symbolic representations of complex operations on sets, including unions, (cartesian) products, exponentiation, and differences (asymmetric and symmetric).","text_prefix":false,"timestamp":1627248666,"version":"0.2.6"},"r-setrng":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://distr.r-forge.r-project.org/","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/setRNG/setRNG_2024.2-1.tar.gz","https://cran.r-project.org/src/contrib/setRNG_2024.2-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"SetRNG provides utilities to help set and record the setting of the seed and the uniform and normal generators used when a random experiment is run. The utilities can be used in other functions  that do random experiments to simplify recording and/or setting all the  necessary information for reproducibility.  See the vignette and reference manual for examples.","text_prefix":false,"timestamp":1537097280,"version":"2024.2_1"},"r-sets":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sets","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sets/sets_1.0-25.tar.gz","https://cran.r-project.org/src/contrib/sets_1.0-25.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals.","text_prefix":false,"timestamp":1639261452,"version":"1.0_25"},"r-settings":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/settings","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/settings/settings_0.2.7.tar.gz","https://cran.r-project.org/src/contrib/settings_0.2.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides option settings management that goes beyond R's default 'options' function. With this package, users can define their own option settings manager holding option names, default values and  (if so desired) ranges or sets of allowed option values that will be  automatically checked. Settings can then be retrieved, altered and reset  to defaults with ease. For R programmers and package developers it offers  cloning and merging functionality which allows for conveniently defining  global and local options, possibly in a multilevel options hierarchy. See  the package vignette for some examples concerning functions, S4 classes,  and reference classes. There are convenience functions to reset par()  and options() to their 'factory defaults'.","text_prefix":false,"timestamp":1539795860,"version":"0.2.7"},"r-seurat":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/satijalab/seurat","doc_url":"https://satijalab.org/seurat/reference/index.html","home":"https://satijalab.org/seurat","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Seurat/Seurat_5.5.1.tar.gz","https://cran.r-project.org/src/contrib/Seurat_5.5.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.","text_prefix":false,"timestamp":1616061479,"version":"5.5.1"},"r-seuratdisk":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The h5Seurat file format is specifically designed for the storage and analysis of multi-modal single-cell and spatially-resolved expression experiments, for example, from CITE-seq or 10X Visium technologies. It holds all molecular information and associated metadata, including (for example) nearest-neighbor graphs, dimensional reduction information, spatial coordinates and image data, and cluster labels. We also support rapid and on-disk conversion between h5Seurat and AnnData objects, with the goal of enhancing interoperability between Seurat and Scanpy.","dev_url":"https://github.com/mojaveazure/seurat-disk","home":"https://github.com/mojaveazure/seurat-disk","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/mojaveazure/seurat-disk/archive/877d4e18ab38c686f5db54f8cd290274ccdbe295.tar.gz","subdirs":["linux-64","noarch","osx-64"],"summary":"Support for multi-modal single cell through h5Seurat and AnnData for Seurat","text_prefix":false,"timestamp":1618980291,"version":"0.0.9021"},"r-seuratobject":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/satijalab/seurat-object","home":"https://satijalab.github.io/seurat-object/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SeuratObject/SeuratObject_5.4.0.tar.gz","https://cran.r-project.org/src/contrib/SeuratObject_5.4.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for more details.","text_prefix":false,"timestamp":1637717673,"version":"5.4.0"},"r-sever":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://sever.john-coene.com/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sever/sever_0.0.7.tar.gz","https://cran.r-project.org/src/contrib/sever_0.0.7.tar.gz"],"subdirs":["noarch"],"summary":"Customise 'Shiny' disconnected screens as well as sanitize error messages to make them clearer and friendlier to the user.","text_prefix":false,"timestamp":1758705920,"version":"0.0.7"},"r-sf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-spatial/sf/","home":"https://r-spatial.github.io/sf/","license":"GPL-2.0-only OR MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/sf/sf_1.1-3.tar.gz","https://cloud.r-project.org/src/contrib/sf_1.1-3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations.","text_prefix":true,"timestamp":1633806315,"version":"1.1_3"},"r-sfarrow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://wcjochem.github.io/sfarrow/","home":"https://github.com/wcjochem/sfarrow","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sfarrow/sfarrow_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/sfarrow_0.4.1.tar.gz"],"subdirs":["noarch"],"summary":"Support for reading/writing simple feature ('sf') spatial objects from/to 'Parquet' files. 'Parquet' files are an open-source, column-oriented data storage format from Apache (<https://parquet.apache.org/>), now popular across programming languages. This implementation converts simple feature list geometries into well-known binary format for use by 'arrow', and coordinate reference system information is maintained in a standard metadata format.","text_prefix":false,"timestamp":1773525464,"version":"0.4.1"},"r-sfd":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sfd","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sfd/sfd_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/sfd_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"A collection of pre-optimized space-filling designs, for up to ten parameters, is contained here. Functions are provided to access designs described by Husslage et al (2011) <doi:10.1007/s11081-010-9129-8> and Wang and Fang (2005) <doi:10.1142/9789812701190_0040>. The design types included are Audze-Eglais, MaxiMin, and uniform.","text_prefix":false,"timestamp":1757518491,"version":"0.1.0"},"r-sfheaders":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://dcooley.github.io/sfheaders/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sfheaders/sfheaders_0.4.5.tar.gz","https://cran.r-project.org/src/contrib/sfheaders_0.4.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Converts between R and Simple Feature 'sf' objects, without depending on the Simple Feature library. Conversion functions are available at both the R level, and through 'Rcpp'.","text_prefix":false,"timestamp":1604277910,"version":"0.4.5"},"r-sfsmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sfsmisc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/sfsmisc/sfsmisc_1.1-26.tar.gz","https://cloud.r-project.org/src/contrib/sfsmisc_1.1-26.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990's. For graphics, have pretty (Log-scale) axes, an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().","text_prefix":false,"timestamp":1535797980,"version":"1.1_26"},"r-sftime":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sftime","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sftime/sftime_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/sftime_0.3.2.tar.gz"],"subdirs":["noarch"],"summary":"Classes and methods for spatial objects that have a registered time column, in particular for irregular spatiotemporal data. The time column can be of any type, but needs to be ordinal. 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In addition, it contains pre-computed Pathway-GPS data for pathways in the KEGG and  Reactome pathway repositories and mechanisms for extracting GPS for user-supplied repositories.","text_prefix":false,"timestamp":1774276527,"version":"3.2.0"},"r-simcdm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tmsalab/simcdm","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/simcdm/simcdm_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/simcdm_0.1.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides efficient R and 'C++' routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy \"And\" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.","text_prefix":false,"timestamp":1766978234,"version":"0.1.2"},"r-simcomp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SimComp","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SimComp/SimComp_3.6.tar.gz","https://cran.r-project.org/src/contrib/SimComp_3.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).","text_prefix":false,"timestamp":0,"version":"3.6"},"r-simdesign":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/philchalmers/SimDesign","doc_url":"https://github.com/philchalmers/SimDesign/wiki","home":"https://philchalmers.github.io/SimDesign/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SimDesign/SimDesign_2.26.tar.gz","https://cran.r-project.org/src/contrib/SimDesign_2.26.tar.gz"],"subdirs":["noarch"],"summary":"Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.","text_prefix":false,"timestamp":1786443588,"version":"2.26"},"r-simfit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=simfit","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/simfit/simfit_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/simfit_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Simulates data from model objects (e.g., from lm(), glm()), and plots this along with the original data to compare how well the simulated data matches the original data to determine model fit.","text_prefix":false,"timestamp":1758663873,"version":"0.1.0"},"r-simpleboot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rdpeng/simpleboot","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/simpleboot/simpleboot_1.1-8.tar.gz","https://cran.r-project.org/src/contrib/simpleboot_1.1-8.tar.gz"],"subdirs":["noarch"],"summary":"Simple bootstrap routines.","text_prefix":false,"timestamp":1544989460,"version":"1.1_8"},"r-simplecache":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.github.com/databio/simpleCache","license":"BSD-2-clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/simpleCache/simpleCache_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/simpleCache_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides intuitive functions for caching R objects, encouraging reproducible, restartable, and distributed R analysis. The user selects a location to store caches, and then provides  nothing more than a cache name and instructions (R code) for how to produce the R object. Also provides some advanced options like environment assignments, recreating or reloading caches, and cluster compute bindings (using the 'batchtools' package) making it flexible enough for use in large-scale data analysis projects.","text_prefix":false,"timestamp":1589523610,"version":"0.5.0"},"r-simplevis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://StatisticsNZ.github.io/simplevis/, https://github.com/StatisticsNZ/simplevis/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/simplevis/simplevis_7.1.0.tar.gz","https://cran.r-project.org/src/contrib/simplevis_7.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Wrapper functions around the amazing 'ggplot2' and 'leaflet' packages that aims to simplify beautiful 'ggplot2' and 'leaflet' visualisation. Precursor package to the 'ggblanket' package.","text_prefix":false,"timestamp":1654695950,"version":"7.1.0"},"r-sinaplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sinaplot","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sinaplot/sinaplot_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/sinaplot_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"The sinaplot is a data visualization chart suitable for plotting any single variable in a multiclass data set. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class.","text_prefix":false,"timestamp":1758359569,"version":"1.1.0"},"r-sinew":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/metrumresearchgroup/sinew","license":"GPL-2.0-only OR GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sinew/sinew_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/sinew_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Create 'roxygen2' skeleton populated with information scraped from the within the function script. Also creates field entries for imports in the 'DESCRIPTION' and import in the 'NAMESPACE' files. Can be run from the R console or through the 'RStudio' 'addin' menu.","text_prefix":false,"timestamp":1621999869,"version":"0.4.0"},"r-sirius-ms":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"R client for the SIRIUS (local) REST API (OpenAPI) that provides the full functionality of the SIRIUS MS/MS analyses software including its web based features as background service.","dev_url":"https://github.com/sirius-ms/sirius-client-openAPI/tree/master/client-api_r","doc_url":"https://v6.docs.sirius-ms.io/","home":"https://github.com/sirius-ms/sirius-client-openAPI/tree/master/client-api_r","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/sirius-ms/sirius-client-openAPI/archive/refs/tags/3.2+sirius6.5.4.zip","subdirs":["noarch"],"summary":"RSirius: R library for SIRIUS MS/MS analyses software.","text_prefix":false,"timestamp":1787738416,"version":"3.2+sirius6.5.4"},"r-sirt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://sites.google.com/view/alexander-robitzsch/software","home":"https://github.com/alexanderrobitzsch/sirt","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sirt/sirt_4.2-133.tar.gz","https://cran.r-project.org/src/contrib/sirt_4.2-133.tar.gz"],"subdirs":["linux-64","win-64"],"summary":"Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), MCMC for hierarchical IRT models and testlet models (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>), ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, <doi:10.1177/014662169602000403>), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, <doi:10.1080/00273171.2016.1142856>).","text_prefix":false,"timestamp":1773560489,"version":"4.2_133"},"r-sitmo":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/coatless/sitmo","home":"https://thecoatlessprofessor.com/projects/sitmo/, https://github.com/stdfin/random/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sitmo/sitmo_2.0.2.tar.gz","https://cran.r-project.org/src/contrib/sitmo_2.0.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence. The objective of this library to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and 'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the header files inside of 'sitmo' instead of including a copy of each engine within their individual package. Lastly, the package contains example implementations using the 'sitmo' package and three accompanying vignette that provide additional information.","text_prefix":false,"timestamp":1621666620,"version":"2.0.2"},"r-sits":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/e-sensing/sits","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sits/sits_1.5.4.tar.gz","https://cran.r-project.org/src/contrib/sits_1.5.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The sits R package provides a set of tools for analysis, visualization and classification of satellite image time series. The main aim of SITS is to support land cover and land change classification of image data cubes using machine learning methods. The basic workflow in SITS is:  1. Create a data cube using image collections available in the cloud or in local machines. 2. Extract time series from the data cube which are used as training data. 3. Perform quality control and filtering on the samples. 4. Train a machine learning model using the extracted samples. 5. Classify the data cube using the trained model. 6. Post-process the classified images. 7. Evaluate the accuracy of the classification using best practices.","text_prefix":false,"timestamp":1642277813,"version":"1.5.4"},"r-sizer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dereksonderegger/SiZer","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SiZer/SiZer_0.1-8.tar.gz","https://cran.r-project.org/src/contrib/SiZer_0.1-8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Calculates and plots the SiZer map for scatterplot data.  A  SiZer map is a way of examining when the p-th derivative of a  scatterplot-smoother is significantly negative, possibly zero or  significantly positive across a range of smoothing bandwidths.","text_prefix":false,"timestamp":1536088084,"version":"0.1_8"},"r-sjlabelled":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://strengejacke.github.io/sjlabelled","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sjlabelled/sjlabelled_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/sjlabelled_1.2.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Collection of functions dealing with labelled data, like reading and  writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways  to get, set or change value and variable label attributes, to convert  labelled vectors into factors or numeric (and vice versa), or to deal with  multiple declared missing values.","text_prefix":false,"timestamp":1533983414,"version":"1.2.0"},"r-sjmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://strengejacke.github.io/sjmisc","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sjmisc/sjmisc_2.8.11.tar.gz","https://cran.r-project.org/src/contrib/sjmisc_2.8.11.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Collection of miscellaneous utility functions, supporting data  transformation tasks like recoding, dichotomizing or grouping variables,  setting and replacing missing values. The data transformation functions  also support labelled data, and all integrate seamlessly into a  'tidyverse'-workflow.","text_prefix":false,"timestamp":1533451211,"version":"2.8.11"},"r-sjplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://strengejacke.github.io/sjPlot/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sjPlot/sjPlot_2.9.0.tar.gz","https://cran.r-project.org/src/contrib/sjPlot_2.9.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.","text_prefix":false,"timestamp":1532111379,"version":"2.9.0"},"r-sjstats":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/strengejacke/sjstats, https://strengejacke.github.io/sjstats","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sjstats/sjstats_0.19.1.tar.gz","https://cran.r-project.org/src/contrib/sjstats_0.19.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures,  which otherwise could only be calculated with additional effort  (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared),  or for which currently no functions available. Second, another focus  lies on weighted variants of common statistical measures and tests  like weighted standard error, mean, t-test, correlation, and more.","text_prefix":false,"timestamp":1531293450,"version":"0.19.1"},"r-skat":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SKAT","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SKAT/SKAT_2.2.5.tar.gz","https://cran.r-project.org/src/contrib/SKAT_2.2.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.","text_prefix":false,"timestamp":1621984061,"version":"2.2.5"},"r-skellam":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://r-forge.r-project.org/projects/healthqueues/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/skellam/skellam_0.2.4.tar.gz","https://cran.r-project.org/src/contrib/skellam_0.2.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions for the Skellam distribution, including: density (pmf), cdf, quantiles and regression.","text_prefix":false,"timestamp":1540152284,"version":"0.2.4"},"r-skewhyperbolic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://r-forge.r-project.org/projects/rmetrics/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SkewHyperbolic/SkewHyperbolic_0.4-2.tar.gz","https://cran.r-project.org/src/contrib/SkewHyperbolic_0.4-2.tar.gz"],"subdirs":["noarch"],"summary":"Functions are provided for the density function, distribution function, quantiles and random number generation for the skew hyperbolic t-distribution. There are also functions that fit the distribution to data. There are functions for the mean, variance, skewness, kurtosis and mode of a given distribution and to calculate moments of any order about any centre. To assess goodness of fit, there are functions to generate a Q-Q plot, a P-P plot and a tail plot.","text_prefix":false,"timestamp":1758604183,"version":"0.4_2"},"r-skimr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/skimr (website), https://github.com/ropensci/skimr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/skimr/skimr_2.2.2.tar.gz","https://cran.r-project.org/src/contrib/skimr_2.2.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the \"Using skimr\" vignette and the README.","text_prefix":false,"timestamp":0,"version":"2.2.2"},"r-skmeans":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=skmeans","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/skmeans/skmeans_0.2-20.tar.gz","https://cran.r-project.org/src/contrib/skmeans_0.2-20.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Algorithms to compute spherical k-means partitions. 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The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, <doi:10.1007/978-3-540-71918-2>), SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi:10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi:10.1080/00207543.2017.1380326>) and several simulation functions. 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The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.","text_prefix":false,"timestamp":1540164971,"version":"2.3.1"},"r-snotelr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bluegreen-labs/snotelr","license":"AGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/snotelr/snotelr_1.5.2.tar.gz","https://cran.r-project.org/src/contrib/snotelr_1.5.2.tar.gz"],"subdirs":["noarch"],"summary":"Programmatic interface to the 'SNOTEL' snow data (<https://www.nrcs.usda.gov/wps/portal/wcc/home>). Provides easy downloads of snow data into your R work space or a local directory. Additional post-processing routines to extract snow season indexes are provided.","text_prefix":false,"timestamp":1758708512,"version":"1.5.2"},"r-snow":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=snow","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/snow/snow_0.4-4.tar.gz","https://cran.r-project.org/src/contrib/snow_0.4-4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Support for simple parallel computing in R.","text_prefix":false,"timestamp":0,"version":"0.4_4"},"r-snowballc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://r-forge.r-project.org/projects/r-temis/","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SnowballC/SnowballC_0.7.1.tar.gz","https://cran.r-project.org/src/contrib/SnowballC_0.7.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An R interface to the C libstemmer library that implements Porter's word stemming algorithm for collapsing words to a common root to aid comparison of vocabulary. Currently supported languages are Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish and Turkish.","text_prefix":false,"timestamp":1665269921,"version":"0.7.1"},"r-snowfall":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=snowfall","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/snowfall/snowfall_1.84-6.3.tar.gz","https://cran.r-project.org/src/contrib/snowfall_1.84-6.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Usability wrapper around snow for easier development of parallel R programs. This package offers e.g. extended error checks, and additional functions. All functions work in sequential mode, too, if no cluster is present or wished. Package is also designed as connector to the cluster management tool sfCluster, but can also used without it.","text_prefix":false,"timestamp":1538233704,"version":"1.84_6.3"},"r-snowflakeauth":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/posit-dev/snowflakeauth","home":"https://posit-dev.github.io/snowflakeauth/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/snowflakeauth/snowflakeauth_0.3.1.tar.gz","https://cloud.r-project.org/src/contrib/snowflakeauth_0.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Authentication helpers for 'Snowflake'. It provides compatibility with authentication approaches supported by the 'Snowflake Connector for Python' <https://pypi.org/project/snowflake-connector-python> and the 'Snowflake CLI' <https://pypi.org/project/snowflake-cli>.","text_prefix":false,"timestamp":1791445864,"version":"0.3.1"},"r-snpassoc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/isglobal-brge/SNPassoc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SNPassoc/SNPassoc_2.3.1.tar.gz","https://cran.r-project.org/src/contrib/SNPassoc_2.3.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"This package carries out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated.","text_prefix":false,"timestamp":1539910492,"version":"2.3.1"},"r-snpls":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sNPLS","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sNPLS/sNPLS_1.0.27.tar.gz","https://cran.r-project.org/src/contrib/sNPLS_1.0.27.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores.","text_prefix":false,"timestamp":1541530780,"version":"1.0.27"},"r-sodium":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jeroen/sodium#readme (devel) https://download.libsodium.org/doc/ (upstream)","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sodium/sodium_1.4.0.tar.gz","https://cran.r-project.org/src/contrib/sodium_1.4.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Bindings to 'libsodium': a modern, easy-to-use software library for encryption, decryption, signatures, password hashing and more. Sodium uses curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein, which has become very popular after it was discovered that the NSA had backdoored Dual EC DRBG.","text_prefix":false,"timestamp":1634846559,"version":"1.4.0"},"r-softimpute":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=softImpute","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/softImpute/softImpute_1.4-3.tar.gz","https://cran.r-project.org/src/contrib/softImpute_1.4-3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an \"EM\" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named \"Incomplete\" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)","text_prefix":false,"timestamp":1603206547,"version":"1.4_3"},"r-soildb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://ncss-tech.github.io/AQP/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/soilDB/soilDB_2.9.2.tar.gz","https://cran.r-project.org/src/contrib/soilDB_2.9.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"A collection of functions for reading data from USDA-NCSS soil databases.","text_prefix":false,"timestamp":0,"version":"2.9.2"},"r-solarius":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/web/packages/solarius/index.html","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/solarius/solarius_0.3.0.2.tar.gz","https://cran.r-project.org/src/contrib/solarius_0.3.0.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"SOLAR is the standard software program to perform linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. This package allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It also parses the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses, that makes the calculation of genome-wide scans more efficient. See <http","text_prefix":false,"timestamp":0,"version":"0.3.0.2"},"r-solrium":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/solrium (devel), https://docs.ropensci.org/solrium/ (user manual)","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/solrium/solrium_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/solrium_1.2.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a set of functions for querying and parsing data from 'Solr' (<https://lucene.apache.org/solr>) 'endpoints' (local and remote), including search, 'faceting', 'highlighting', 'stats', and 'more like this'. In addition, some functionality is included for creating, deleting, and updating documents in a 'Solr' 'database'.","text_prefix":false,"timestamp":1539915424,"version":"1.2.0"},"r-solvesaphe":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SolveSAPHE","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SolveSAPHE/SolveSAPHE_2.1.0.tar.gz","https://cran.r-project.org/src/contrib/SolveSAPHE_2.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Universal and robust algorithm for solving the total alkalinity-pH equation presented in G. Munhoven (2013) <doi:10.5194/gmd-6-1367-2013> and G. Munhoven (2021) <doi:10.5194/gmd-2020-447>. The total alkalinity-pH equation relates total alkalinity and pH for a given set of acid-base concentrations in a given water sample, among which carbonic acid. This package is particularly useful in marine chemistry involving dissolved inorganic carbon. Original package in Fortran can be found at <doi:10.5281/zenodo.4328965>.","text_prefix":false,"timestamp":1757926140,"version":"2.1.0"},"r-som":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=som","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/som/som_0.3-5.2.tar.gz","https://cran.r-project.org/src/contrib/som_0.3-5.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Self-Organizing Map (with application in gene clustering).","text_prefix":false,"timestamp":1757789135,"version":"0.3_5.2"},"r-somadataio":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://somalogic.github.io/SomaDataIO/, https://somalogic.com","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SomaDataIO/SomaDataIO_6.6.1.tar.gz","https://cran.r-project.org/src/contrib/SomaDataIO_6.6.1.tar.gz"],"subdirs":["noarch"],"summary":"Load and export 'SomaScan' data via the 'Standard BioTools, Inc.' structured text file called an ADAT ('*.adat'). For file format see <https://github.com/SomaLogic/SomaLogic-Data/blob/main/README.md>. The package also exports auxiliary functions for manipulating, wrangling, and extracting relevant information from an ADAT object once in memory.","text_prefix":false,"timestamp":1779109704,"version":"6.6.1"},"r-sombrero":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SOMbrero","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SOMbrero/SOMbrero_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/SOMbrero_1.5.0.tar.gz"],"subdirs":["noarch"],"summary":"The stochastic (also called on-line) version of the Self-Organising Map (SOM) algorithm is provided. Different versions of the algorithm are implemented, for numeric and relational data and for contingency tables as described, respectively, in Kohonen (2001) <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005) <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004) <doi:10.1016/j.neunet.2004.07.010>. The package also contains many plotting features (to help the user interpret the results) and a graphical user interface based on 'shiny'.","text_prefix":false,"timestamp":1591795962,"version":"1.5.0"},"r-soniclength":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sonicLength","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sonicLength/sonicLength_1.4.7.tar.gz","https://cran.r-project.org/src/contrib/sonicLength_1.4.7.tar.gz"],"subdirs":["noarch"],"summary":"Estimate the abundance of cell clones from the distribution of lengths of DNA fragments (as created by sonication, whence `sonicLength').  The algorithm in \"Estimating abundances of retroviral insertion sites from DNA fragment length data\" by Berry CC, Gillet NA, Melamed A, Gormley N, Bangham CR, Bushman FD. Bioinformatics; 2012 Mar 15;28(6):755-62 is implemented.  The experimental setting and estimation details are described in detail there. Briefly, integration of new DNA in a host genome (due to retroviral infection or gene therapy) can be tracked using DNA sequencing, potentially allowing characterization of the abundance of individual cell clones bearing distinct integration sites. The locations of integration sites can be determined by fragmenting the host DNA (via sonication or fragmentase), breaking the newly integrated DNA at a known sequence, amplifying the fragments containing both host and integrated DNA, sequencing those amplicons, then mapping the host sequences to positions on the reference genome. The relative number of fragments containing a given position in the host genome estimates the relative abundance of cells hosting the corresponding integration site, but that number is not available and the count of amplicons per fragment varies widely.  However, the expected number of distinct fragment lengths is a function of the abundance of cells hosting an integration site at a given position and a certain nuisance parameter. The algorithm implicitly estimates that function to estimate the relative abundance.","text_prefix":false,"timestamp":1545380332,"version":"1.4.7"},"r-sortable":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/sortable, https://rstudio.github.io/sortable/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sortable/sortable_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/sortable_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Enables drag-and-drop behaviour in Shiny apps, by exposing the functionality of the 'SortableJS' <https://sortablejs.github.io/Sortable/> JavaScript library as an 'htmlwidget'. You can use this in Shiny apps and widgets, 'learnr' tutorials as well as R Markdown. In addition, provides a custom 'learnr' question type - 'question_rank()' - that allows ranking questions with drag-and-drop.","text_prefix":false,"timestamp":1635845944,"version":"0.5.0"},"r-sos":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sos","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sos/sos_2.1-8.tar.gz","https://cran.r-project.org/src/contrib/sos_2.1-8.tar.gz"],"subdirs":["noarch"],"summary":"Search contributed R packages, sort by package.","text_prefix":false,"timestamp":1665472964,"version":"2.1_8"},"r-soupx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/constantAmateur/SoupX","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SoupX/SoupX_1.6.2.tar.gz","https://cran.r-project.org/src/contrib/SoupX_1.6.2.tar.gz"],"subdirs":["noarch"],"summary":"Quantify, profile and remove ambient mRNA contamination (the \"soup\") from droplet based single cell RNA-seq experiments.  Implements the method described in Young et al. (2018) <doi:10.1101/303727>.","text_prefix":false,"timestamp":1621765059,"version":"1.6.2"},"r-sourcetools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sourcetools","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sourcetools/sourcetools_0.1.7-2.tar.gz","https://cran.r-project.org/src/contrib/sourcetools_0.1.7-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Tools for the reading and tokenization of R code. The 'sourcetools' package provides both an R and C++ interface for the tokenization of R code, and helpers for interacting with the tokenized representation of R code.","text_prefix":false,"timestamp":0,"version":"0.1.7_2"},"r-sp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/edzer/sp/","home":"https://edzer.github.io/sp/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sp/sp_2.2-3.tar.gz","https://cran.r-project.org/src/contrib/sp_2.2-3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.","text_prefix":false,"timestamp":1532416273,"version":"2.2_3"},"r-spacefillr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/tylermorganwall/spacefillr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spacefillr/spacefillr_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/spacefillr_0.4.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol',  'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from \"Constructing Sobol sequences with better two-dimensional projections\" (2012) <doi:10.1137/070709359> S. Joe and F. Y. Kuo, \"Progressive Multi-Jittered Sample Sequences\" (2018) <https://graphics.pixar.com/library/ProgressiveMultiJitteredSampling/paper.pdf> Christensen, P., Kensler, A. and Kilpatrick, C., and \"A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space\" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, <doi:10.1145/3306307.3328191>.","text_prefix":false,"timestamp":1665960987,"version":"0.4.0"},"r-spacesxyz":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=spacesXYZ","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spacesXYZ/spacesXYZ_1.6-0.tar.gz","https://cran.r-project.org/src/contrib/spacesXYZ_1.6-0.tar.gz"],"subdirs":["noarch"],"summary":"Functions for converting among CIE XYZ, xyY, Lab, and Luv. Calculate Correlated Color Temperature (CCT) and the Planckian and daylight loci. The XYZs of some standard illuminants and some standard linear chromatic adaptation transforms (CATs) are included. Three standard color difference metrics are included.","text_prefix":false,"timestamp":1758032193,"version":"1.6_0"},"r-spacetime":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/edzer/spacetime","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spacetime/spacetime_1.3-3.tar.gz","https://cran.r-project.org/src/contrib/spacetime_1.3-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.","text_prefix":false,"timestamp":0,"version":"1.3_3"},"r-spacyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://spacyr.quanteda.io","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spacyr/spacyr_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/spacyr_1.3.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An R wrapper to the 'Python' 'spaCy' 'NLP' library, from <http://spacy.io>.","text_prefix":false,"timestamp":1528082151,"version":"1.3.0"},"r-spam":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.math.uzh.ch/spam/","license":"LGPL-2.0-only OR BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spam/spam_2.11-4.tar.gz","https://cran.r-project.org/src/contrib/spam_2.11-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation(\"spam\")' for details.","text_prefix":false,"timestamp":1568744667,"version":"2.11_4"},"r-spam64":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://git.math.uzh.ch/reinhard.furrer/spam","license":"LGPL-2.0-only OR BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spam64/spam64_2.11-4.tar.gz","https://cran.r-project.org/src/contrib/spam64_2.11-4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides the Fortran code of the R package 'spam' with 64-bit integers. Loading this package together with the R package spam enables the sparse matrix class spam to handle huge sparse matrices with more than 2^31-1 non-zero elements. Documentation is provided in Gerber, Moesinger and Furrer (2017) <doi:10.1016/j.cageo.2016.11.015>.","text_prefix":false,"timestamp":1780657731,"version":"2.11_4"},"r-sparcl":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sparcl","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparcl/sparcl_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/sparcl_1.0.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements the sparse clustering methods of Witten and Tibshirani (2010), \"A framework for feature selection in clustering,\" published in Journal of the American Statistical Association 105(490): 713-726.","text_prefix":false,"timestamp":1774276917,"version":"1.0.4"},"r-sparklyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://spark.rstudio.com","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/sparklyr/sparklyr_1.9.6.tar.gz","https://cloud.r-project.org/src/contrib/sparklyr_1.9.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.","text_prefix":false,"timestamp":0,"version":"1.9.6"},"r-sparklyr.nested":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sparklyr.nested","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparklyr.nested/sparklyr.nested_0.0.4.tar.gz","https://cran.r-project.org/src/contrib/sparklyr.nested_0.0.4.tar.gz"],"subdirs":["noarch"],"summary":"A 'sparklyr' extension adding the capability to work easily with nested data.","text_prefix":false,"timestamp":1758691048,"version":"0.0.4"},"r-sparkr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/apache/spark","home":"http://spark.apache.org/","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://dist.apache.org/repos/dist/release/spark/spark-4.2.0/SparkR_4.2.0.tar.gz","subdirs":["noarch"],"summary":"Provides an R Front end for 'Apache Spark' <https://spark.apache.org>.","text_prefix":false,"timestamp":1657923692,"version":"4.2.0"},"r-sparktf":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sparktf","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparktf/sparktf_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/sparktf_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"A 'sparklyr' extension that enables reading and writing 'TensorFlow' TFRecord files via 'Apache Spark'.","text_prefix":false,"timestamp":1758585156,"version":"0.1.0"},"r-sparkxgb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/rstudio/sparkxgb","home":"https://CRAN.R-project.org/package=sparkxgb","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparkxgb/sparkxgb_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/sparkxgb_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"A 'sparklyr' <https://spark.posit.co/> extension that provides an  interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 'XGBoost' is an  optimized distributed gradient boosting library.","text_prefix":false,"timestamp":1588669340,"version":"0.2.1"},"r-sparql":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SPARQL","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SPARQL/SPARQL_1.16.tar.gz","https://cran.r-project.org/src/contrib/SPARQL_1.16.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Use SPARQL to pose SELECT or UPDATE queries to an end-point.","text_prefix":false,"timestamp":0,"version":"1.16"},"r-sparsebn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/itsrainingdata/sparsebn","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparsebn/sparsebn_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/sparsebn_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.","text_prefix":false,"timestamp":1563602340,"version":"0.1.2"},"r-sparsebnutils":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/itsrainingdata/sparsebnUtils","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparsebnUtils/sparsebnUtils_0.0.8.tar.gz","https://cran.r-project.org/src/contrib/sparsebnUtils_0.0.8.tar.gz"],"subdirs":["noarch"],"summary":"A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>.","text_prefix":false,"timestamp":1545820723,"version":"0.0.8"},"r-sparseinv":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=sparseinv","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparseinv/sparseinv_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/sparseinv_0.1.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Creates a wrapper for the 'SuiteSparse' routines  that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a  sparse inverse subset. Some helper functions are also implemented.  Support for spam matrices is currently limited and will be implemented  in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016>  and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the  application of these equations to statistics.","text_prefix":false,"timestamp":1784608936,"version":"0.1.4"},"r-sparselda":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.imm.dtu.dk/~lhc, https://github.com/topepo/sparselda","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparseLDA/sparseLDA_0.1-9.tar.gz","https://cran.r-project.org/src/contrib/sparseLDA_0.1-9.tar.gz"],"subdirs":["noarch"],"summary":"Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.","text_prefix":false,"timestamp":1758704814,"version":"0.1_9"},"r-sparsem":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.econ.uiuc.edu/~roger/research/sparse/sparse.html","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SparseM/SparseM_1.84-2.tar.gz","https://cran.r-project.org/src/contrib/SparseM_1.84-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Some basic linear algebra functionality for sparse matrices is provided:  including Cholesky decomposition and backsolving as well as  standard R subsetting and Kronecker products.","text_prefix":false,"timestamp":1603187921,"version":"1.84_2"},"r-sparsemvn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/braunm/sparseMVN/","home":"https://braunm.github.io/sparseMVN/","license":"MPL-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparseMVN/sparseMVN_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/sparseMVN_0.2.2.tar.gz"],"subdirs":["noarch"],"summary":"Computes multivariate normal (MVN) densities, and samples from MVN distributions, when the covariance or precision matrix is sparse.","text_prefix":false,"timestamp":1621902425,"version":"0.2.2"},"r-sparsepca":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/erichson/spca","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparsepca/sparsepca_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/sparsepca_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>.","text_prefix":false,"timestamp":1757880287,"version":"0.1.2"},"r-sparsepp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://cran.r-project.org/package=sparsepp","license":"BSD_3_clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparsepp/sparsepp_1.22.tar.gz","https://cran.r-project.org/src/contrib/sparsepp_1.22.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides interface to 'sparsepp' - fast, memory efficient hash map.  It is derived from Google's excellent 'sparsehash' implementation. We believe 'sparsepp' provides an unparalleled combination of performance and memory usage,  and will outperform your compiler's unordered_map on both counts.  Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater  memory usage (especially when the final size of the map is not known in advance).","text_prefix":false,"timestamp":1532517181,"version":"1.22"},"r-sparsesvd":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/lucasmaystre/svdlibc","home":"http://wordspace.r-forge.r-project.org/","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparsesvd/sparsesvd_0.2-3.tar.gz","https://cran.r-project.org/src/contrib/sparsesvd_0.2-3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix. Currently, only sparse real matrices in Matrix package format are supported.","text_prefix":false,"timestamp":1665503867,"version":"0.2_3"},"r-sparsevctrs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/sparsevctrs, https://r-lib.github.io/sparsevctrs/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sparsevctrs/sparsevctrs_0.3.6.tar.gz","https://cran.r-project.org/src/contrib/sparsevctrs_0.3.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. Also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors.","text_prefix":false,"timestamp":1769711242,"version":"0.3.6"},"r-spatest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SPAtest","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SPAtest/SPAtest_3.1.2.tar.gz","https://cran.r-project.org/src/contrib/SPAtest_3.1.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check <https://github.com/leeshawn/SPAtest>.","text_prefix":false,"timestamp":1541449494,"version":"3.1.2"},"r-spatial":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.stats.ox.ac.uk/pub/MASS4/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatial/spatial_7.3-19.tar.gz","https://cran.r-project.org/src/contrib/spatial_7.3-19.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functions for kriging and point pattern analysis.","text_prefix":false,"timestamp":1665283190,"version":"7.3_19"},"r-spatialeco":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jeffreyevans/spatialEco","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatialEco/spatialEco_2.0-5.tar.gz","https://cran.r-project.org/src/contrib/spatialEco_2.0-5.tar.gz"],"subdirs":["noarch"],"summary":"Utilities to support spatial data manipulation, query, sampling and modelling. Functions include models for species population density, download utilities for climate and global deforestation spatial products, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, polygon and point-distance landscape metrics, auto-logistic model, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.","text_prefix":false,"timestamp":1606956579,"version":"2.0_5"},"r-spatialextremes":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://spatialextremes.r-forge.r-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SpatialExtremes/SpatialExtremes_2.1-0.tar.gz","https://cran.r-project.org/src/contrib/SpatialExtremes_2.1-0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.","text_prefix":false,"timestamp":1563602869,"version":"2.1_0"},"r-spatialpack":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://spatialpack.mat.utfsm.cl","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SpatialPack/SpatialPack_0.4-1.tar.gz","https://cran.r-project.org/src/contrib/SpatialPack_0.4-1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. SpatialPack gives methods to complement methodologies that are available in geoR for one spatial process.","text_prefix":false,"timestamp":1592760751,"version":"0.4_1"},"r-spatialreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/r-spatial/spatialreg/","home":"https://r-spatial.github.io/spatialreg/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatialreg/spatialreg_1.4-3.tar.gz","https://cran.r-project.org/src/contrib/spatialreg_1.4-3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep', 'sphet' and 'spse'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. 'spatialreg' >= 1.1-* correspond to 'spdep' >= 1.1-1, in which the model fitting functions are deprecated and pass through to 'spatialreg', but will mask those in 'spatialreg'. From versions 1.2-*, the functions will be made defunct in 'spdep'.","text_prefix":false,"timestamp":1665671482,"version":"1.4_3"},"r-spatialsample":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidymodels/spatialsample","home":"https://spatialsample.tidymodels.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatialsample/spatialsample_0.6.1.tar.gz","https://cran.r-project.org/src/contrib/spatialsample_0.6.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions and classes for spatial resampling to use with the 'rsample' package, such as spatial cross-validation (Brenning, 2012) <doi:10.1109/IGARSS.2012.6352393>. The scope of 'rsample' and 'spatialsample' is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by 'spatialsample' do not contain much overhead in memory.","text_prefix":false,"timestamp":1766677988,"version":"0.6.1"},"r-spatialtools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SpatialTools","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SpatialTools/SpatialTools_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/SpatialTools_1.0.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.","text_prefix":false,"timestamp":1758703151,"version":"1.0.5"},"r-spatialvx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.ral.ucar.edu/projects/icp/SpatialVx","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SpatialVx/SpatialVx_1.0-3.tar.gz","https://cran.r-project.org/src/contrib/SpatialVx_1.0-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Spatial forecast verification arose from verifying high-resolution forecasts, where coarser-resolution models generally are favored even when a human forecaster finds the higher-resolution model to be considerably better.  Most newly proposed methods, which largely come from image analysis, computer vision, and similar, are available, with more on the way.","text_prefix":false,"timestamp":1533745843,"version":"1.0_3"},"r-spatialwidget":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/symbolixau/spatialwidget","home":"https://symbolixau.github.io/spatialwidget/articles/spatialwidget.html","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatialwidget/spatialwidget_0.2.6.tar.gz","https://cran.r-project.org/src/contrib/spatialwidget_0.2.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Many packages use 'htmlwidgets' <https://CRAN.R-project.org/package=htmlwidgets> for interactive plotting of spatial data. This package provides functions for converting R objects, such as simple features, into structures suitable for use in 'htmlwidgets' mapping libraries.","text_prefix":false,"timestamp":1588569238,"version":"0.2.6"},"r-spatstat":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/spatstat/spatstat","home":"http://www.spatstat.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat/spatstat_3.6-2.tar.gz","https://cran.r-project.org/src/contrib/spatstat_3.6-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.","text_prefix":false,"timestamp":1541613411,"version":"3.6_2"},"r-spatstat.core":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.core/spatstat.core_2.4-4.tar.gz","https://cran.r-project.org/src/contrib/spatstat.core_2.4-4.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"This is a subset of the original 'spatstat' package, containing all of the user-level code from 'spatstat', except for the code for linear networks.","text_prefix":false,"timestamp":1635873947,"version":"2.4_4"},"r-spatstat.data":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/spatstat/spatstat.data","home":"http://www.spatstat.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.data/spatstat.data_3.1-9.tar.gz","https://cran.r-project.org/src/contrib/spatstat.data_3.1-9.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains all the datasets for the 'spatstat' family of packages.","text_prefix":false,"timestamp":0,"version":"3.1_9"},"r-spatstat.explore":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/spatstat/spatstat.explore","home":"http://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.explore/spatstat.explore_3.8-2.tar.gz","https://cran.r-project.org/src/contrib/spatstat.explore_3.8-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.","text_prefix":false,"timestamp":1668022814,"version":"3.8_2"},"r-spatstat.geom":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/spatstat/spatstat.geom","home":"http://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.geom/spatstat.geom_3.8-2.tar.gz","https://cran.r-project.org/src/contrib/spatstat.geom_3.8-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)","text_prefix":false,"timestamp":1666693675,"version":"3.8_2"},"r-spatstat.linnet":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/spatstat/spatstat.linnet","home":"http://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.linnet/spatstat.linnet_3.5-3.tar.gz","https://cran.r-project.org/src/contrib/spatstat.linnet_3.5-3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.","text_prefix":false,"timestamp":1668079739,"version":"3.5_3"},"r-spatstat.model":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/spatstat/spatstat.model","home":"http://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.model/spatstat.model_3.7-2.tar.gz","https://cran.r-project.org/src/contrib/spatstat.model_3.7-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.","text_prefix":false,"timestamp":1668066657,"version":"3.7_2"},"r-spatstat.random":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/spatstat/spatstat.random","home":"https://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.random/spatstat.random_3.5-1.tar.gz","https://cran.r-project.org/src/contrib/spatstat.random_3.5-1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering),  simulated realisations of random point processes (simple sequential inhibition, Matern inhibition models, Matern cluster process, Neyman-Scott cluster processes, log-Gaussian Cox processes, product shot noise cluster processes) and simulation of Gibbs point processes (Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.","text_prefix":false,"timestamp":1667493883,"version":"3.5_1"},"r-spatstat.sparse":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.sparse/spatstat.sparse_3.2-0.tar.gz","https://cran.r-project.org/src/contrib/spatstat.sparse_3.2-0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms.","text_prefix":false,"timestamp":1666376387,"version":"3.2_0"},"r-spatstat.univar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/spatstat/spatstat.univar","home":"http://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spatstat.univar/spatstat.univar_3.2-0.tar.gz","https://cran.r-project.org/src/contrib/spatstat.univar_3.2-0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, kernel properties, quantiles and integration.","text_prefix":false,"timestamp":1779180092,"version":"3.2_0"},"r-spatstat.utils":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://spatstat.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/spatstat.utils/spatstat.utils_3.2-5.tar.gz","https://cloud.r-project.org/src/contrib/spatstat.utils_3.2-5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Contains utility functions for the 'spatstat' family of packages which may also be useful for other purposes.","text_prefix":false,"timestamp":1623654026,"version":"3.2_5"},"r-spbayes":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.finley-lab.com","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spBayes/spBayes_0.4-9.tar.gz","https://cran.r-project.org/src/contrib/spBayes_0.4-9.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley, Banerjee, and Cook (2014) <doi:10.1111/2041-210X.12189>.","text_prefix":false,"timestamp":1632170068,"version":"0.4_9"},"r-spdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/Nowosad/spData/","home":"https://nowosad.github.io/spData/","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spData/spData_2.3.5.tar.gz","https://cran.r-project.org/src/contrib/spData_2.3.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps', it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.","text_prefix":false,"timestamp":1538916270,"version":"2.3.5"},"r-spdep":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-spatial/spdep/, https://r-spatial.github.io/spdep/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spdep/spdep_1.4-2.tar.gz","https://cran.r-project.org/src/contrib/spdep_1.4-2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assun\u00e7\u00e3o/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured join count statistics, 'APLE' ('Li 'et al.' ) <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I', 'Gearys C' ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and 'Getis/Ord' G ('Ord' and 'Getis' 1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>, 'saddlepoint' approximations ('Tiefelsdorf' 2002) <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') <doi:10.1007/s00168-011-0492-y>. The implementation of most of the measures is described in 'Bivand' and 'Wong' (2018) <doi:10.1007/s11749-018-0599-x>. From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'.","text_prefix":false,"timestamp":1533343058,"version":"1.4_2"},"r-spdl":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/eddelbuettel/spdl","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/spdl/spdl_0.0.5.tar.gz","https://cran.r-project.org/src/contrib/spdl_0.0.5.tar.gz"],"subdirs":["noarch"],"summary":"Logging functions in 'RcppSpdlog' provide access to the logging functionality from the 'spdlog' 'C++' library. This package offers shorter convenience wrappers for the 'R' functions which match the 'C++' functions, namely via, say, 'spdl::debug()' at the debug level. The actual formatting is done by the 'fmt::format()' function from the 'fmtlib' library (that is also 'std::format()' in 'C++20' or later).","text_prefix":false,"timestamp":1758074933,"version":"0.0.5"},"r-specr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/masurp/specr","home":"https://masurp.github.io/specr/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/specr/specr_1.1.0.tar.gz","https://cloud.r-project.org/src/contrib/specr_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2020, <doi: 10.1038/s41562-020-0912-z>) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi: 10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot all specifications.","text_prefix":false,"timestamp":1792167346,"version":"1.1.0"},"r-specsverification":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SpecsVerification","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/SpecsVerification/SpecsVerification_0.5-4.tar.gz","https://cloud.r-project.org/src/contrib/SpecsVerification_0.5-4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A collection of forecast verification routines developed for the SPECS FP7 project. 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Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint  (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional  delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. 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This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.","text_prefix":false,"timestamp":1758500669,"version":"2.1_0"},"r-ssh":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ropensci/ssh","doc_url":"https://docs.ropensci.org/ssh","home":"https://ropensci.r-universe.dev/ssh","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/ssh/ssh_0.9.4.tar.gz","https://cran.r-project.org/src/contrib/ssh_0.9.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client.","text_prefix":false,"timestamp":1605099593,"version":"0.9.4"},"r-ssoap":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sckott/SSOAP","license":"GPL-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/sckott/SSOAP/archive/master.zip","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Client-side SOAP access for S","text_prefix":false,"timestamp":0,"version":"0.91"},"r-st":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://strimmerlab.org/software/st/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/st/st_1.2.7.tar.gz","https://cran.r-project.org/src/contrib/st_1.2.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implements the \"shrinkage t\" statistic  introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate of the \"correlation-adjusted t-score\" (CAT score) described in Zuber and Strimmer (2009).  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See github for Journal article.","text_prefix":false,"timestamp":1645075504,"version":"1.1.7"},"r-stabledist":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.rmetrics.org, https://r-forge.r-project.org/scm/viewvc.php/pkg/stabledist/?root=rmetrics","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stabledist/stabledist_0.7-2.tar.gz","https://cran.r-project.org/src/contrib/stabledist_0.7-2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Density, Probability and Quantile functions, and random number generation for (skew) stable distributions, using the parametrizations of Nolan.","text_prefix":false,"timestamp":1539720682,"version":"0.7_2"},"r-stabm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://bommert.github.io/stabm/, https://github.com/bommert/stabm","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stabm/stabm_1.2.2.tar.gz","https://cran.r-project.org/src/contrib/stabm_1.2.2.tar.gz"],"subdirs":["noarch"],"summary":"An implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available, see Bommert (2020) <doi:10.17877/DE290R-21906>.","text_prefix":false,"timestamp":1758011205,"version":"1.2.2"},"r-stabs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/hofnerb/stabs","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stabs/stabs_0.7-1.tar.gz","https://cran.r-project.org/src/contrib/stabs_0.7-1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>)  and complementary pairs stability selection with improved error bounds  (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are implemented. The package can be combined with arbitrary user specified variable selection approaches.","text_prefix":false,"timestamp":1536586620,"version":"0.7_1"},"r-stacks":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidymodels/stacks","home":"https://stacks.tidymodels.org/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stacks/stacks_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/stacks_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking.","text_prefix":false,"timestamp":1641447559,"version":"1.1.1"},"r-stampp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=StAMPP","license":"GPL-3-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/StAMPP/StAMPP_1.6.3.tar.gz","https://cran.r-project.org/src/contrib/StAMPP_1.6.3.tar.gz"],"subdirs":["noarch"],"summary":"Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects allowing integration with other packages such adegenet. Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance.","text_prefix":false,"timestamp":1622158944,"version":"1.6.3"},"r-stanheaders":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/stan-dev/rstan","home":"http://mc-stan.org/","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/StanHeaders/StanHeaders_2.39.1.tar.gz","https://cloud.r-project.org/src/contrib/StanHeaders_2.39.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is only useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.","text_prefix":false,"timestamp":1591718568,"version":"2.39.1"},"r-stanova":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bayesstuff/stanova","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://github.com/bayesstuff/stanova/archive/refs/tags/first.tar.gz"],"subdirs":["noarch"],"summary":"More relevant and interpretable summary for Bayesian models.","text_prefix":false,"timestamp":1766286599,"version":"0.3.0"},"r-stapler":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/muschellij2/stapler","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stapler/stapler_0.8.0.tar.gz","https://cran.r-project.org/src/contrib/stapler_0.8.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An implementation of Simultaneous Truth and  Performance Level Estimation (STAPLE) <doi:10.1109/TMI.2004.828354>.  This method is used when there are multiple raters for an object, typically an image, and this method fuses these ratings into one rating.  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It also makes it easier to share specific startup settings by simply copying a file to a directory.","text_prefix":false,"timestamp":1667237459,"version":"0.23.0"},"r-startupmsg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=startupmsg","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/startupmsg/startupmsg_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/startupmsg_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides utilities to create or suppress start-up messages.","text_prefix":false,"timestamp":1545820719,"version":"1.0.0"},"r-statar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/matthieugomez/statar","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/statar/statar_0.7.7.tar.gz","https://cran.r-project.org/src/contrib/statar_0.7.7.tar.gz"],"subdirs":["noarch"],"summary":"A set of tools inspired by 'Stata' to explore data.frames ('summarize', 'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).","text_prefix":false,"timestamp":1622065920,"version":"0.7.7"},"r-statcanr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/warint/statcanR/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/statcanR/statcanR_0.3.9.tar.gz","https://cran.r-project.org/src/contrib/statcanR_0.3.9.tar.gz"],"subdirs":["noarch"],"summary":"An easy connection with R to Statistics Canada's Web Data Service. Open economic data (formerly known as CANSIM tables, now identified by Product IDs (PID)) are accessible as a data frame, directly in the user's R environment. Warin, Le Duc (2019) <doi:10.6084/m9.figshare.10544735>.","text_prefix":false,"timestamp":1784974159,"version":"0.3.9"},"r-statcheck":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=statcheck","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/statcheck/statcheck_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/statcheck_1.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Extract statistics from articles and recompute p values.","text_prefix":false,"timestamp":1758249060,"version":"1.5.0"},"r-statebins":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://gitlab.com/hrbrmstr/statebins","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/statebins/statebins_1.4.0.tar.gz","https://cran.r-project.org/src/contrib/statebins_1.4.0.tar.gz"],"subdirs":["noarch"],"summary":"The 'cartogram' heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on \"The states most threatened by trade\" (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). \"State bins\" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.","text_prefix":false,"timestamp":1758355034,"version":"1.4.0"},"r-statip":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/paulponcet/statip","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/statip/statip_0.2.3.tar.gz","https://cran.r-project.org/src/contrib/statip_0.2.3.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"A collection of miscellaneous statistical functions for  probability distributions: dbern(), pbern(), qbern(), rbern() for  the Bernoulli distribution, and distr2name(), name2distr() for  distribution names;  probability density estimation: densityfun();  most frequent value estimation: mfv(), mfv1();  calculation of the Hellinger distance: hellinger();  use of classical kernels: kernelfun(), kernel_properties();  univariate piecewise-constant regression: picor().","text_prefix":false,"timestamp":1568968253,"version":"0.2.3"},"r-statmod":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=statmod","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/statmod/statmod_1.5.2.tar.gz","https://cran.r-project.org/src/contrib/statmod_1.5.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A collection of algorithms and functions to aid statistical modeling. Includes growth curve comparisons, limiting dilution analysis (aka ELDA), mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. Includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families.","text_prefix":false,"timestamp":1578661647,"version":"1.5.2"},"r-statnet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://statnet.org","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/statnet/statnet_2019.6.tar.gz","https://cran.r-project.org/src/contrib/statnet_2019.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Statnet is a collection of packages for statistical network analysis that are  designed to work together because they share common data representations and 'API'  design.  They provide an integrated set of tools for the representation,  visualization, analysis, and simulation of many different forms of network data.   This package is designed to make it easy to install and load the  key 'statnet' packages in a single step.  Learn more about 'statnet'  at <http://www.statnet.org>.  For an introduction to functions in this package,  type help(package='statnet').","text_prefix":false,"timestamp":1530083164,"version":"2019.6"},"r-statnet.common":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.statnet.org","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/statnet.common/statnet.common_4.13.0.tar.gz","https://cran.r-project.org/src/contrib/statnet.common_4.13.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.","text_prefix":false,"timestamp":1557840737,"version":"4.13.0"},"r-statsexpressions":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/IndrajeetPatil/statsExpressions","home":"https://indrajeetpatil.github.io/statsExpressions/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/statsExpressions/statsExpressions_2.2.0.tar.gz","https://cloud.r-project.org/src/contrib/statsExpressions_2.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.","text_prefix":false,"timestamp":1581700482,"version":"2.2.0"},"r-steadyica":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=steadyICA","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/steadyICA/steadyICA_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/steadyICA_1.0.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions related to multivariate measures of independence and ICA: -estimate independent components by minimizing distance covariance; -conduct a test of mutual independence based on distance covariance; -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons); -order indepedent components by skewness; -match independent components from multiple estimates; -other functions useful in ICA.","text_prefix":false,"timestamp":1758065125,"version":"1.0.1"},"r-sticky":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/decisionpatterns/sticky http://decisionpatterns.com","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sticky/sticky_0.5.6.1.tar.gz","https://cran.r-project.org/src/contrib/sticky_0.5.6.1.tar.gz"],"subdirs":["noarch"],"summary":"In base R, object attributes are lost when objects are modified by common data operations such as subset, filter, slice, append, extract etc. This packages allows objects to be marked as 'sticky' and have attributes persisted during these operations or when inserted into or extracted from list-like or table-like objects.","text_prefix":false,"timestamp":1757848299,"version":"0.5.6.1"},"r-stinepack":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=stinepack","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stinepack/stinepack_1.5.tar.gz","https://cran.r-project.org/src/contrib/stinepack_1.5.tar.gz"],"subdirs":["noarch"],"summary":"A consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.","text_prefix":false,"timestamp":1758604658,"version":"1.5"},"r-stm":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://structuraltopicmodel.com","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stm/stm_1.3.8.tar.gz","https://cran.r-project.org/src/contrib/stm_1.3.8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions. Methods developed in Roberts et al (2014) <doi:10.1111/ajps.12103> and Roberts et al (2016) <doi:10.1080/01621459.2016.1141684>. Vignette is Roberts et al (2019) <doi:10.18637/jss.v091.i02>.","text_prefix":false,"timestamp":1766461328,"version":"1.3.8"},"r-stochvol":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/gregorkastner/stochvol","home":"https://gregorkastner.github.io/stochvol/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stochvol/stochvol_3.2.9.tar.gz","https://cran.r-project.org/src/contrib/stochvol_3.2.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Fr\u00fchwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.","text_prefix":false,"timestamp":1772868407,"version":"3.2.9"},"r-stopwords":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/davnn/stopwords","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stopwords/stopwords_2.3.tar.gz","https://cran.r-project.org/src/contrib/stopwords_2.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides multiple sources of stopwords, for use in text analysis and natural language processing.","text_prefix":false,"timestamp":1536482264,"version":"2.3"},"r-stormr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://umr-amap.github.io/StormR/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/StormR/StormR_0.3.0.tar.gz","https://cran.r-project.org/src/contrib/StormR_0.3.0.tar.gz"],"subdirs":["linux-64","osx-64"],"summary":"Set of functions to quantify and map the behaviour of winds generated by tropical storms and cyclones in space and time. It includes functions to compute and analyze fields such as the maximum sustained wind field, power dissipation index and duration of exposure to winds above a given threshold. It also includes functions to map the trajectories as well as characteristics of the storms.","text_prefix":false,"timestamp":1784190209,"version":"0.3.0"},"r-storr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/richfitz/storr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/storr/storr_1.2.6.tar.gz","https://cran.r-project.org/src/contrib/storr_1.2.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Creates and manages simple key-value stores.  These can use a variety of approaches for storing the data.  This package implements the base methods and support for file system, in-memory and DBI-based database stores.","text_prefix":false,"timestamp":1539977595,"version":"1.2.6"},"r-stpphawkes":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=stpphawkes","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/stpphawkes/stpphawkes_0.2.4.tar.gz","https://cloud.r-project.org/src/contrib/stpphawkes_0.2.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Estimation of model parameters for marked Hawkes process. Accounts for missing data in the estimation of the parameters. Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).","text_prefix":false,"timestamp":1790161423,"version":"0.2.4"},"r-stratifyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=stratifyR","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/stratifyR/stratifyR_2.0-1.tar.gz","https://cloud.r-project.org/src/contrib/stratifyR_2.0-1.tar.gz"],"subdirs":["noarch"],"summary":"The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) <doi:10.1177/0008068320020518> and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674> in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.","text_prefix":false,"timestamp":1789458793,"version":"2.0_1"},"r-strawr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/aidenlab/straw/tree/master/R","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/strawr/strawr_0.0.92.tar.gz","https://cran.r-project.org/src/contrib/strawr_0.0.92.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"API for fast data extraction for .hic files that provides programmatic access to the matrices. It doesn't store the pointer data for all the matrices, only the one queried, and currently we are only supporting matrices (not vectors).","text_prefix":false,"timestamp":1665663029,"version":"0.0.92"},"r-streamr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=streamR","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/streamR/streamR_0.4.5.tar.gz","https://cran.r-project.org/src/contrib/streamR_0.4.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions to access Twitter's filter, sample, and user streams, and to parse the output into data frames.","text_prefix":false,"timestamp":1758110789,"version":"0.4.5"},"r-strex":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/rorynolan/strex","doc_url":"https://rorynolan.github.io/strex/reference/index.html","home":"https://rorynolan.github.io/strex","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/strex/strex_2.1.0.tar.gz","https://cran.r-project.org/src/contrib/strex_2.1.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"There are some things that I wish were easier with the 'stringr' or 'stringi' packages. The foremost of these is the extraction of numbers from strings. 'stringr' and 'stringi' make you figure out the regular expression for yourself; 'strex' takes care of this for you. There are many other handy functionalities in 'strex'. Contributions to this package are encouraged: it is intended as a miscellany of string manipulation functions that cannot be found in 'stringi' or 'stringr'.","text_prefix":false,"timestamp":1556990898,"version":"2.1.0"},"r-stringdist":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/markvanderloo/stringdist","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stringdist/stringdist_0.9.17.tar.gz","https://cran.r-project.org/src/contrib/stringdist_0.9.17.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Implements an approximate string matching version of R's native 'match' function. Can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences. This package is built for speed and runs in parallel by using 'openMP'. An API for C or C++ is exposed as well.","text_prefix":false,"timestamp":1533361329,"version":"0.9.17"},"r-stringfish":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/traversc/stringfish","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stringfish/stringfish_0.19.2.tar.gz","https://cran.r-project.org/src/contrib/stringfish_0.19.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides an extendable and performant 'alt-string' implementation backed by 'C++' vectors and strings.","text_prefix":false,"timestamp":1638390871,"version":"0.19.2"},"r-stringi":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.gagolewski.com/software/stringi/ http://site.icu-project.org/ http://www.unicode.org/","license":"FOSS","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stringi/stringi_1.8.9.tar.gz","https://cran.r-project.org/src/contrib/stringi_1.8.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Fast, correct, consistent, portable, as well as convenient character string/text processing in every locale and any native encoding. Owing to the use of the 'ICU' (International Components for Unicode) library, the package provides 'R' users with platform-independent functions known to 'Java', 'Perl', 'Python', 'PHP', and 'Ruby' programmers. Available features include: pattern searching (e.g., with 'Java'-like regular expressions or the 'Unicode' collation algorithm), random string generation, case mapping, string transliteration, concatenation, Unicode normalization, date-time formatting and parsing, and many more.","text_prefix":false,"timestamp":1578732365,"version":"1.8.9"},"r-stringmagic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/lrberge/stringmagic","home":"https://lrberge.github.io/stringmagic/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stringmagic/stringmagic_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/stringmagic_1.3.0.tar.gz"],"subdirs":["linux-64","osx-64","osx-arm64","win-64"],"summary":"Performs complex string operations compactly and efficiently. Supports string interpolation jointly with over 50 string operations. Also enhances regular string functions (like grep() and co). See an introduction at <https://lrberge.github.io/stringmagic/>.","text_prefix":false,"timestamp":1785849126,"version":"1.3.0"},"r-stringr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidyverse/stringr","home":"https://stringr.tidyverse.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/stringr/stringr_1.6.0.tar.gz","https://cran.r-project.org/src/contrib/stringr_1.6.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A consistent, simple and easy to use set of wrappers around the fantastic 'stringi' package. All function and argument names (and positions) are consistent, all functions deal with \"NA\"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.","text_prefix":false,"timestamp":0,"version":"1.6.0"},"r-striprtf":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/kota7/striprtf","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/striprtf/striprtf_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/striprtf_0.6.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Extracts plain text from RTF (Rich Text Format) file.","text_prefix":false,"timestamp":1621984012,"version":"0.6.0"},"r-strucchange":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=strucchange","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/strucchange/strucchange_1.6-0.tar.gz","https://cran.r-project.org/src/contrib/strucchange_1.6-0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Testing, monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data.","text_prefix":false,"timestamp":1536683290,"version":"1.6_0"},"r-styler":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/styler","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/styler/styler_1.11.0.tar.gz","https://cran.r-project.org/src/contrib/styler_1.11.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Pretty-prints R code without changing the user's formatting intent.","text_prefix":false,"timestamp":1530083235,"version":"1.11.0"},"r-subplex":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/kingaa/subplex/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/subplex/subplex_1.9.tar.gz","https://cran.r-project.org/src/contrib/subplex_1.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"The subplex algorithm for unconstrained optimization, developed by Tom Rowan <http://www.netlib.org/opt/subplex.tgz>.","text_prefix":false,"timestamp":1621688803,"version":"1.9"},"r-subselect":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=subselect","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/subselect/subselect_0.16.2.tar.gz","https://cloud.r-project.org/src/contrib/subselect_0.16.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette \"subselect\", which includes additional references.","text_prefix":false,"timestamp":1621995852,"version":"0.16.2"},"r-subspace":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=subspace","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/subspace/subspace_1.0.4.tar.gz","https://cran.r-project.org/src/contrib/subspace_1.0.4.tar.gz"],"subdirs":["noarch"],"summary":"An interface to 'OpenSubspace', an open source framework for evaluation and exploration of subspace clustering algorithms in WEKA  (see <http://dme.rwth-aachen.de/de/opensubspace> for more information).  Also performs visualization.","text_prefix":false,"timestamp":1758742270,"version":"1.0.4"},"r-suggests":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/owenjonesuob/suggests","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/suggests/suggests_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/suggests_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"By adding dependencies to the \"Suggests\" field of a package's DESCRIPTION file, and then declaring that they are needed within any dependent functionality, it is often possible to significantly reduce the number of \"hard\" dependencies required by a package. This package provides a minimal way to declare when a suggested package is needed.","text_prefix":false,"timestamp":1786352409,"version":"0.1.0"},"r-summarytools":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dcomtois/summarytools","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/summarytools/summarytools_1.1.5.tar.gz","https://cran.r-project.org/src/contrib/summarytools_1.1.5.tar.gz"],"subdirs":["noarch"],"summary":"Data frame summaries, cross-tabulations, weight-enabled frequency tables and common descriptive (univariate) statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.","text_prefix":false,"timestamp":1622049073,"version":"1.1.5"},"r-sunburstr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/timelyportfolio/sunburstR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sunburstR/sunburstR_2.1.8.tar.gz","https://cran.r-project.org/src/contrib/sunburstR_2.1.8.tar.gz"],"subdirs":["noarch"],"summary":"Make interactive 'd3.js' sequence sunburst diagrams in R with the convenience and infrastructure of an 'htmlwidget'.","text_prefix":false,"timestamp":1665661265,"version":"2.1.8"},"r-suncalc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/datastorm-open/suncalc","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/suncalc/suncalc_0.5.3.tar.gz","https://cran.r-project.org/src/contrib/suncalc_0.5.3.tar.gz"],"subdirs":["noarch"],"summary":"Get sun position, sunlight phases (times for sunrise, sunset, dusk, etc.), moon position and lunar phase for the given location and time. Most calculations are based on the formulas given in Astronomy Answers articles about position of the sun and the planets : <https://www.aa.quae.nl/en/reken/zonpositie.html>.","text_prefix":false,"timestamp":1784203454,"version":"0.5.3"},"r-suncalcmeeus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.r4photobiology.info/SunCalcMeeus/, https://github.com/aphalo/SunCalcMeeus","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SunCalcMeeus/SunCalcMeeus_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/SunCalcMeeus_0.1.3.tar.gz"],"subdirs":["noarch"],"summary":"Compute the position of the sun, and local solar time using Meeus' formulae. Compute day and/or night length using different twilight definitions or arbitrary sun elevation angles. This package is part of the 'r4photobiology' suite, Aphalo, P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>. Algorithms from Meeus (1998, ISBN:0943396611).","text_prefix":false,"timestamp":1758534297,"version":"0.1.3"},"r-supercell":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SuperCell","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SuperCell/SuperCell_1.1.tar.gz","https://cran.r-project.org/src/contrib/SuperCell_1.1.tar.gz"],"subdirs":["noarch"],"summary":"Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells. 'SuperCell' uses 'velocyto.R' <doi:10.1038/s41586-018-0414-6> <https://github.com/velocyto-team/velocyto.R> for RNA velocity and 'WeightedCluster' <doi:10.12682/lives.2296-1658.2013.24> <https://mephisto.unige.ch/weightedcluster/> for weighted clustering on metacells. We also recommend installing 'scater' Bioconductor package <doi:10.18129/B9.bioc.scater> <https://bioconductor.org/packages/release/bioc/html/scater.html>.","text_prefix":false,"timestamp":1773528126,"version":"1.1"},"r-superexacttest":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mw201608/SuperExactTest/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SuperExactTest/SuperExactTest_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/SuperExactTest_1.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.","text_prefix":false,"timestamp":1784312792,"version":"1.2.0"},"r-superheat":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=superheat","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/superheat/superheat_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/superheat_0.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"A system for generating extendable and customizable heatmaps for exploring complex datasets, including big data and data with multiple data types.","text_prefix":false,"timestamp":0,"version":"0.1.0"},"r-superlearner":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ecpolley/SuperLearner","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/SuperLearner/SuperLearner_2.0-41.tar.gz","https://cloud.r-project.org/src/contrib/SuperLearner_2.0-41.tar.gz"],"subdirs":["noarch"],"summary":"Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.","text_prefix":false,"timestamp":1588685672,"version":"2.0_41"},"r-supernova":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/UCLATALL/supernova","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/supernova/supernova_3.0.2.tar.gz","https://cran.r-project.org/src/contrib/supernova_3.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Produces ANOVA tables in the format used by Judd, McClelland, and Ryan (2017, ISBN: 978-1138819832) in their introductory textbook, Data Analysis. This includes proportional reduction in error and formatting to improve ease the transition between the book and R.","text_prefix":false,"timestamp":1772354533,"version":"3.0.2"},"r-superpc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www-stat.stanford.edu/~tibs/superpc, https://github.com/jedazard/superpc","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/superpc/superpc_1.12.tar.gz","https://cran.r-project.org/src/contrib/superpc_1.12.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Supervised principal components for regression and survival analsysis. Especially useful for high-dimnesional data, including microarray data.","text_prefix":false,"timestamp":1534360864,"version":"1.12"},"r-superpower":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://aaroncaldwell.us/SuperpowerBook/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Superpower/Superpower_0.2.4.1.tar.gz","https://cran.r-project.org/src/contrib/Superpower_0.2.4.1.tar.gz"],"subdirs":["noarch"],"summary":"Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). \"Simulation-Based Power Analysis for Factorial Analysis of Variance Designs\". <doi:10.1177/2515245920951503>.","text_prefix":false,"timestamp":1635706891,"version":"0.2.4.1"},"r-suppdists":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SuppDists","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SuppDists/SuppDists_1.1-9.9.tar.gz","https://cran.r-project.org/src/contrib/SuppDists_1.1-9.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Ten distributions supplementing those built into R. Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho, maximum F ratio, the Pearson product moment correlation coefficient, Johnson distributions, normal scores and generalized hypergeometric distributions. In addition two random number generators of George Marsaglia are included.","text_prefix":false,"timestamp":1563426119,"version":"1.1_9.9"},"r-support":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=support","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/support/support_0.1.7.tar.gz","https://cran.r-project.org/src/contrib/support_0.1.7.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The 'support' package provides the functions sp() and sp_seq() for computing the support points in Mak and Joseph (2018) <DOI:10.1214/17-AOS1629>. Support points can be used as a representative sample of a desired distribution, or a representative reduction of a big dataset (e.g., an \"optimal\" thinning of Markov-chain Monte Carlo sample chains). This work was supported by USARO grant W911NF-14-1-0024 and NSF DMS grant 1712642.","text_prefix":false,"timestamp":1542784187,"version":"0.1.7"},"r-survauc":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=survAUC","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survAUC/survAUC_1.4-0.tar.gz","https://cran.r-project.org/src/contrib/survAUC_1.4-0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The package provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data.","text_prefix":false,"timestamp":1666019438,"version":"1.4_0"},"r-survc1":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=survC1","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survC1/survC1_1.0-3.tar.gz","https://cran.r-project.org/src/contrib/survC1_1.0-3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) <doi:10.1002/sim.4154>. Inference for the difference in C between two competing prediction models is also implemented.","text_prefix":false,"timestamp":1758181634,"version":"1.0_3"},"r-surveillance":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://surveillance.R-Forge.R-project.org/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/surveillance/surveillance_1.26.1.tar.gz","https://cran.r-project.org/src/contrib/surveillance_1.26.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of H\u00f6hle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by H\u00f6hle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.","text_prefix":false,"timestamp":1668624366,"version":"1.26.1"},"r-survey":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://r-survey.r-forge.r-project.org/survey/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survey/survey_4.5.tar.gz","https://cran.r-project.org/src/contrib/survey_4.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.","text_prefix":false,"timestamp":0,"version":"4.5"},"r-survidinri":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=survIDINRI","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survIDINRI/survIDINRI_1.1-2.tar.gz","https://cran.r-project.org/src/contrib/survIDINRI_1.1-2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).","text_prefix":false,"timestamp":1758687123,"version":"1.1_2"},"r-survival":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/therneau/survival","license":"LGPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/survival/survival_3.8-12.tar.gz","https://cloud.r-project.org/src/contrib/survival_3.8-12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Contains the core survival analysis routines, including definition of Surv objects,  Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.","text_prefix":false,"timestamp":1646180997,"version":"3.8_12"},"r-survival666":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=survival666","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survival666/survival666_0.5.tar.gz","https://cran.r-project.org/src/contrib/survival666_0.5.tar.gz"],"subdirs":["noarch"],"summary":"Functions can be used for batch survival analysis, but not only for it. Most importantly, it can verify any P-value calculated according to the gene expression level and eliminate the influence of co-expression genes.","text_prefix":false,"timestamp":1758825235,"version":"0.5"},"r-survivalanalysis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=survivalAnalysis","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survivalAnalysis/survivalAnalysis_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/survivalAnalysis_0.4.0.tar.gz"],"subdirs":["noarch"],"summary":"A high-level interface to perform survival analysis,  including Kaplan-Meier analysis and log-rank tests and Cox regression. Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting. Builds upon the 'survminer' package for Kaplan-Meier plots and provides a customizable implementation for forest plots. Kaplan & Meier (1958) <doi:10.1080/01621459.1958.10501452> Cox (1972) <JSTOR:2985181> Peto & Peto (1972) <doi:10.2307/2344317>.","text_prefix":false,"timestamp":1622210200,"version":"0.4.0"},"r-survivalmodels":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/RaphaelS1/survivalmodels/","home":"https://github.com/RaphaelS1/survivalmodels/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survivalmodels/survivalmodels_0.1.191.tar.gz","https://cran.r-project.org/src/contrib/survivalmodels_0.1.191.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox>.","text_prefix":false,"timestamp":1758589143,"version":"0.1.191"},"r-survivalroc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=survivalROC","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survivalROC/survivalROC_1.0.3.1.tar.gz","https://cran.r-project.org/src/contrib/survivalROC_1.0.3.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344)","text_prefix":false,"timestamp":1665045072,"version":"1.0.3.1"},"r-survivalsvm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/imbs-hl/survivalsvm","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survivalsvm/survivalsvm_0.0.6.tar.gz","https://cran.r-project.org/src/contrib/survivalsvm_0.0.6.tar.gz"],"subdirs":["noarch"],"summary":"Performs support vectors analysis for data sets with survival outcome. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in the same model.","text_prefix":false,"timestamp":1758565601,"version":"0.0.6"},"r-survmetrics":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/skyee1/SurvMetrics","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SurvMetrics/SurvMetrics_0.5.1.tar.gz","https://cran.r-project.org/src/contrib/SurvMetrics_0.5.1.tar.gz"],"subdirs":["noarch"],"summary":"An implementation of popular evaluation metrics that are commonly used in survival prediction including Concordance Index, Brier Score, Integrated Brier Score, Integrated Square Error, Integrated Absolute Error and Mean Absolute Error. For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) <doi:10.1214/08-AOAS169>) and (Moradian H, Larocque D and Bellavance F (2017) <doi:10.1007/s10985-016-9372-1>) for different evaluation metrics.","text_prefix":false,"timestamp":1656505893,"version":"0.5.1"},"r-survminer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.sthda.com/english/rpkgs/survminer/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survminer/survminer_0.5.2.tar.gz","https://cran.r-project.org/src/contrib/survminer_0.5.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains the function 'ggsurvplot()' for drawing easily beautiful and 'ready-to-publish' survival curves with the 'number at risk' table and 'censoring count plot'. Other functions are also available to plot adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.","text_prefix":false,"timestamp":0,"version":"0.5.2"},"r-survmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=survMisc","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survMisc/survMisc_0.5.6.tar.gz","https://cran.r-project.org/src/contrib/survMisc_0.5.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of functions to help in the analysis of right-censored survival data. These extend the methods available in package:survival.","text_prefix":false,"timestamp":1537182923,"version":"0.5.6"},"r-survpresmooth":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=survPresmooth","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survPresmooth/survPresmooth_1.1-12.tar.gz","https://cran.r-project.org/src/contrib/survPresmooth_1.1-12.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data.","text_prefix":false,"timestamp":1621732537,"version":"1.1_12"},"r-survtmle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/benkeser/survtmle","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survtmle/survtmle_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/survtmle_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"Targeted estimates of marginal cumulative incidence in survival settings with and without competing risks, including estimators that respect bounds (Benkeser, Carone, and Gilbert. Statistics in Medicine, 2017. <doi:10.1002/sim.7337>).","text_prefix":false,"timestamp":1758344094,"version":"1.1.1"},"r-survxai":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://mi2datalab.github.io/survxai/","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/survxai/survxai_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/survxai_0.2.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Survival models may have very different structures. This package contains functions  for creating a unified representation of a survival models, which can be further processed by various  survival explainers. Tools implemented in 'survxai' help to understand how input variables are used in  the model and what impact do they have on the final model prediction. Currently, four explanation methods are implemented.  We can divide them into two groups: local and global.","text_prefix":false,"timestamp":1538299436,"version":"0.2.2"},"r-susier":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/stephenslab/susieR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/susieR/susieR_0.14.2.tar.gz","https://cran.r-project.org/src/contrib/susieR_0.14.2.tar.gz"],"subdirs":["noarch"],"summary":"Implements methods for variable selection in linear regression based on the \"Sum of Single Effects\" (SuSiE) model, as described in Wang et al (2020) <DOI:10.1101/501114>. These methods provide simple summaries, called \"Credible Sets\", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called \"Iterative Bayesian Stepwise Selection\" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables).","text_prefix":false,"timestamp":1636756872,"version":"0.14.2"},"r-svars":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=svars","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svars/svars_1.3.12.tar.gz","https://cran.r-project.org/src/contrib/svars_1.3.12.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>),  patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).","text_prefix":false,"timestamp":1667669977,"version":"1.3.12"},"r-svd":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://github.com/asl/svd","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svd/svd_0.5.8.tar.gz","https://cran.r-project.org/src/contrib/svd_0.5.8.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"R bindings to SVD and eigensolvers (PROPACK, nuTRLan).","text_prefix":false,"timestamp":1665014659,"version":"0.5.8"},"r-svdialogs":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SciViews/svDialogs, http://www.sciviews.org/SciViews-R","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svDialogs/svDialogs_1.1.2.tar.gz","https://cran.r-project.org/src/contrib/svDialogs_1.1.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Rapidly construct standard dialog boxes for your GUI, including  message boxes, input boxes, list, file or directory selection, ... In case R cannot display GUI dialog boxes, a simpler command line version of these interactive elements is also provided as fallback solution.","text_prefix":false,"timestamp":0,"version":"1.1.2"},"r-svglite":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-lib/svglite","doc_url":"https://svglite.r-lib.org/reference/index.html","home":"https://svglite.r-lib.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svglite/svglite_2.2.2.tar.gz","https://cran.r-project.org/src/contrib/svglite_2.2.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"A graphics device for R that produces 'Scalable Vector Graphics'. 'svglite' is a fork of the older 'RSvgDevice' package.","text_prefix":false,"timestamp":1568197849,"version":"2.2.2"},"r-svgpanzoom":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/timelyportfolio/svgPanZoom","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svgPanZoom/svgPanZoom_0.3.4.tar.gz","https://cran.r-project.org/src/contrib/svgPanZoom_0.3.4.tar.gz"],"subdirs":["noarch"],"summary":"This 'htmlwidget' provides pan and zoom interactivity to R graphics, including 'base', 'lattice', and 'ggplot2'. The interactivity is provided through the 'svg-pan-zoom.js' library. Various options to the widget can tailor the pan and zoom experience to nearly any user desire.","text_prefix":false,"timestamp":1758683427,"version":"0.3.4"},"r-svgui":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SciViews/svGUI, http://www.sciviews.org/SciViews-R","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svGUI/svGUI_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/svGUI_1.0.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The SciViews svGUI package eases the management of Graphical User Interfaces (GUI) in R. It is independent from any particular GUI widgets (Tk, Gtk2, native, ...). It centralizes info about GUI elements currently used, and it dispatches GUI calls to the particular toolkits in use in function of the context (is R run at the terminal, within a Tk application, a HTML page?).","text_prefix":false,"timestamp":1539296103,"version":"1.0.2"},"r-svmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/SciViews/svMisc, http://www.sciviews.org/SciViews-R","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svMisc/svMisc_1.4.3.tar.gz","https://cran.r-project.org/src/contrib/svMisc_1.4.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Miscellaneous functions for SciViews or general use: manage a temporary environment attached to the search path for temporary variables you do not want to save() or load(), test if Aqua, Mac, Win, ... Show progress bar, etc.","text_prefix":false,"timestamp":0,"version":"1.4.3"},"r-svunit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.sciviews.org/SciViews-R","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svUnit/svUnit_1.0.8.tar.gz","https://cran.r-project.org/src/contrib/svUnit_1.0.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A complete unit test system and functions to implement its GUI part","text_prefix":false,"timestamp":1535633644,"version":"1.0.8"},"r-svyvgam":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=svyVGAM","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/svyVGAM/svyVGAM_1.3.tar.gz","https://cran.r-project.org/src/contrib/svyVGAM_1.3.tar.gz"],"subdirs":["noarch"],"summary":"Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package.","text_prefix":false,"timestamp":1768576591,"version":"1.3"},"r-swagger":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/swagger","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/swagger/swagger_5.32.1.tar.gz","https://cran.r-project.org/src/contrib/swagger_5.32.1.tar.gz"],"subdirs":["noarch"],"summary":"A collection of 'HTML', 'JavaScript', and 'CSS' assets that dynamically generate beautiful documentation from a 'Swagger' compliant API: <https://swagger.io/specification/>.","text_prefix":false,"timestamp":1588591596,"version":"5.32.1"},"r-sweater":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/gesistsa/sweater","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sweater/sweater_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/sweater_0.1.8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.","text_prefix":false,"timestamp":1767999113,"version":"0.1.8"},"r-sweep":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/business-science/sweep","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sweep/sweep_0.2.7.tar.gz","https://cran.r-project.org/src/contrib/sweep_0.2.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to \"tidy\" data frames with 'sw_sweep'.","text_prefix":false,"timestamp":1588742000,"version":"0.2.7"},"r-sweidnumbr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rOpenGov/sweidnumbr/","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sweidnumbr/sweidnumbr_1.5.0.tar.gz","https://cran.r-project.org/src/contrib/sweidnumbr_1.5.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Structural handling of identity numbers used in the Swedish administration such as personal identity numbers ('personnummer') and organizational identity numbers ('organisationsnummer').","text_prefix":false,"timestamp":1537583454,"version":"1.5.0"},"r-swfscmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/EricArcher/swfscMisc","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/swfscMisc/swfscMisc_1.7.6.tar.gz","https://cloud.r-project.org/src/contrib/swfscMisc_1.7.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Collection of conversion, analytical, geodesic, mapping, and plotting functions. Used to support packages and code written by researchers at the Southwest Fisheries Science Center of the National Oceanic and Atmospheric Administration.","text_prefix":false,"timestamp":1541705692,"version":"1.7.6"},"r-sybil":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.cs.hhu.de/lehrstuehle-und-arbeitsgruppen/computational-cell-biology/software/sybil.html","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sybil/sybil_2.2.0.tar.gz","https://cran.r-project.org/src/contrib/sybil_2.2.0.tar.gz"],"subdirs":["noarch"],"summary":"This Systems Biology Package (Gelius-Dietrich et. al. (2012) <doi:10.1186/1752-0509-7-125>) implements algorithms for constraint based analyses of metabolic networks, e.g. flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on/off minimization (ROOM), robustness analysis and flux variability analysis. The package is easily extendable for additional algorithms. Most of the current LP/MILP solvers are supported via additional packages.","text_prefix":false,"timestamp":1621902204,"version":"2.2.0"},"r-sylly":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://reaktanz.de/?c=hacking&s=sylly","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sylly/sylly_0.1-7.tar.gz","https://cran.r-project.org/src/contrib/sylly_0.1-7.tar.gz"],"subdirs":["noarch"],"summary":"Provides the hyphenation algorithm used for 'TeX'/'LaTeX' and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the 'koRpus' package, but later released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos>); this package does not include any language specific data. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).","text_prefix":false,"timestamp":1588559660,"version":"0.1_7"},"r-sylly.en":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://reaktanz.de/?c=hacking&s=koRpus","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/sylly.en/sylly.en_0.1-4.tar.gz","https://cloud.r-project.org/src/contrib/sylly.en_0.1-4.tar.gz"],"subdirs":["noarch"],"summary":"Adds support for the English language to the 'sylly' package. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).","text_prefix":false,"timestamp":1790872144,"version":"0.1_4"},"r-symengine":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/symengine/symengine.R","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/symengine/symengine_0.2.14.tar.gz","https://cloud.r-project.org/src/contrib/symengine_0.2.14.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides an R interface to 'SymEngine' <https://github.com/symengine/>, a standalone 'C++' library for fast symbolic manipulation. The package has functionalities for symbolic computation like calculating exact mathematical expressions, solving systems of linear equations and code generation.","text_prefix":false,"timestamp":1665474580,"version":"0.2.14"},"r-symphony":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=symphony","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/symphony/symphony_0.1.3.tar.gz","https://cran.r-project.org/src/contrib/symphony_0.1.3.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.","text_prefix":false,"timestamp":1779958750,"version":"0.1.3"},"r-syncsa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=SYNCSA","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/SYNCSA/SYNCSA_1.3.5.tar.gz","https://cran.r-project.org/src/contrib/SYNCSA_1.3.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).","text_prefix":false,"timestamp":1756285526,"version":"1.3.5"},"r-synth":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/j-hai/Synth","home":"https://web.stanford.edu/~jhain/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Synth/Synth_1.1-10.tar.gz","https://cran.r-project.org/src/contrib/Synth_1.1-10.tar.gz"],"subdirs":["noarch"],"summary":"Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.","text_prefix":false,"timestamp":1783424526,"version":"1.1_10"},"r-synthpop":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.synthpop.org.uk/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/synthpop/synthpop_1.9-3.tar.gz","https://cloud.r-project.org/src/contrib/synthpop_1.9-3.tar.gz"],"subdirs":["noarch"],"summary":"A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>.","text_prefix":false,"timestamp":1640010273,"version":"1.9_3"},"r-sys":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jeroen/sys","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sys/sys_3.4.3.tar.gz","https://cran.r-project.org/src/contrib/sys_3.4.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Drop-in replacements for the base system2() function with fine control and consistent behavior across platforms. Supports clean interruption, timeout,  background tasks, and streaming STDIN / STDOUT / STDERR over binary or text  connections. Arguments on Windows automatically get encoded and quoted to work  on different locales.","text_prefix":false,"timestamp":1552834093,"version":"3.4.3"},"r-sysfonts":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/yixuan/sysfonts","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/sysfonts/sysfonts_0.8.9.tar.gz","https://cran.r-project.org/src/contrib/sysfonts_0.8.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Loading system fonts and Google Fonts <https://fonts.google.com/> into R, in order to support other packages such as 'R2SWF' and 'showtext'.","text_prefix":false,"timestamp":1627281855,"version":"0.8.9"},"r-systemfit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.systemfit.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/systemfit/systemfit_1.1-30.tar.gz","https://cran.r-project.org/src/contrib/systemfit_1.1-30.tar.gz"],"subdirs":["noarch"],"summary":"Econometric estimation of simultaneous systems of linear and nonlinear equations using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS) as suggested, e.g., by Zellner (1962) <doi:10.2307/2281644>, Zellner and Theil (1962) <doi:10.2307/1911287>, and Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.","text_prefix":false,"timestamp":1645542630,"version":"1.1_30"},"r-systemfonts":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-lib/systemfonts","home":"https://systemfonts.r-lib.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/systemfonts/systemfonts_1.3.2.tar.gz","https://cran.r-project.org/src/contrib/systemfonts_1.3.2.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides system native access to the font catalogue. As font handling varies between systems it is difficult to correctly locate  installed fonts across different operating systems. The 'systemfonts'  package provides bindings to the native libraries on Windows, macOS and  Linux for finding font files that can then be used further by e.g. graphic devices. The main use is intended to be from compiled code but 'systemfonts' also provides access from R.","text_prefix":false,"timestamp":1591693120,"version":"1.3.2"},"r-syuzhet":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mjockers/syuzhet","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/syuzhet/syuzhet_1.0.7.tar.gz","https://cran.r-project.org/src/contrib/syuzhet_1.0.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Extracts sentiment and sentiment-derived plot arcs from text using a variety of sentiment dictionaries conveniently packaged for consumption by R users.  Implemented dictionaries include \"syuzhet\" (default) developed in the Nebraska Literary Lab \"afinn\" developed by Finn {\\AA}rup Nielsen, \"bing\" developed by Minqing Hu and Bing Liu, and \"nrc\" developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the \"get_sentiment\" function.  The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization.","text_prefix":false,"timestamp":1538317287,"version":"1.0.7"},"r-table1":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/benjaminrich/table1","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/table1/table1_1.5.1.tar.gz","https://cran.r-project.org/src/contrib/table1_1.5.1.tar.gz"],"subdirs":["noarch"],"summary":"Create HTML tables of descriptive statistics, as one would expect to see as the first table (i.e. \"Table 1\") in a medical/epidemiological journal article.","text_prefix":false,"timestamp":1616920011,"version":"1.5.1"},"r-tablehtml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/LyzandeR/tableHTML","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tableHTML/tableHTML_2.1.3.tar.gz","https://cran.r-project.org/src/contrib/tableHTML_2.1.3.tar.gz"],"subdirs":["noarch"],"summary":"A tool to create and style HTML tables with CSS. These can be exported and used in any application that accepts HTML (e.g. 'shiny', 'rmarkdown', 'PowerPoint'). It also provides functions to create CSS files (which also work with shiny).","text_prefix":false,"timestamp":1764599111,"version":"2.1.3"},"r-tableone":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/kaz-yos/tableone","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tableone/tableone_0.13.2.tar.gz","https://cran.r-project.org/src/contrib/tableone_0.13.2.tar.gz"],"subdirs":["noarch"],"summary":"Creates 'Table 1', i.e., description of baseline patient characteristics, which is essential in every medical research. Supports both continuous and categorical variables, as well as p-values and standardized mean differences. Weighted data are supported via the 'survey' package.","text_prefix":false,"timestamp":1758266094,"version":"0.13.2"},"r-tablerdash":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://rinterface.github.io/tablerDash/, https://github.com/RinteRface/tablerDash/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tablerDash/tablerDash_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/tablerDash_0.1.5.tar.gz"],"subdirs":["noarch"],"summary":"'R' interface to the 'Tabler' HTML template. See more here <https://tabler.io>.  'tablerDash' is a light 'Bootstrap 4' dashboard template. There are different  layouts available such as a one page dashboard or a multi page template, where the navigation menu is contained in the navigation bar. A fancy example is available at <https://dgranjon.shinyapps.io/shinyMons/>.","text_prefix":false,"timestamp":1758616358,"version":"0.1.5"},"r-tables":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://r-forge.r-project.org/projects/tables/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tables/tables_0.9.35.tar.gz","https://cran.r-project.org/src/contrib/tables_0.9.35.tar.gz"],"subdirs":["noarch"],"summary":"Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.","text_prefix":false,"timestamp":1666071415,"version":"0.9.35"},"r-tabpfn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://tabpfn.tidymodels.org, https://github.com/tidymodels/tabpfn","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/tabpfn/tabpfn_0.3.0.tar.gz","https://cloud.r-project.org/src/contrib/tabpfn_0.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a consistent API for classification and regression models based on the 'TabPFN' model of Hollmann et al. (2025), \"Accurate predictions on small data with a tabular foundation model,\" Nature, 637(8045) <doi:10.1038/s41586-024-08328-6>. The calculations are served via 'Python' to train and predict the model.","text_prefix":false,"timestamp":1788343564,"version":"0.3.0"},"r-tabulizer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/tabulizer","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tabulizer/tabulizer_0.2.2.tar.gz","https://cran.r-project.org/src/contrib/tabulizer_0.2.2.tar.gz"],"subdirs":["noarch"],"summary":"Bindings for the 'Tabula' <http://tabula.technology/> 'Java' library, which can extract tables from PDF documents. The 'tabulizerjars' package <https://github.com/ropensci/tabulizerjars> provides versioned 'Java' .jar files, including all dependencies, aligned to releases of 'Tabula'.","text_prefix":false,"timestamp":1758378153,"version":"0.2.2"},"r-tabulizerjars":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/tabulizerjars","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tabulizerjars/tabulizerjars_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/tabulizerjars_1.0.1.tar.gz"],"subdirs":["noarch"],"summary":"'Java' .jar files for the 'Tabula' <http://tabula.technology/> 'Java' library, which are required by the 'tabulizer' R package. The version numbering of this package corresponds to versions of 'tabula-java' library releases <https://github.com/tabulapdf/tabula-java/releases/>.","text_prefix":false,"timestamp":1758162925,"version":"1.0.1"},"r-tagcloud":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://logfc.wordpress.com","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tagcloud/tagcloud_0.7.0.tar.gz","https://cran.r-project.org/src/contrib/tagcloud_0.7.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Generating Tag and Word Clouds.","text_prefix":false,"timestamp":1757851018,"version":"0.7.0"},"r-tailor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tidymodels/tailor, https://tailor.tidymodels.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tailor/tailor_0.1.0.tar.gz","https://cran.r-project.org/src/contrib/tailor_0.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations. This package introduces 'tailor' objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) <doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the 'tidymodels' framework.","text_prefix":false,"timestamp":1757575339,"version":"0.1.0"},"r-tam":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.edmeasurementsurveys.com/TAM/Tutorials/, https://github.com/alexanderrobitzsch/TAM, https://sites.google.com/view/alexander-robitzsch/software","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/TAM/TAM_4.3-25.tar.gz","https://cran.r-project.org/src/contrib/TAM_4.3-25.tar.gz"],"subdirs":["linux-64","win-64"],"summary":"Includes marginal maximum likelihood estimation and joint maximum likelihood estimation for unidimensional and multidimensional item response models. The package functionality covers the Rasch model, 2PL model, 3PL model, generalized partial credit model, multi-faceted Rasch model, nominal item response model, structured latent class model, mixture distribution IRT models, and located latent class models. Latent regression models and plausible value imputation are also supported. For details see Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>, Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>, Formann, 1982 <doi:10.1002/bimj.4710240209>, Formann, 1992 <doi:10.1080/01621459.1992.10475229>.","text_prefix":false,"timestamp":1773528385,"version":"4.3_25"},"r-tangles":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=tangles","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tangles/tangles_2.0.1.tar.gz","https://cran.r-project.org/src/contrib/tangles_2.0.1.tar.gz"],"subdirs":["noarch"],"summary":"Spatial data anonymization preserves confidentiality. Using methods described in Zandbergen (2014) <doi:10.1155/2014/567049>, spatial data anonymization is achieved by dithering original spatial coordinates with combinations of randomized vertical, horizontal and rotational shifts. This can apply to non-grid spatial point patterns and raster objects, and the methods preserve the same spatial characteristics and relationships of the original data. Unique hash keying enables data subjected to anonymization sequences to be re-identified where required.","text_prefix":false,"timestamp":1759389127,"version":"2.0.1"},"r-tangram":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=tangram","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tangram/tangram_0.8.3.tar.gz","https://cran.r-project.org/src/contrib/tangram_0.8.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides an extensible formula system to quickly and easily create production quality tables. The steps of the process are formula parser, statistical content generation from data, to rendering. Each step of the process is separate and user definable thus creating a set of building blocks for highly extensible table generation. A user is not limited by any of the  choices of the package creator other than the formula grammar. For example, one could chose to add a different S3 rendering function and output a format not provided in the default package. Or possibly one would rather have Gini coefficients for their statistical content. Routines to achieve New England Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are provided. The package contains rendering for HTML5, Rmarkdown and an indexing format for use in tracing and tracking are provided.","text_prefix":false,"timestamp":1537478186,"version":"0.8.3"},"r-tarchetypes":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ropensci/tarchetypes","home":"https://docs.ropensci.org/tarchetypes/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tarchetypes/tarchetypes_0.14.1.tar.gz","https://cran.r-project.org/src/contrib/tarchetypes_0.14.1.tar.gz"],"subdirs":["noarch"],"summary":"Function-oriented Make-like declarative workflows for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible workflows concisely and compactly. The methods in this package were influenced by the 'drake' R package by Will Landau (2018) <doi:10.21105/joss.00550>.","text_prefix":false,"timestamp":1639236797,"version":"0.14.1"},"r-targets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ropensci/targets","home":"https://docs.ropensci.org/targets/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/targets/targets_1.12.0.tar.gz","https://cran.r-project.org/src/contrib/targets_1.12.0.tar.gz"],"subdirs":["noarch"],"summary":"As a pipeline toolkit for Statistics and data science in R, the 'targets' package brings together function-oriented programming and 'Make'-like declarative workflows. It analyzes the dependency relationships among the tasks of a workflow, skips steps that are already up to date, runs the necessary computation with optional parallel workers, abstracts files as R objects, and provides tangible evidence that the results match the underlying code and data. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).","text_prefix":false,"timestamp":1632300497,"version":"1.12.0"},"r-tarifx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=taRifx","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/taRifx/taRifx_1.0.6.2.tar.gz","https://cran.r-project.org/src/contrib/taRifx_1.0.6.2.tar.gz"],"subdirs":["noarch"],"summary":"A collection of various utility and convenience functions.","text_prefix":false,"timestamp":1758626862,"version":"1.0.6.2"},"r-tau":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=tau","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tau/tau_0.0-29.tar.gz","https://cran.r-project.org/src/contrib/tau_0.0-29.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Utilities for text analysis.","text_prefix":false,"timestamp":1784298885,"version":"0.0_29"},"r-taxa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/taxa","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/taxa/taxa_0.4.4.tar.gz","https://cran.r-project.org/src/contrib/taxa_0.4.4.tar.gz"],"subdirs":["noarch"],"summary":"Provides taxonomic classes for groupings of taxonomic names without data, and those with data. 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'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team  within Google's Machine Intelligence research organization for the  purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.","text_prefix":false,"timestamp":1539317993,"version":"2.20.0"},"r-tensr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=tensr","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tensr/tensr_1.0.2.tar.gz","https://cran.r-project.org/src/contrib/tensr_1.0.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of functions for Kronecker structured covariance estimation and testing under the array normal model. 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'testthat' is a testing framework for R that is easy to learn and use, and integrates with your existing 'workflow'.","text_prefix":false,"timestamp":1608207478,"version":"3.3.2"},"r-texreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/leifeld/texreg/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/texreg/texreg_1.39.5.tar.gz","https://cran.r-project.org/src/contrib/texreg_1.39.5.tar.gz"],"subdirs":["noarch"],"summary":"Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. (If the Zelig package, which this package enhances, cannot be found on CRAN, you can find it at <https://github.com/IQSS/Zelig>.)","text_prefix":false,"timestamp":1646406334,"version":"1.39.5"},"r-text2vec":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://text2vec.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/text2vec/text2vec_0.6.6.tar.gz","https://cran.r-project.org/src/contrib/text2vec_0.6.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. This package provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.","text_prefix":false,"timestamp":1650788080,"version":"0.6.6"},"r-textclean":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/trinker/textclean","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/textclean/textclean_0.9.7.tar.gz","https://cran.r-project.org/src/contrib/textclean_0.9.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tools to clean and process text.  Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For example, emoticons are often used in text but not always easily handled by analysis algorithms.  The replace_emoticon() function replaces emoticons with word equivalents.","text_prefix":false,"timestamp":1528557471,"version":"0.9.7"},"r-textfeatures":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/mkearney/textfeatures","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/textfeatures/textfeatures_0.3.3.tar.gz","https://cran.r-project.org/src/contrib/textfeatures_0.3.3.tar.gz"],"subdirs":["noarch"],"summary":"A tool for extracting some generic features (e.g., number of words, line breaks, characters per word, URLs, lower case, upper case, commas, periods, exclamation points, etc.) from strings of text.","text_prefix":false,"timestamp":1758702422,"version":"0.3.3"},"r-textminer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.rtextminer.com/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/textmineR/textmineR_3.0.6.tar.gz","https://cran.r-project.org/src/contrib/textmineR_3.0.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An aid for text mining in R, with a syntax that should be familiar to experienced R users. Provides a wrapper for several topic models that take similarly-formatted input and give similarly-formatted output. Has additional functionality for analyzing and diagnostics for topic models.","text_prefix":false,"timestamp":1760772105,"version":"3.0.6"},"r-textrecipes":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidymodels/textrecipes","home":"https://textrecipes.tidymodels.org/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/textrecipes/textrecipes_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/textrecipes_1.1.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","osx-arm64","win-64"],"summary":"Converting text to numerical features requires specifically created procedures, which are implemented as steps according to the 'recipes' package. These steps allows for tokenization, filtering, counting (tf and tfidf) and feature hashing.","text_prefix":false,"timestamp":1758619902,"version":"1.1.0"},"r-textshape":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/trinker/textshape","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/textshape/textshape_1.7.5.tar.gz","https://cran.r-project.org/src/contrib/textshape_1.7.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Tools that can be used to reshape and restructure text data.","text_prefix":false,"timestamp":1538306586,"version":"1.7.5"},"r-textshaping":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/r-lib/textshaping","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/textshaping/textshaping_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/textshaping_1.0.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides access to the text shaping functionality in the 'HarfBuzz' library and the bidirectional algorithm in the 'Fribidi' library. 'textshaping' is a low-level utility package mainly for graphic devices that expands upon the font tool-set provided by the 'systemfonts' package.","text_prefix":false,"timestamp":1622312968,"version":"1.0.5"},"r-textstem":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/trinker/textstem","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/textstem/textstem_0.1.4.tar.gz","https://cran.r-project.org/src/contrib/textstem_0.1.4.tar.gz"],"subdirs":["noarch"],"summary":"Tools that stem and lemmatize text.  Stemming is a process that removes endings such as affixes.  Lemmatization is the process of grouping inflected forms together as a single base form.","text_prefix":false,"timestamp":1766460316,"version":"0.1.4"},"r-tfautograph":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://t-kalinowski.github.io/tfautograph/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tfautograph/tfautograph_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/tfautograph_0.3.2.tar.gz"],"subdirs":["noarch"],"summary":"Translate R control flow expressions into 'Tensorflow' graphs.","text_prefix":false,"timestamp":1629442190,"version":"0.3.2"},"r-tfdatasets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/tfdatasets","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tfdatasets/tfdatasets_2.18.0.tar.gz","https://cran.r-project.org/src/contrib/tfdatasets_2.18.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Interface to 'TensorFlow' Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See <https://www.tensorflow.org/programmers_guide/datasets> for additional details.","text_prefix":false,"timestamp":1530482507,"version":"2.18.0"},"r-tfestimators":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/tfestimators","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tfestimators/tfestimators_1.9.3.tar.gz","https://cran.r-project.org/src/contrib/tfestimators_1.9.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Interface to 'TensorFlow' Estimators  <https://www.tensorflow.org/programmers_guide/estimators>, a high-level  API that provides implementations of many different model types  including linear models and deep neural networks.","text_prefix":false,"timestamp":1530506735,"version":"1.9.3"},"r-tfisher":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=TFisher","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/TFisher/TFisher_0.2.1.tar.gz","https://cloud.r-project.org/src/contrib/TFisher_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"We provide the cumulative distribution function (CDF), quantile, and statistical power calculator for a collection of thresholding Fisher's p-value combination methods, including Fisher's p-value combination method, truncated product method and, in particular, soft-thresholding Fisher's p-value combination method which is proven to be optimal in some context of signal detection. The p-value calculator for the omnibus version of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. \"TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values\", submitted.","text_prefix":false,"timestamp":1787731122,"version":"0.2.1"},"r-tfmpvalue":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ge11232002/TFMPvalue","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/TFMPvalue/TFMPvalue_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/TFMPvalue_1.0.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"In putative Transcription Factor Binding Sites (TFBSs)  identification from sequence/alignments, we are interested in the significance of certain match score. TFMPvalue provides the accurate calculation of P-value with  score threshold for Position Weight Matrices,  or the score with given P-value.  It is an interface to code originally made available by  Helene Touzet and Jean-Stephane Varre, 2007,  Algorithms Mol Biol:2, 15. Touzet and Varre (2007) <DOI:10.1186/1748-7188-2-15>.","text_prefix":false,"timestamp":1533402505,"version":"1.0.0"},"r-tfplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://tsanalysis.r-forge.r-project.org/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tfplot/tfplot_2021.6-1.tar.gz","https://cran.r-project.org/src/contrib/tfplot_2021.6-1.tar.gz"],"subdirs":["noarch"],"summary":"Utilities for simple manipulation and quick plotting of time series data. These utilities use the 'tframe' package which provides a programming kernel for time series. Extensions to 'tframe' provided in 'tframePlus' can also be used. See the Guide vignette for examples.","text_prefix":false,"timestamp":1758190431,"version":"2021.6_1"},"r-tfprobability":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/tfprobability","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tfprobability/tfprobability_0.15.2.tar.gz","https://cran.r-project.org/src/contrib/tfprobability_0.15.2.tar.gz"],"subdirs":["noarch"],"summary":"Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.","text_prefix":false,"timestamp":1643805171,"version":"0.15.2"},"r-tframe":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://tsanalysis.r-forge.r-project.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tframe/tframe_2015.12-1.1.tar.gz","https://cran.r-project.org/src/contrib/tframe_2015.12-1.1.tar.gz"],"subdirs":["noarch"],"summary":"A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time. Also provides plotting, time windowing, and some other utility functions which are specifically intended for time series. See the Guide distributed as a vignette, or ?tframe.Intro for more details. (User utilities are in package tfplot.)","text_prefix":false,"timestamp":1757895346,"version":"2015.12_1.1"},"r-tfruns":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/tfruns","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tfruns/tfruns_1.5.4.tar.gz","https://cran.r-project.org/src/contrib/tfruns_1.5.4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Create and manage unique directories for each 'TensorFlow'  training run. Provides a unique, time stamped directory for each run along with functions to retrieve the directory of the latest run or  latest several runs.","text_prefix":false,"timestamp":1528985438,"version":"1.5.4"},"r-tfse":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://tfse.mikewk.com","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tfse/tfse_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/tfse_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"A collection of useful tools for programming and writing-scripts.  Several functions are simple wrappers around base R functions that extend  their functionality while also providing some convenient properties\u00e2\u20ac\u201cregular expression functions that automatically detect look-ahead and look-behind  statements, a read-line function that suppresses incomplete-final-line  warnings and automatically opens and closes connections, a version of  substrings that starts from the end of strings, etc. Other functions are  useful for checking whether packages are installed, omitting  missing data, and showing in-use connections.","text_prefix":false,"timestamp":1758111653,"version":"0.5.0"},"r-tgp":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://bobby.gramacy.com/r_packages/tgp/","license":"LGPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tgp/tgp_2.4-23.tar.gz","https://cran.r-project.org/src/contrib/tgp_2.4-23.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM).  Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output.  Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement.  The latter supports derivative-free optimization of noisy black-box functions.  For details and tutorials, see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) <doi:10.18637/jss.v033.i06>.","text_prefix":false,"timestamp":1758479674,"version":"2.4_23"},"r-th.data":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=TH.data","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/TH.data/TH.data_1.1-5.tar.gz","https://cran.r-project.org/src/contrib/TH.data_1.1-5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains data sets used in other packages Torsten Hothorn maintains.","text_prefix":false,"timestamp":1536619043,"version":"1.1_5"},"r-thankr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/dirkschumacher/thankr","home":"https://CRAN.R-project.org/package=thankr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/thankr/thankr_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/thankr_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Find out who maintains the packages you use in your current session or in your package library and maybe say 'thank you'.","text_prefix":false,"timestamp":1758482436,"version":"1.0.0"},"r-theft":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://hendersontrent.github.io/theft/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/theft/theft_0.8.4.tar.gz","https://cran.r-project.org/src/contrib/theft_0.8.4.tar.gz"],"subdirs":["noarch"],"summary":"Consolidates and calculates different sets of time-series features from multiple 'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, 'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, 'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>. Provides a standardised workflow from feature calculation to feature processing, machine learning classification procedures, and the production of statistical graphics.","text_prefix":false,"timestamp":1777455897,"version":"0.8.4"},"r-thematic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://rstudio.github.io/thematic/, https://github.com/rstudio/thematic","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/thematic/thematic_0.1.8.tar.gz","https://cran.r-project.org/src/contrib/thematic_0.1.8.tar.gz"],"subdirs":["noarch"],"summary":"Theme 'ggplot2', 'lattice', and 'base' graphics based on a few choices, including foreground color, background color, accent color, and font family. Fonts that aren't available on the system, but are available via download on 'Google Fonts', can be automatically downloaded, cached, and registered for use with the 'showtext' and 'ragg' packages.","text_prefix":false,"timestamp":1760415164,"version":"0.1.8"},"r-themis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tidymodels/themis","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/themis/themis_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/themis_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 <arXiv:1106.1813>, BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008 <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the number of majority cases using NearMiss 2003 <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.","text_prefix":false,"timestamp":1648636147,"version":"1.1.0"},"r-thief":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://pkg.robjhyndman.com/thief, https://github.com/robjhyndman/thief","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/thief/thief_0.3.tar.gz","https://cran.r-project.org/src/contrib/thief_0.3.tar.gz"],"subdirs":["noarch"],"summary":"Methods and tools for generating forecasts at different temporal frequencies using a hierarchical time series approach.","text_prefix":false,"timestamp":1758669496,"version":"0.3"},"r-thinkr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=thinkr","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/thinkr/thinkr_0.16.tar.gz","https://cran.r-project.org/src/contrib/thinkr_0.16.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Some tools for cleaning up messy 'Excel' files to be suitable for R. People who have been working with 'Excel' for years built more or less complicated sheets with names, characters, formats that are not homogeneous. 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In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) <http://jmlr.org/papers/v18/16-305.html>.","text_prefix":false,"timestamp":1544543322,"version":"1.0.2"},"r-tidypredict":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://tidymodels.github.io/tidypredict","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/tidypredict/tidypredict_1.2.2.tar.gz","https://cloud.r-project.org/src/contrib/tidypredict_1.2.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. 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The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. 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'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out  of string columns. 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The package includes utilities for updating of date-time components (year, month, day etc.), modification of time-zones, rounding of date-times, period addition and subtraction etc. Parts of the 'CCTZ' source code, released under the Apache 2.0 License, are included in this package. See <https://github.com/google/cctz> for more details.","text_prefix":false,"timestamp":1769737833,"version":"0.4.0"},"r-timedate":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.rmetrics.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/timeDate/timeDate_4052.112.tar.gz","https://cran.r-project.org/src/contrib/timeDate_4052.112.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"The 'timeDate' class fulfils the conventions of the ISO 8601  standard as well as of the ANSI C and POSIX standards. Beyond these standards it provides the \"Financial Center\" concept which allows to handle data records collected in different time  zones and mix them up to have always the proper time stamps with  respect to your personal financial center, or alternatively to the GMT reference time. 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Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package.","text_prefix":false,"timestamp":1598278534,"version":"2.0.7"},"r-timeroc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=timeROC","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/timeROC/timeROC_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/timeROC_0.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed.","text_prefix":false,"timestamp":1538300670,"version":"0.4.1"},"r-timeseries":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.rmetrics.org","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/timeSeries/timeSeries_4052.112.tar.gz","https://cran.r-project.org/src/contrib/timeSeries_4052.112.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a class and various tools for financial time series. 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The local .Rmd (or .Rnw) is uploaded as a plain-text file to Google Drive. By taking advantage of the easily readable Markdown (or LaTeX) syntax and the well-known online interface offered by Google Docs, collaborators can easily contribute to the writing and editing process. After integrating all authors\u2019 contributions, the final document can be downloaded and rendered locally.","text_prefix":false,"timestamp":1629884855,"version":"1.1.1"},"r-tractor.base":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.tractor-mri.org.uk, https://github.com/tractor/tractor","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/tractor.base/tractor.base_3.5.2.1.tar.gz","https://cloud.r-project.org/src/contrib/tractor.base_3.5.2.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions for working with magnetic resonance images. Reading and writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH); interactive and non-interactive visualisation; flexible image manipulation; metadata and sparse image handling.","text_prefix":false,"timestamp":0,"version":"3.5.2.1"},"r-traits":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/traits","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/traits/traits_0.6.0.tar.gz","https://cran.r-project.org/src/contrib/traits_0.6.0.tar.gz"],"subdirs":["noarch"],"summary":"Species trait data from many different sources, including sequence data from 'NCBI', plant trait data from 'BETYdb', plant data from the USDA plants database, data from 'EOL' 'Traitbank', Coral traits data (<https://coraltraits.org>), 'Birdlife' International, and more.","text_prefix":false,"timestamp":1588686609,"version":"0.6.0"},"r-traminer":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://traminer.unige.ch","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/TraMineR/TraMineR_2.2-13.tar.gz","https://cran.r-project.org/src/contrib/TraMineR_2.2-13.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. 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Includes methods to simulate and analyse outbreaks. The methodology is described in Didelot et al. (2014) <doi:10.1093/molbev/msu121>, Didelot et al. (2017) <doi:10.1093/molbev/msw275>.","text_prefix":false,"timestamp":1591989231,"version":"1.4.5"},"r-transport":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.dominic.schuhmacher.name","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/transport/transport_0.15-4.tar.gz","https://cran.r-project.org/src/contrib/transport_0.15-4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. 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(2014) <doi:10.1515/ijb-2012-0052> for further details.","text_prefix":false,"timestamp":1534182912,"version":"2.1.1"},"r-tree":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=tree","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/tree/tree_1.0-47.tar.gz","https://cloud.r-project.org/src/contrib/tree_1.0-47.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Classification and regression trees.","text_prefix":false,"timestamp":1665414034,"version":"1.0_47"},"r-treebase":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/treebase","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/treebase/treebase_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/treebase_0.1.5.tar.gz"],"subdirs":["noarch"],"summary":"Interface to the API for 'TreeBASE' <http://treebase.org> from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic trees (of species, population, or genes) and the data used to create them.","text_prefix":false,"timestamp":1758608788,"version":"0.1.5"},"r-treedist":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://ms609.github.io/TreeDist/, https://github.com/ms609/TreeDist","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/TreeDist/TreeDist_2.15.0.tar.gz","https://cloud.r-project.org/src/contrib/TreeDist_2.15.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements measures of tree similarity, including information-based generalized Robinson-Foulds distances (Phylogenetic Information Distance, Clustering Information Distance, Matching Split Information Distance; Smith 2020) <doi:10.1093/bioinformatics/btaa614>; Jaccard-Robinson-Foulds distances (Bocker et al. 2013) <doi:10.1007/978-3-642-40453-5_13>, including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>; the Matching Split Distance (Bogdanowicz & Giaro 2012) <doi:10.1109/TCBB.2011.48>; the Hierarchical Mutual Information (Perotti et al. 2015) <doi:10.1103/PhysRevE.92.062825>; Maximum Agreement Subtree distances; the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. (1996) <doi:10.1007/3-540-61332-3_168>. Includes tools for visualizing mappings of tree space (Smith 2022) <doi:10.1093/sysbio/syab100>, for identifying islands of trees (Silva and Wilkinson 2021) <doi:10.1093/sysbio/syab015>, for calculating the median of sets of trees, and for computing the information content of trees and splits.","text_prefix":false,"timestamp":1600360732,"version":"2.15.0"},"r-treeheatr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://trang1618.github.io/treeheatr/index.html, https://trang1618.github.io/treeheatr-manuscript/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/treeheatr/treeheatr_0.2.3.tar.gz","https://cran.r-project.org/src/contrib/treeheatr_0.2.3.tar.gz"],"subdirs":["noarch"],"summary":"Creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. 'treeheatr' utilizes the customizable 'ggparty' package for drawing decision trees.","text_prefix":false,"timestamp":1769738074,"version":"0.2.3"},"r-treeman":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=treeman","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/treeman/treeman_1.1.4.tar.gz","https://cran.r-project.org/src/contrib/treeman_1.1.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"S4 class and methods for intuitive and efficient phylogenetic tree manipulation.","text_prefix":false,"timestamp":1588668945,"version":"1.1.4"},"r-treemap":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=treemap","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/treemap/treemap_2.4-4.tar.gz","https://cran.r-project.org/src/contrib/treemap_2.4-4.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A treemap is a space-filling visualization of hierarchical structures. This package offers great flexibility to draw treemaps.","text_prefix":false,"timestamp":1538426822,"version":"2.4_4"},"r-treemapify":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/wilkox/treemapify","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/treemapify/treemapify_2.6.1.tar.gz","https://cran.r-project.org/src/contrib/treemapify_2.6.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides 'ggplot2' geoms for drawing treemaps.","text_prefix":false,"timestamp":1534096709,"version":"2.6.1"},"r-treesim":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=TreeSim","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/TreeSim/TreeSim_2.4.tar.gz","https://cran.r-project.org/src/contrib/TreeSim_2.4.tar.gz"],"subdirs":["noarch"],"summary":"Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.","text_prefix":false,"timestamp":1758830575,"version":"2.4"},"r-treesitter":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/DavisVaughan/r-tree-sitter","home":"https://davisvaughan.github.io/r-tree-sitter/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/treesitter/treesitter_0.3.2.tar.gz","https://cran.r-project.org/src/contrib/treesitter_0.3.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides bindings to 'Tree-sitter', an incremental parsing system for programming tools. 'Tree-sitter' builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. It also includes a robust error recovery system that provides useful parse results even in the presence of syntax errors.","text_prefix":false,"timestamp":1784627612,"version":"0.3.2"},"r-treetools":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ms609/TreeTools/","home":"https://ms609.github.io/TreeTools/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/TreeTools/TreeTools_2.5.0.tar.gz","https://cloud.r-project.org/src/contrib/TreeTools_2.5.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Efficient implementations of functions for the creation, modification and analysis of phylogenetic trees. Applications include: generation of trees with specified shapes; tree rearrangement; analysis of tree shape; rooting of trees and extraction of subtrees; calculation and depiction of split support; plotting the position of rogue taxa (Klopfstein & Spasojevic 2019) <doi:10.1371/journal.pone.0212942>; calculation of ancestor-descendant relationships, of 'stemwardness' (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>, and of tree balance (Mir et al. 2013, Lemant et al. 2022) <doi:10.1016/j.mbs.2012.10.005>, <doi:10.1093/sysbio/syac027>; artificial extinction (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>; import and export of trees from Newick, Nexus (Maddison et al. 1997) <doi:10.1093/sysbio/46.4.590>, and TNT <https://www.lillo.org.ar/phylogeny/tnt/> formats; and analysis of splits and cladistic information.","text_prefix":false,"timestamp":1608284714,"version":"2.5.0"},"r-trekcolors":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/leonawicz/trekcolors","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/trekcolors/trekcolors_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/trekcolors_0.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a dataset of predefined color palettes based on the Star Trek science fiction series, associated color palette functions, and additional functions for generating customized palettes that are on theme. The package also offers functions for applying the palettes to plots made using the 'ggplot2' package.","text_prefix":false,"timestamp":1588735559,"version":"0.2.0"},"r-trend":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=trend","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/trend/trend_1.1.9.tar.gz","https://cloud.r-project.org/src/contrib/trend_1.1.9.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test.","text_prefix":false,"timestamp":1665654778,"version":"1.1.9"},"r-triangle":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/bertcarnell/triangle/","home":"https://bertcarnell.github.io/triangle/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/triangle/triangle_1.1.0.tar.gz","https://cran.r-project.org/src/contrib/triangle_1.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides the \"r, q, p, and d\" distribution functions for the triangle distribution.  Also includes maximum likelihood estimation of parameters.","text_prefix":false,"timestamp":1665466504,"version":"1.1.0"},"r-triebeard":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Ironholds/triebeard/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/triebeard/triebeard_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/triebeard_0.4.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in developing packages with 'Rcpp'.","text_prefix":false,"timestamp":1665214790,"version":"0.4.1"},"r-trimcluster":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.homepages.ucl.ac.uk/~ucakche/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/trimcluster/trimcluster_0.2-0.tar.gz","https://cran.r-project.org/src/contrib/trimcluster_0.2-0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Trimmed k-means clustering. The method is described in Cuesta-Albertos et al. (1997) <doi:10.1214/aos/1031833664>.","text_prefix":false,"timestamp":1538477163,"version":"0.2_0"},"r-tripack":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=tripack","license":"LicenseRef-ACM","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/tripack/tripack_1.3-9.4.tar.gz","https://cran.r-project.org/src/contrib/tripack_1.3-9.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A constrained two-dimensional Delaunay triangulation package providing both triangulation and generation of voronoi mosaics of irregular spaced data.","text_prefix":false,"timestamp":1780823760,"version":"1.3_9.4"},"r-truncdist":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=truncdist","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/truncdist/truncdist_1.0-2.tar.gz","https://cran.r-project.org/src/contrib/truncdist_1.0-2.tar.gz"],"subdirs":["noarch"],"summary":"A collection of tools to evaluate probability density functions, cumulative distribution functions, quantile functions and random numbers for truncated random variables.  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Render R graphics fast and easy to many common file formats. 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If no such unique substring exists, the least frequent substring is used. If multiple unique substrings exist, the lexicographically smallest substring is used. 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In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'.  Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, \"Discrete Data Analysis with R\" by Michael Friendly and David Meyer.","text_prefix":false,"timestamp":1538225758,"version":"0.9.8"},"r-vcfr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/knausb/vcfR, https://knausb.github.io/vcfR_documentation/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vcfR/vcfR_1.16.0.tar.gz","https://cran.r-project.org/src/contrib/vcfR_1.16.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","osx-64","osx-arm64","win-64"],"summary":"Facilitates easy manipulation of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices of data. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file (*.vcf.gz). It also may be converted into other popular R objects (e.g., genlight, DNAbin). 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A port of the Ruby gem of the same name (<https://github.com/vcr/vcr/>). Works by hooking into the 'webmockr' R package for matching 'HTTP' requests by various rules ('HTTP' method, 'URL', query parameters, headers, body, etc.), and then caching real 'HTTP' responses on disk in 'cassettes'. Subsequent 'HTTP' requests matching any previous requests in the same 'cassette' use a cached 'HTTP' response.","text_prefix":false,"timestamp":1764971711,"version":"2.1.0"},"r-vctrs":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://vctrs.r-lib.org/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vctrs/vctrs_0.7.3.tar.gz","https://cran.r-project.org/src/contrib/vctrs_0.7.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Defines new notions of prototype and size that are used to provide tools for consistent and well-founded type-coercion and size-recycling, and are in turn connected to ideas of type- and size-stability useful for analysing function interfaces.","text_prefix":false,"timestamp":1666425705,"version":"0.7.3"},"r-vdiffr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-lib/vdiffr","home":"https://vdiffr.r-lib.org/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vdiffr/vdiffr_1.0.9.tar.gz","https://cran.r-project.org/src/contrib/vdiffr_1.0.9.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An extension to the 'testthat' package that makes it easy to add graphical unit tests. It provides a Shiny application to manage the test cases.","text_prefix":false,"timestamp":1623919731,"version":"1.0.9"},"r-vegan":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/vegandevs/vegan","home":"https://vegandevs.github.io/vegan/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vegan/vegan_2.7-5.tar.gz","https://cran.r-project.org/src/contrib/vegan_2.7-5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Ordination methods, diversity analysis and other functions for community and vegetation ecologists.","text_prefix":false,"timestamp":1551960602,"version":"2.7_5"},"r-vegclust":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://emf-creaf.github.io/vegclust/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vegclust/vegclust_2.0.3.tar.gz","https://cran.r-project.org/src/contrib/vegclust_2.0.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) <doi:10.1111/j.1654-1103.2010.01211.x>; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) <doi:10.1111/2041-210X.12116>.","text_prefix":false,"timestamp":1728724141,"version":"2.0.3"},"r-velox":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=velox","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/velox/velox_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/velox_0.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"C++ accelerated raster manipulation and extraction.","text_prefix":false,"timestamp":1588668636,"version":"0.2.0"},"r-venn":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dusadrian/venn","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/venn/venn_1.13.tar.gz","https://cran.r-project.org/src/contrib/venn_1.13.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A close to zero dependency package to draw and display Venn diagrams up to 7 sets, and any Boolean union of set intersections.","text_prefix":false,"timestamp":0,"version":"1.13"},"r-venndiagram":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=VennDiagram","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/VennDiagram/VennDiagram_1.8.2.tar.gz","https://cran.r-project.org/src/contrib/VennDiagram_1.8.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.","text_prefix":false,"timestamp":0,"version":"1.8.2"},"r-venneuler":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.rforge.net/venneuler/, http://www.cs.uic.edu/~wilkinson/","license":"MPL-1.1","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/venneuler/venneuler_1.1-4.tar.gz","https://cran.r-project.org/src/contrib/venneuler_1.1-4.tar.gz"],"subdirs":["noarch"],"summary":"Calculates and displays Venn and Euler Diagrams","text_prefix":false,"timestamp":1621879511,"version":"1.1_4"},"r-verification":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=verification","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/verification/verification_1.45.tar.gz","https://cran.r-project.org/src/contrib/verification_1.45.tar.gz"],"subdirs":["noarch"],"summary":"Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.","text_prefix":false,"timestamp":1758805342,"version":"1.45"},"r-vetiver":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidymodels/vetiver","home":"https://vetiver.tidymodels.org/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vetiver/vetiver_0.2.7.tar.gz","https://cran.r-project.org/src/contrib/vetiver_0.2.7.tar.gz"],"subdirs":["noarch"],"summary":"The goal of 'vetiver' is to provide fluent tooling to version, share, deploy, and monitor a trained model. Functions handle both recording and checking the model's input data prototype, and predicting from a remote API endpoint. The 'vetiver' package is extensible, with generics that can support many kinds of models.","text_prefix":false,"timestamp":1660253064,"version":"0.2.7"},"r-vgam":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://www.stat.auckland.ac.nz/~yee/VGAM","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/VGAM/VGAM_1.1-14.tar.gz","https://cran.r-project.org/src/contrib/VGAM_1.1-14.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book \"Vector Generalized Linear and Additive Models: With an Implementation in R\" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.","text_prefix":false,"timestamp":1603449154,"version":"1.1_14"},"r-vicus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rikenbit/Vicus","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Vicus/Vicus_0.99.0.tar.gz","https://cran.r-project.org/src/contrib/Vicus_0.99.0.tar.gz"],"subdirs":["noarch"],"summary":"Compared with the similar graph embedding method such as Laplacian Eigenmaps, 'Vicus' can exploit more local structures of graph data. For the details of the methods, see the reference section of 'GitHub' README.md <https://github.com/rikenbit/Vicus>.","text_prefix":false,"timestamp":1778662594,"version":"0.99.0"},"r-vim":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/statistikat/VIM","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/VIM/VIM_7.3.1.tar.gz","https://cloud.r-project.org/src/contrib/VIM_7.3.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods (<doi:10.1007/s11634-011-0102-y>). A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods. Fast imputation methods such as k-nearest neighbor imputation (<doi:10.18637/jss.v074.i07>) and imputation using robust methods are provided (<doi:10.1016/j.csda.2011.04.012>).","text_prefix":false,"timestamp":1626968174,"version":"7.3.1"},"r-vinecopula":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tnagler/VineCopula","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/VineCopula/VineCopula_2.6.1.tar.gz","https://cran.r-project.org/src/contrib/VineCopula_2.6.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides tools for the statistical analysis of regular vine copula models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>. The package includes tools for parameter estimation, model selection, simulation, goodness-of-fit tests, and visualization. Tools for estimation, selection and exploratory data analysis of bivariate copula models are also provided.","text_prefix":false,"timestamp":1770887873,"version":"2.6.1"},"r-vinereg":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://tnagler.github.io/vinereg/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/vinereg/vinereg_0.13.0.tar.gz","https://cloud.r-project.org/src/contrib/vinereg_0.13.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Implements D-vine quantile regression models with parametric or nonparametric pair-copulas. See Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and Schallhorn et al. (2017) <doi:10.48550/arXiv.1705.08310>.","text_prefix":false,"timestamp":1788294936,"version":"0.13.0"},"r-vioplot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=vioplot","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vioplot/vioplot_0.5.1.tar.gz","https://cran.r-project.org/src/contrib/vioplot_0.5.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A violin plot is a combination of a box plot and a kernel density plot. This package allows extensive customisation of violin plots.","text_prefix":false,"timestamp":0,"version":"0.5.1"},"r-vip":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://koalaverse.github.io/vip/index.html, https://github.com/koalaverse/vip/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vip/vip_0.4.6.tar.gz","https://cran.r-project.org/src/contrib/vip_0.4.6.tar.gz"],"subdirs":["noarch"],"summary":"A general framework for constructing variable importance plots from  various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include an efficient permutation-based variable importance measure as  well as novel approaches based on partial dependence plots (PDPs) and  individual conditional expectation (ICE) curves which are described in  Greenwell et al. (2018) <arXiv:1805.04755>. An experimental method for  quantifying the relative strength of interaction effects is also included (see  the previous reference for details).","text_prefix":false,"timestamp":1588740910,"version":"0.4.6"},"r-vipor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=vipor","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vipor/vipor_0.4.7.tar.gz","https://cran.r-project.org/src/contrib/vipor_0.4.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise.","text_prefix":false,"timestamp":1540019157,"version":"0.4.7"},"r-viridis":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sjmgarnier/viridis","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/viridis/viridis_0.6.5.tar.gz","https://cran.r-project.org/src/contrib/viridis_0.6.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implementation of the 'viridis' - the default -, 'magma', 'plasma',  'inferno', and 'cividis' color maps for 'R'. 'viridis', 'magma', 'plasma',  and 'inferno' are ported from 'matplotlib' <http://matplotlib.org/>, a  popular plotting library for 'python'. 'cividis', was developed by Jamie R.  Nu\u00f1ez and Sean M. Colby. These color maps are designed in such a way that  they will analytically be perfectly perceptually-uniform, both in regular  form and also when converted to black-and-white. They are also designed to  be perceived by readers with the most common form of color blindness (all  color maps in this package) and color vision deficiency ('cividis' only).","text_prefix":false,"timestamp":0,"version":"0.6.5"},"r-viridislite":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sjmgarnier/viridisLite","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/viridisLite/viridisLite_0.4.3.tar.gz","https://cran.r-project.org/src/contrib/viridisLite_0.4.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Implementation of the 'viridis' - the default -, 'magma', 'plasma',  'inferno', and 'cividis' color maps for 'R'. 'viridis', 'magma', 'plasma',  and 'inferno' are ported from 'matplotlib' <http://matplotlib.org/>, a  popular plotting library for 'python'. 'cividis', was developed by Jamie R.  Nu\u00f1ez and Sean M. Colby. These color maps are designed in such a way that  they will analytically be perfectly perceptually-uniform, both in regular  form and also when converted to black-and-white. They are also designed to  be perceived by readers with the most common form of color blindness (all  color maps in this package) and color vision deficiency ('cividis' only).  This is the 'lite' version of the more complete 'viridis' package that can  be found at <https://cran.r-project.org/package=viridis>.","text_prefix":false,"timestamp":0,"version":"0.4.3"},"r-virtualspecies":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://borisleroy.com/virtualspecies","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/virtualspecies/virtualspecies_1.6.1.tar.gz","https://cran.r-project.org/src/contrib/virtualspecies_1.6.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides a framework for generating virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. 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It allows an interactive visualization of networks.","text_prefix":false,"timestamp":0,"version":"2.1.4"},"r-visreg":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://pbreheny.github.io/visreg","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/visreg/visreg_3.0.0.tar.gz","https://cran.r-project.org/src/contrib/visreg_3.0.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.)","text_prefix":false,"timestamp":1785268976,"version":"3.0.0"},"r-vistime":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://shosaco.github.io/vistime/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vistime/vistime_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/vistime_1.3.0.tar.gz"],"subdirs":["noarch"],"summary":"A library for creating time based charts, like Gantt or timelines. Possible outputs include 'ggplot2' diagrams, 'plotly.js' graphs, 'Highcharts.js' widgets and data.frames. Results can be used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in Shiny apps. In the interactive outputs created by vistime() and hc_vistime(), you can interact with the plot using mouse hover or zoom.","text_prefix":false,"timestamp":1774685195,"version":"1.3.0"},"r-visualize":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/coatless/visualize, http://thecoatlessprofessor.com/projects/visualize/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/visualize/visualize_4.5.0.tar.gz","https://cran.r-project.org/src/contrib/visualize_4.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Graphs the pdf or pmf and highlights what area or probability is present in user defined locations. 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Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015) <https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.","text_prefix":false,"timestamp":1588686022,"version":"1.2.1"},"r-vtree":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/nbarrowman/vtree, https://nbarrowman.github.io/vtree","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/vtree/vtree_5.7.0.tar.gz","https://cran.r-project.org/src/contrib/vtree_5.7.0.tar.gz"],"subdirs":["noarch"],"summary":"A tool for calculating and drawing \"variable trees\". 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There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme.","text_prefix":false,"timestamp":1540126576,"version":"1.1.4"},"r-waterml":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/jirikadlec2/waterml","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WaterML/WaterML_1.9.8.tar.gz","https://cran.r-project.org/src/contrib/WaterML_1.9.8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Lets you connect to any of the 'Consortium of Universities for the Advancement of Hydrological Science, Inc.' ('CUAHSI') Water Data Center 'WaterOneFlow' web services and read any 'WaterML' time series data file. To see list of available web services, see <http://hiscentral.cuahsi.org>. All versions of 'WaterML' (1.0, 1.1 and 2.0) and both types of the web service protocol ('SOAP' and 'REST') are supported. The package has six data download functions: GetServices(): show all public web services from the HIS Central Catalog. HISCentral_GetSites() and HISCentral_GetSeriesCatalog(): search for sites or time series from the HIS Central catalog based on geographic bounding box, server, or keyword. GetVariables(): Show a data.frame with all variables on the server. GetSites(): Show a data.frame with all sites on the server. GetSiteInfo(): Show what variables, methods and quality control levels are available at the specific site. GetValues(): Given a site code, variable code, start time and end time, fetch a data.frame of all the observation time series data values. The GetValues() function can also parse 'WaterML' data from a custom URL or from a local file.","text_prefix":false,"timestamp":1540117463,"version":"1.9.8"},"r-waveslim":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://waveslim.blogspot.com, http://www2.imperial.ac.uk/~bwhitche/book","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/waveslim/waveslim_1.8.5.tar.gz","https://cran.r-project.org/src/contrib/waveslim_1.8.5.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Basic wavelet routines for time series (1D), image (2D)  and array (3D) analysis.  The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).  All figures in chapters 4-7 of GSW (2001) are reproducible using this  package and R code available at the book website(s) below.","text_prefix":false,"timestamp":1655780780,"version":"1.8.5"},"r-wavethresh":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=wavethresh","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wavethresh/wavethresh_4.7.3.tar.gz","https://cran.r-project.org/src/contrib/wavethresh_4.7.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.","text_prefix":false,"timestamp":1650985420,"version":"4.7.3"},"r-wcorr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=wCorr","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wCorr/wCorr_1.9.8.tar.gz","https://cran.r-project.org/src/contrib/wCorr_1.9.8.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Calculates Pearson, Spearman, polychoric, and polyserial correlation coefficients, in weighted or unweighted form. The package implements tetrachoric correlation as a special case of the polychoric and biserial correlation as a specific case of the polyserial.","text_prefix":false,"timestamp":1665896954,"version":"1.9.8"},"r-wdi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.github.com/vincentarelbundock/WDI","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/WDI/WDI_2.8.0.tar.gz","https://cloud.r-project.org/src/contrib/WDI_2.8.0.tar.gz"],"subdirs":["noarch"],"summary":"Search, extract and format data from the World Bank's World Development Indicators.","text_prefix":false,"timestamp":1593958313,"version":"2.8.0"},"r-wdm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/tnagler/wdm-r","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/wdm/wdm_0.3.0.tar.gz","https://cloud.r-project.org/src/contrib/wdm_0.3.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides efficient implementations of weighted dependence measures and related asymptotic tests for independence. Implemented measures are the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and Hollander et al. 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This package provides functions to download these binaries and to manage processes involving them.","text_prefix":false,"timestamp":1628179281,"version":"0.2.6"},"r-wdpar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/prioritizr/wdpar","home":"https://prioritizr.github.io/wdpar/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wdpar/wdpar_1.3.9.tar.gz","https://cran.r-project.org/src/contrib/wdpar_1.3.9.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Fetch and clean data from the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM). Data is obtained from Protected Planet <https://www.protectedplanet.net/en>. To augment data cleaning procedures, users can install the 'prepr' R package (available at <https://github.com/prioritizr/prepr>). For more information on this package, see Hanson (2022) <doi:10.21105/joss.04594>.","text_prefix":false,"timestamp":1628187143,"version":"1.3.9"},"r-weatherdata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://ram-n.github.io/weatherData/","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/weatherData/weatherData_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/weatherData_0.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Functions that help in fetching weather data from websites. Given a location and a date range, these functions help fetch weather data (temperature, pressure etc.) for any weather related analysis.","text_prefix":false,"timestamp":0,"version":"0.4.1"},"r-webchem":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://docs.ropensci.org/webchem, https://github.com/ropensci/webchem","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/webchem/webchem_1.3.1.tar.gz","https://cran.r-project.org/src/contrib/webchem_1.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Chemical information from around the web. This package interacts with a suite of web services for chemical information. Sources include: Alan Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver, ChEBI, Chemical Translation Service, ChemIDplus, ChemSpider, ETOX, Flavornet, NIST Chemistry WebBook, OPSIN, PAN Pesticide Database, PubChem, SRS, Wikidata.","text_prefix":false,"timestamp":1638818252,"version":"1.3.1"},"r-webdriver":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/webdriver","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/webdriver/webdriver_1.0.6.tar.gz","https://cran.r-project.org/src/contrib/webdriver_1.0.6.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A client for the 'WebDriver' 'API'. It allows driving a (probably headless) web browser, and can be used to test web applications, including 'Shiny' apps. In theory it works with any 'WebDriver' implementation, but it was only tested with 'PhantomJS'.","text_prefix":false,"timestamp":1540162339,"version":"1.0.6"},"r-webgestaltr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/bzhanglab/WebGestaltR","license":"LGPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WebGestaltR/WebGestaltR_0.4.6.tar.gz","https://cran.r-project.org/src/contrib/WebGestaltR_0.4.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"The web version WebGestalt <http://www.webgestalt.org> supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.","text_prefix":false,"timestamp":1589587131,"version":"0.4.6"},"r-webmockr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/ropensci/webmockr","doc_url":"https://docs.ropensci.org/webmockr/","home":"https://books.ropensci.org/http-testing/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/webmockr/webmockr_2.2.0.tar.gz","https://cran.r-project.org/src/contrib/webmockr_2.2.0.tar.gz"],"subdirs":["noarch"],"summary":"Stubbing and setting expectations on 'HTTP' requests. Includes tools for stubbing 'HTTP' requests, including expected request conditions and response conditions. Match on 'HTTP' method, query parameters, request body, headers and more. Can be used for unit tests or outside of a testing context.","text_prefix":false,"timestamp":1758721605,"version":"2.2.0"},"r-webshot":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/wch/webshot/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/webshot/webshot_0.5.5.tar.gz","https://cran.r-project.org/src/contrib/webshot_0.5.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Takes screenshots of web pages, including Shiny applications and R Markdown documents.","text_prefix":false,"timestamp":0,"version":"0.5.5"},"r-webshot2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rstudio/webshot2","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/webshot2/webshot2_0.1.2.tar.gz","https://cran.r-project.org/src/contrib/webshot2_0.1.2.tar.gz"],"subdirs":["noarch"],"summary":"Takes screenshots of web pages, including Shiny applications and R Markdown documents. 'webshot2' uses headless Chrome or Chromium as the browser back-end.","text_prefix":false,"timestamp":1758457468,"version":"0.1.2"},"r-websocket":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=websocket","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/websocket/websocket_1.4.4.tar.gz","https://cran.r-project.org/src/contrib/websocket_1.4.4.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides a 'WebSocket' client interface for R. 'WebSocket' is a protocol for low-overhead real-time communication: <https://en.wikipedia.org/wiki/WebSocket>.","text_prefix":false,"timestamp":1767228120,"version":"1.4.4"},"r-webutils":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jeroen/webutils","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/webutils/webutils_1.2.3.tar.gz","https://cran.r-project.org/src/contrib/webutils_1.2.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","win-64"],"summary":"Parses http request data in application/json, multipart/form-data, or application/x-www-form-urlencoded format. Includes example of hosting and parsing html form data in R using either 'httpuv' or 'Rhttpd'.","text_prefix":false,"timestamp":1569688857,"version":"1.2.3"},"r-weightedcluster":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://mephisto.unige.ch/weightedcluster","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WeightedCluster/WeightedCluster_2.0.tar.gz","https://cran.r-project.org/src/contrib/WeightedCluster_2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting clusters of state sequences.","text_prefix":false,"timestamp":1665611798,"version":"2.0"},"r-weightit":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ngreifer/WeightIt","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/WeightIt/WeightIt_2.1.0.tar.gz","https://cloud.r-project.org/src/contrib/WeightIt_2.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Generates weights to form equivalent groups in observational studies with point or longitudinal treatments by easing and extending the functionality of the R packages 'twang' for generalized boosted modeling (McCaffrey, Ridgeway & Morral, 2004) <doi:10.1037/1082-989X.9.4.403>, 'CBPS' for covariate balancing propensity score weighting (Imai & Ratkovic, 2014) <doi:10.1111/rssb.12027>, 'ebal' for entropy balancing (Hainmueller, 2012) <doi:10.1093/pan/mpr025>, 'optweight' for optimization-based weights (Zubizarreta, 2015) <doi:10.1080/01621459.2015.1023805>, 'ATE' for empirical balancing calibration weighting (Chan, Yam, & Zhang, 2016) <doi:10.1111/rssb.12129>, and 'SuperLearner' for stacked machine learning-based propensity scores (Pirracchio, Petersen, & van der Laan, 2015) <doi:10.1093/aje/kwu253>. Also allows for assessment of weights and checking of covariate balance by interfacing directly with 'cobalt'.","text_prefix":false,"timestamp":1579908433,"version":"2.1.0"},"r-weightr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://faculty.ucmerced.edu/jvevea/, https://vevealab.shinyapps.io/WeightFunctionModel/, http://faculty.ucmerced.edu/jvevea/other/Vevea_and_Hedges_1995.pdf, http://faculty.ucmerced.edu/jvevea/other/Vevea_and_Woods_Psy_Meth.pdf","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/weightr/weightr_2.0.2.tar.gz","https://cran.r-project.org/src/contrib/weightr_2.0.2.tar.gz"],"subdirs":["noarch"],"summary":"Estimates the Vevea and Hedges (1995) weight-function model. By specifying arguments, users can also estimate the modified model described in Vevea and Woods (2005), which may be more practical with small datasets. Users can also specify moderators to estimate a linear model. The package functionality allows users to easily extract the results of these analyses as R objects for other uses. In addition, the package includes a function to launch both models as a Shiny application. Although the Shiny application is also available online, this function allows users to launch it locally if they choose.","text_prefix":false,"timestamp":1758139590,"version":"2.0.2"},"r-weights":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=weights","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/weights/weights_1.1.2.tar.gz","https://cran.r-project.org/src/contrib/weights_1.1.2.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests.  Also now includes some software for quickly recoding survey data and plotting point estimates from interaction terms in regressions (and multiply imputed regressions). NOTE: Weighted partial correlation calculations pulled to address a bug.","text_prefix":false,"timestamp":1614368788,"version":"1.1.2"},"r-weightsvm":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WeightSVM/WeightSVM_1.7-16.tar.gz","https://cran.r-project.org/src/contrib/WeightSVM_1.7-16.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Functions for subject/instance weighted support vector machines (SVM). It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix.","text_prefix":false,"timestamp":1773521277,"version":"1.7_16"},"r-wellknown":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://docs.ropensci.org/wellknown/, https://github.com/ropensci/wellknown","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wellknown/wellknown_0.7.4.tar.gz","https://cran.r-project.org/src/contrib/wellknown_0.7.4.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Convert 'WKT' to 'GeoJSON' and 'GeoJSON' to 'WKT'. Functions included for converting between 'GeoJSON' to 'WKT', creating both 'GeoJSON' features, and non-features, creating 'WKT' from R objects (e.g., lists, data.frames, vectors), and linting 'WKT'.","text_prefix":false,"timestamp":1609939062,"version":"0.7.4"},"r-wemix":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/American-Institutes-for-Research/WeMix/","home":"https://american-institutes-for-research.github.io/WeMix/","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WeMix/WeMix_4.0.3.tar.gz","https://cran.r-project.org/src/contrib/WeMix_4.0.3.tar.gz"],"subdirs":["noarch"],"summary":"Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels.","text_prefix":false,"timestamp":1638868052,"version":"4.0.3"},"r-wesanderson":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/karthik/wesanderson","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wesanderson/wesanderson_0.3.7.tar.gz","https://cran.r-project.org/src/contrib/wesanderson_0.3.7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Palettes generated mostly from 'Wes Anderson' movies.","text_prefix":false,"timestamp":0,"version":"0.3.7"},"r-wfe":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/insongkim/wfe","home":"https://CRAN.R-project.org/package=wfe","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wfe/wfe_1.9.1.tar.gz","https://cran.r-project.org/src/contrib/wfe_1.9.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) \"When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?\", available at <https://imai.fas.harvard.edu/research/FEmatch.html>.","text_prefix":false,"timestamp":1758632158,"version":"1.9.1"},"r-wheatmap":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/zwdzwd/wheatmap","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wheatmap/wheatmap_0.2.4.tar.gz","https://cran.r-project.org/src/contrib/wheatmap_0.2.4.tar.gz"],"subdirs":["noarch"],"summary":"Builds complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as 'LeftOf', 'RightOf', 'TopOf', and 'Beneath' and more. Other features include clustering, dendrograms and integration with 'ggplot2' generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.","text_prefix":false,"timestamp":1621898133,"version":"0.2.4"},"r-whirl":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://novonordisk-opensource.github.io/whirl/, https://github.com/novonordisk-opensource/whirl","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/whirl/whirl_0.3.3.tar.gz","https://cloud.r-project.org/src/contrib/whirl_0.3.3.tar.gz"],"subdirs":["noarch"],"summary":"Logging of scripts suitable for clinical trials using 'Quarto' to create nice human readable logs. 'whirl' enables execution of scripts in batch, while simultaneously creating logs for the execution of each script, and providing an overview summary log of the entire batch execution.","text_prefix":false,"timestamp":1792315500,"version":"0.3.3"},"r-whisker":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/edwindj/whisker","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/whisker/whisker_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/whisker_0.4.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"logicless templating, reuse templates in many programming languages including R","text_prefix":false,"timestamp":1536411576,"version":"0.4.1"},"r-whitening":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://strimmerlab.org/software/whitening/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/whitening/whitening_1.4.0.tar.gz","https://cran.r-project.org/src/contrib/whitening_1.4.0.tar.gz"],"subdirs":["noarch"],"summary":"Implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) \"Optimal whitening and decorrelation\", <doi:10.1080/00031305.2016.1277159>, as well as the whitening approach to canonical correlation analysis allowing negative canonical correlations described in Jendoubi and Strimmer (2019) \"A whitening approach to probabilistic canonical correlation analysis for omics data integration\", <doi:10.1186/s12859-018-2572-9>.","text_prefix":false,"timestamp":1636315454,"version":"1.4.0"},"r-whoami":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/whoami#readme","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/whoami/whoami_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/whoami_1.3.0.tar.gz"],"subdirs":["linux-64","noarch","win-64"],"summary":"Look up the username and full name of the current user, the current user's email address and 'GitHub' username, using various sources of system and configuration information.","text_prefix":false,"timestamp":1544210770,"version":"1.3.0"},"r-wicket":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/ropensci/wicket","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wicket/wicket_0.4.0.tar.gz","https://cran.r-project.org/src/contrib/wicket_0.4.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Utilities to generate bounding boxes from 'WKT' (Well-Known Text) objects and R data types, validate 'WKT' objects and convert object types from the 'sp' package into 'WKT' representations.","text_prefix":false,"timestamp":1758642001,"version":"0.4.0"},"r-widgetframe":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bhaskarvk/widgetframe, https://bhaskarvk.github.io/widgetframe/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/widgetframe/widgetframe_0.3.1.tar.gz","https://cran.r-project.org/src/contrib/widgetframe_0.3.1.tar.gz"],"subdirs":["noarch"],"summary":"Provides two functions 'frameableWidget()', and 'frameWidget()'. The 'frameableWidget()' is used to add extra code to a 'htmlwidget' which allows is to be rendered correctly inside a responsive 'iframe'. The 'frameWidget()' is a 'htmlwidget' which displays content of another 'htmlwidget' inside a responsive 'iframe'. These functions allow for easier embedding of 'htmlwidgets' in content management systems such as 'wordpress', 'blogger' etc. They also allow for separation of widget content from main HTML content where CSS of the main HTML could interfere with the widget.","text_prefix":false,"timestamp":1758026812,"version":"0.3.1"},"r-widyr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://github.com/dgrtwo/widyr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/widyr/widyr_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/widyr_0.1.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Encapsulates the pattern of untidying data into a wide matrix, performing some processing, then turning it back into a tidy form. This is useful for several operations such as co-occurrence counts, correlations, or clustering that are mathematically convenient on wide matrices.","text_prefix":false,"timestamp":1536531747,"version":"0.1.5"},"r-wiesbaden":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/sumtxt/wiesbaden/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wiesbaden/wiesbaden_1.2.10.tar.gz","https://cran.r-project.org/src/contrib/wiesbaden_1.2.10.tar.gz"],"subdirs":["noarch"],"summary":"Retrieve and import data from different databases of the Federal Statistical Office of Germany (DESTATIS) using their SOAP XML web service <https://www-genesis.destatis.de/>.","text_prefix":false,"timestamp":1665609305,"version":"1.2.10"},"r-wikidataqueryservicer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bearloga/WikidataQueryServiceR","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WikidataQueryServiceR/WikidataQueryServiceR_1.0.0.tar.gz","https://cran.r-project.org/src/contrib/WikidataQueryServiceR_1.0.0.tar.gz"],"subdirs":["noarch"],"summary":"An API client for the 'Wikidata Query Service' <https://query.wikidata.org/>.","text_prefix":false,"timestamp":1758421158,"version":"1.0.0"},"r-wikidatar":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/TS404/WikidataR/issues","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WikidataR/WikidataR_2.3.3.tar.gz","https://cran.r-project.org/src/contrib/WikidataR_2.3.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"An API client for the Wikidata <https://www.wikidata.org/wiki/Wikidata:Main_Page> store of semantic data.","text_prefix":false,"timestamp":1539987018,"version":"2.3.3"},"r-wikipedir":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/Ironholds/WikipediR/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WikipediR/WikipediR_1.7.1.tar.gz","https://cran.r-project.org/src/contrib/WikipediR_1.7.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A wrapper for the MediaWiki API, aimed particularly at the Wikimedia 'production' wikis, such as Wikipedia. It can be used to retrieve page text, information about users or the history of pages, and elements of the category tree.","text_prefix":false,"timestamp":1539914229,"version":"1.7.1"},"r-wikitaxa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/wikitaxa","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wikitaxa/wikitaxa_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/wikitaxa_0.5.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"'Taxonomic' information from 'Wikipedia', 'Wikicommons', 'Wikispecies', and 'Wikidata'. Functions included for getting taxonomic information from each of the sources just listed, as well performing taxonomic search.","text_prefix":false,"timestamp":1540030468,"version":"0.5.0"},"r-withr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://withr.r-lib.org, http://github.com/r-lib/withr#readme","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/withr/withr_3.0.3.tar.gz","https://cran.r-project.org/src/contrib/withr_3.0.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A set of functions to run code 'with' safely and temporarily modified global state. Many of these functions were originally a part of the 'devtools' package, this provides a simple package with limited dependencies to provide access to these functions.","text_prefix":false,"timestamp":1535798290,"version":"3.0.3"},"r-wk":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/paleolimbot/wk","home":"https://paleolimbot.github.io/wk/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wk/wk_0.9.5.tar.gz","https://cran.r-project.org/src/contrib/wk_0.9.5.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provides a minimal R and C++ API for parsing well-known binary and well-known text representation of geometries to and from R-native formats. Well-known binary is compact and fast to parse; well-known text is human-readable and is useful for writing tests. These formats are only useful in R if the information they contain can be accessed in R, for which high-performance functions are provided here.","text_prefix":false,"timestamp":1665648694,"version":"0.9.5"},"r-wkb":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=wkb","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wkb/wkb_0.4-0.tar.gz","https://cran.r-project.org/src/contrib/wkb_0.4-0.tar.gz"],"subdirs":["noarch"],"summary":"Utility functions to convert between the 'Spatial' classes specified by the package 'sp', and the well-known binary '(WKB)' representation for geometry specified by the 'Open Geospatial Consortium'. Supports 'Spatial' objects of class 'SpatialPoints', 'SpatialPointsDataFrame', 'SpatialLines', 'SpatialLinesDataFrame', 'SpatialPolygons', and 'SpatialPolygonsDataFrame'. Supports 'WKB' geometry types 'Point', 'LineString', 'Polygon', 'MultiPoint', 'MultiLineString', and 'MultiPolygon'. Includes extensions to enable creation of maps with 'TIBCO Spotfire'.","text_prefix":false,"timestamp":1758253545,"version":"0.4_0"},"r-wmtsa":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=wmtsa","license":"GPL-2","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wmtsa/wmtsa_2.0-3.tar.gz","https://cran.r-project.org/src/contrib/wmtsa_2.0-3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Software to book Wavelet Methods for Time Series Analysis, Donald B. Percival and Andrew T. Walden, Cambridge University Press, 2000.","text_prefix":false,"timestamp":1539263926,"version":"2.0_3"},"r-word2vec":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/bnosac/word2vec","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/word2vec/word2vec_0.4.1.tar.gz","https://cran.r-project.org/src/contrib/word2vec_0.4.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Learn vector representations of words by continuous bag of words and skip-gram implementations of the 'word2vec' algorithm. The techniques are detailed in the paper \"Distributed Representations of Words and Phrases and their Compositionality\" by Mikolov et al. (2013), available at <arXiv:1310.4546>.","text_prefix":false,"timestamp":1764230572,"version":"0.4.1"},"r-wordcloud":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://blog.fellstat.com/?cat=11 http://www.fellstat.com","license":"LGPL-2.1-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wordcloud/wordcloud_2.6.tar.gz","https://cran.r-project.org/src/contrib/wordcloud_2.6.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Functionality to create pretty word clouds, visualize differences and similarity between documents, and avoid over-plotting in scatter plots with text.","text_prefix":false,"timestamp":1542751088,"version":"2.6"},"r-wordcloud2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/lchiffon/wordcloud2","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wordcloud2/wordcloud2_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/wordcloud2_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"A fast visualization tool for creating wordcloud by using 'wordcloud2.js'. 'wordcloud2.js' is a JavaScript library to create wordle presentation on 2D canvas or HTML <https://timdream.org/wordcloud2.js/>.","text_prefix":false,"timestamp":1758486974,"version":"0.2.1"},"r-wordspace":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://wordspace.r-forge.r-project.org/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wordspace/wordspace_0.2-9.tar.gz","https://cran.r-project.org/src/contrib/wordspace_0.2-9.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"An interactive laboratory for research on distributional semantic models ('DSM', see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).","text_prefix":false,"timestamp":1574159937,"version":"0.2_9"},"r-workflowr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/workflowr/workflowr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/workflowr/workflowr_1.7.2.tar.gz","https://cran.r-project.org/src/contrib/workflowr_1.7.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Provides a workflow for your analysis projects by combining literate programming ('knitr' and 'rmarkdown') and version control ('Git', via 'git2r') to generate a website containing time-stamped, versioned, and documented results.","text_prefix":false,"timestamp":1530597166,"version":"1.7.2"},"r-workflows":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidymodels/workflows","home":"https://workflows.tidymodels.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/workflows/workflows_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/workflows_1.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Managing both a 'parsnip' model and a preprocessor, such as a model formula or recipe from 'recipes', can often be challenging. The goal of 'workflows' is to streamline this process by bundling the model alongside the preprocessor, all within the same object.","text_prefix":false,"timestamp":1582057710,"version":"1.3.0"},"r-workflowsets":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/tidymodels/workflowsets","home":"https://workflowsets.tidymodels.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/workflowsets/workflowsets_1.1.1.tar.gz","https://cran.r-project.org/src/contrib/workflowsets_1.1.1.tar.gz"],"subdirs":["noarch"],"summary":"A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021) <https://www.tmwr.org/>). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results.","text_prefix":false,"timestamp":1626971999,"version":"1.1.1"},"r-worrms":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ropensci/worrms","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/worrms/worrms_0.4.3.tar.gz","https://cran.r-project.org/src/contrib/worrms_0.4.3.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Client for World Register of Marine Species (<http://www.marinespecies.org/>). Includes functions for each of the API methods, including searching for names by name, date and common names, searching using external identifiers, fetching synonyms, as well as fetching taxonomic children and taxonomic classification.","text_prefix":false,"timestamp":0,"version":"0.4.3"},"r-wrapr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/WinVector/wrapr, https://winvector.github.io/wrapr/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wrapr/wrapr_2.1.0.tar.gz","https://cran.r-project.org/src/contrib/wrapr_2.1.0.tar.gz"],"subdirs":["noarch"],"summary":"Tools for writing and debugging R code. Provides: '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return), 'build_frame()'/'draw_frame()' ('data.frame' example tools), 'qc()' (quoting concatenate), ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more.","text_prefix":false,"timestamp":1758273768,"version":"2.1.0"},"r-wrassp":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/IPS-LMU/wrassp","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wrassp/wrassp_1.0.6.tar.gz","https://cran.r-project.org/src/contrib/wrassp_1.0.6.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A wrapper around Michel Scheffers's 'libassp' (<http://libassp.sourceforge.net/>).  The 'libassp' (Advanced Speech Signal Processor) library aims at providing functionality for handling speech signal files in most common audio formats and for performing analyses common in phonetic science/speech science. This includes the calculation of formants, fundamental frequency, root mean square, auto correlation, a variety of spectral analyses, zero crossing rate, filtering etc. This wrapper provides R with a large subset of 'libassp's signal processing functions and provides them to the user in a (hopefully) user-friendly manner.","text_prefix":false,"timestamp":1665606997,"version":"1.0.6"},"r-writexl":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/ropensci/writexl","doc_url":"https://docs.ropensci.org/writexl/","home":"https://libxlsxwriter.github.io","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/writexl/writexl_2.0.1.tar.gz","https://cloud.r-project.org/src/contrib/writexl_2.0.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter'.  Fast and no Java or Excel required.","text_prefix":false,"timestamp":1666106951,"version":"2.0.1"},"r-writexls":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/marcschwartz/WriteXLS","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WriteXLS/WriteXLS_6.8.0.tar.gz","https://cran.r-project.org/src/contrib/WriteXLS_6.8.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX) files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains or can be specified by the user.","text_prefix":false,"timestamp":1539225257,"version":"6.8.0"},"r-wrmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=wrMisc","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wrMisc/wrMisc_2.1.1.tar.gz","https://cran.r-project.org/src/contrib/wrMisc_2.1.1.tar.gz"],"subdirs":["noarch"],"summary":"The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.","text_prefix":false,"timestamp":1784622742,"version":"2.1.1"},"r-wrs2":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://r-forge.r-project.org/projects/psychor/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/WRS2/WRS2_1.1-7.tar.gz","https://cran.r-project.org/src/contrib/WRS2_1.1-7.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.","text_prefix":false,"timestamp":0,"version":"1.1_7"},"r-wrswor":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/krlmlr/wrswoR/","doc_url":"https://krlmlr.github.io/wrswoR/reference/index.html","home":"https://krlmlr.github.io/wrswoR/","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wrswoR/wrswoR_1.2.1.tar.gz","https://cran.r-project.org/src/contrib/wrswoR_1.2.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"A collection of implementations of classical and novel algorithms for weighted sampling without replacement.","text_prefix":false,"timestamp":1778659089,"version":"1.2.1"},"r-wto":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=wTO","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/wTO/wTO_2.1.tar.gz","https://cran.r-project.org/src/contrib/wTO_2.1.tar.gz"],"subdirs":["noarch"],"summary":"Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). It also computes the cut-off threshold or p-value based on the individuals bootstrap or the values reshuffle per individual. It also allows the construction of a consensus network, based on multiple wTO networks. The package includes a visualization tool for the networks.  More about the methodology can be found at <arXiv:1711.04702>.","text_prefix":false,"timestamp":1652249377,"version":"2.1"},"r-x13binary":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=x13binary","license":"GPL-2.0-or-later AND LicenseRef-X13AS","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/x13binary/x13binary_1.1.61.2.tar.gz","https://cran.r-project.org/src/contrib/x13binary_1.1.61.2.tar.gz"],"subdirs":["noarch"],"summary":"The US Census Bureau provides a seasonal adjustment program now called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions -- which this package integrates for use by other R packages.","text_prefix":false,"timestamp":1628793322,"version":"1.1.61.2"},"r-xaringan":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/yihui/xaringan","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xaringan/xaringan_0.31.tar.gz","https://cran.r-project.org/src/contrib/xaringan_0.31.tar.gz"],"subdirs":["noarch"],"summary":"Create HTML5 slides with R Markdown and the JavaScript library 'remark.js' (<https://remarkjs.com>).","text_prefix":false,"timestamp":1655216153,"version":"0.31"},"r-xaringanthemer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pkg.garrickadenbuie.com/xaringanthemer/, https://github.com/gadenbuie/xaringanthemer","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xaringanthemer/xaringanthemer_0.4.4.tar.gz","https://cran.r-project.org/src/contrib/xaringanthemer_0.4.4.tar.gz"],"subdirs":["noarch"],"summary":"Create beautifully color-coordinated and customized themes for your 'xaringan' slides, without writing any CSS. Complete your slide theme with 'ggplot2' themes that match the font and colors used in your slides.  Customized styles can be created directly in your slides' 'R Markdown' source file or in a separate external script.","text_prefix":false,"timestamp":1637526247,"version":"0.4.4"},"r-xefun":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/ShichenXie/xefun","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xefun/xefun_0.1.5.tar.gz","https://cran.r-project.org/src/contrib/xefun_0.1.5.tar.gz"],"subdirs":["noarch"],"summary":"Miscellaneous functions used for x-engineering (feature engineering) or for supporting in other packages maintained by 'Shichen Xie'.","text_prefix":false,"timestamp":1757945510,"version":"0.1.5"},"r-xfun":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/yihui/xfun","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/xfun/xfun_0.61.tar.gz","https://cloud.r-project.org/src/contrib/xfun_0.61.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.","text_prefix":false,"timestamp":1652229293,"version":"0.61"},"r-xgboost":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. 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Runs on single machine, Hadoop, Spark, Flink and DataFlow","text_prefix":false,"timestamp":1667416221,"version":"3.4.2"},"r-xgboost-cpu":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.","dev_url":"https://github.com/dmlc/xgboost/","doc_url":"https://xgboost.readthedocs.io/","home":"https://github.com/dmlc/xgboost","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_git_url":"https://github.com/dmlc/xgboost","source_url":"https://github.com/dmlc/xgboost/releases/download/v2.1.4/xgboost-2.1.4.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow","text_prefix":false,"timestamp":1666903252,"version":"2.1.4"},"r-xgboost-gpu":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.","dev_url":"https://github.com/dmlc/xgboost/","doc_url":"https://xgboost.readthedocs.io/","home":"https://github.com/dmlc/xgboost","license":"Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_git_url":"https://github.com/dmlc/xgboost","source_url":"https://github.com/dmlc/xgboost/releases/download/v2.1.4/xgboost-2.1.4.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","win-64"],"summary":"Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow","text_prefix":false,"timestamp":1744334307,"version":"2.1.4"},"r-xgxr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/Novartis/xgxr","home":"https://opensource.nibr.com/xgx/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xgxr/xgxr_1.1.6.tar.gz","https://cran.r-project.org/src/contrib/xgxr_1.1.6.tar.gz"],"subdirs":["noarch"],"summary":"Supports a structured approach for exploring PKPD data <https://opensource.nibr.com/xgx/>. It also contains helper functions for enabling the modeler to follow best R practices (by appending the program name, figure name location, and draft status to each plot). 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The reference for the methods implemented here is Chatterjee, Sourav (2020) <arXiv:1909.10140> This package includes the Galton peas example.","text_prefix":false,"timestamp":1665539040,"version":"0.4.1"},"r-xlconnect":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.mirai-solutions.com https://github.com/miraisolutions/xlconnect","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/XLConnect/XLConnect_1.3.0.tar.gz","https://cran.r-project.org/src/contrib/XLConnect_1.3.0.tar.gz"],"subdirs":["noarch"],"summary":"Provides comprehensive functionality to read, write and format Excel data.","text_prefix":false,"timestamp":1597117339,"version":"1.3.0"},"r-xlconnectjars":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://www.mirai-solutions.com https://github.com/miraisolutions/xlconnectjars","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/XLConnectJars/XLConnectJars_0.2-15.tar.gz","https://cran.r-project.org/src/contrib/XLConnectJars_0.2-15.tar.gz"],"subdirs":["noarch"],"summary":"Provides external JAR dependencies for the XLConnect package.","text_prefix":false,"timestamp":1758608001,"version":"0.2_15"},"r-xlsx":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/dragua/rexcel, http://groups.google.com/group/R-package-xlsx","license":"GPL-3","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xlsx/xlsx_0.6.5.tar.gz","https://cran.r-project.org/src/contrib/xlsx_0.6.5.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.","text_prefix":false,"timestamp":0,"version":"0.6.5"},"r-xlsxjars":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=xlsxjars","license":"GPL-3.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xlsxjars/xlsxjars_0.9.0.tar.gz","https://cran.r-project.org/src/contrib/xlsxjars_0.9.0.tar.gz"],"subdirs":["linux-64","noarch","osx-64"],"summary":"The xlsxjars package collects all the external jars required for the xlxs package. This release corresponds to POI 3.10.1.","text_prefix":false,"timestamp":0,"version":"0.9.0"},"r-xmisc":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://CRAN.R-project.org/package=Xmisc","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/Xmisc/Xmisc_0.2.1.tar.gz","https://cran.r-project.org/src/contrib/Xmisc_0.2.1.tar.gz"],"subdirs":["noarch"],"summary":"This is Xiaobei's miscellaneous classes and functions useful when developing R packages, particularly for OOP using R Reference Class.","text_prefix":false,"timestamp":1687035251,"version":"0.2.1"},"r-xml":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"http://www.omegahat.net/RSXML","license":"BSD-2-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/XML/XML_3.99-0.25.tar.gz","https://cloud.r-project.org/src/contrib/XML_3.99-0.25.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Many approaches for both reading and creating XML (and HTML) documents (including DTDs), both local and accessible via HTTP or FTP.  Also offers access to an 'XPath' \"interpreter\".","text_prefix":true,"timestamp":1554794447,"version":"3.99_0.25"},"r-xml2":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/r-lib/xml2","home":"https://xml2.r-lib.org/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xml2/xml2_1.6.0.tar.gz","https://cran.r-project.org/src/contrib/xml2_1.6.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Work with XML files using a simple, consistent interface. Built on top of the 'libxml2' C library.","text_prefix":false,"timestamp":1583913903,"version":"1.6.0"},"r-xml2r":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/cpsievert/XML2R","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/XML2R/XML2R_0.0.9.tar.gz","https://cran.r-project.org/src/contrib/XML2R_0.0.9.tar.gz"],"subdirs":["noarch"],"summary":"XML2R is a framework that reduces the effort required to transform XML content into number of tables while preserving parent to child relationships.","text_prefix":false,"timestamp":1758458578,"version":"0.0.9"},"r-xmlparsedata":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/xmlparsedata#readme","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xmlparsedata/xmlparsedata_1.0.5.tar.gz","https://cran.r-project.org/src/contrib/xmlparsedata_1.0.5.tar.gz"],"subdirs":["noarch"],"summary":"Convert the output of 'utils::getParseData()' to an 'XML' tree, that one can search via 'XPath', and easier to manipulate in general.","text_prefix":false,"timestamp":1588559483,"version":"1.0.5"},"r-xmlschema":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/omegahat/XMLSchema/archive/0eaa22b30a240fd81a975d7de4e6e9131fecf05b.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"R package to work with XML Schema","text_prefix":false,"timestamp":0,"version":"0.8"},"r-xnomial":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=XNomial","license":"GPL","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/XNomial/XNomial_1.0.4.1.tar.gz","https://cran.r-project.org/src/contrib/XNomial_1.0.4.1.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tests whether a set of counts fit a given expected ratio. For example, a genetic cross might be expected to produce four types in the relative frequencies of 9:3:3:1. To see whether a set of observed counts fits this expectation, one can examine all possible outcomes with xmulti() or a random sample of them with xmonte() and find the probability of an observation deviating from the expectation by at least as much as the observed. As a measure of deviation from the expected, one can use the log-likelihood ratio, the multinomial probability, or the classic chi-square statistic. A histogram of the test statistic can also be plotted and compared with the asymptotic curve.","text_prefix":false,"timestamp":1758737230,"version":"1.0.4.1"},"r-xopen":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/r-lib/xopen#readme","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xopen/xopen_1.0.1.tar.gz","https://cran.r-project.org/src/contrib/xopen_1.0.1.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Cross platform solution to open files, directories or 'URLs' with their associated programs.","text_prefix":false,"timestamp":1540852883,"version":"1.0.1"},"r-xplorerr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/rsquaredacademy/xplorerr","home":"https://xplorerr.rsquaredacademy.com/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xplorerr/xplorerr_0.2.0.tar.gz","https://cran.r-project.org/src/contrib/xplorerr_0.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Tools for interactive data exploration built using 'shiny'. Includes apps for descriptive statistics, visualizing probability distributions, inferential statistics, linear regression, logistic regression and RFM analysis.","text_prefix":false,"timestamp":1758181320,"version":"0.2.0"},"r-xportr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://atorus-research.github.io/xportr/, https://github.com/atorus-research/xportr","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xportr/xportr_0.5.0.tar.gz","https://cran.r-project.org/src/contrib/xportr_0.5.0.tar.gz"],"subdirs":["noarch"],"summary":"Tools to build CDISC compliant data sets and check for CDISC compliance.","text_prefix":false,"timestamp":1768369651,"version":"0.5.0"},"r-xptr":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://randy3k.github.io/xptr/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xptr/xptr_1.2.0.tar.gz","https://cran.r-project.org/src/contrib/xptr_1.2.0.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"There is limited native support for external pointers in the R interface. This package provides some basic tools to verify, create and modify 'externalptr' objects.","text_prefix":false,"timestamp":1576184226,"version":"1.2.0"},"r-xroi":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://CRAN.R-project.org/package=xROI","license":"AGPL-3.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xROI/xROI_0.9.20.tar.gz","https://cran.r-project.org/src/contrib/xROI_0.9.20.tar.gz"],"subdirs":["noarch"],"summary":"Digital repeat photography and near-surface remote sensing have been used by environmental scientists to study the environmental change for nearly a decade. However, a user-friendly, reliable, and robust platform to extract color-based statistics and time-series from a large stack of images is still lacking. Here, we present an interactive open-source toolkit, called 'xROI', that facilitate the process time-series extraction and improve the quality of the final data. 'xROI' provides a responsive environment for scientists to interactively a) delineate regions of interest (ROI), b) handle field of view (FOV) shifts, and c) extract and export time series data characterizing image color (i.e. red, green and blue channel digital numbers for the defined ROI). Using 'xROI', user can detect FOV shifts without minimal difficulty. The software gives user the opportunity to readjust the mask files or redraw new ones every time an FOV shift occurs. 'xROI' helps to significantly improve data accuracy and continuity.","text_prefix":false,"timestamp":1687035331,"version":"0.9.20"},"r-xslt":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_url":"https://docs.ropensci.org/xslt/","home":"https://ropensci.r-universe.dev/xslt","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xslt/xslt_1.5.1.tar.gz","https://cran.r-project.org/src/contrib/xslt_1.5.1.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"An extension for the 'xml2' package to transform XML documents by applying an 'xslt' style-sheet.","text_prefix":false,"timestamp":1757849317,"version":"1.5.1"},"r-xtable":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"http://xtable.r-forge.r-project.org/","license":"GPL (>= 2)","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xtable/xtable_1.8-8.tar.gz","https://cran.r-project.org/src/contrib/xtable_1.8-8.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Coerce data to LaTeX and HTML tables.","text_prefix":false,"timestamp":0,"version":"1.8_8"},"r-xtensor":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"Multi dimensional arrays with broadcasting and lazy computing","dev_url":"https://github.com/xtensor-stack/Xtensor.R","doc_url":"https://xtensor-r.readthedocs.io/","home":"http://github.com/xtensor-stack/Xtensor.R","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/xtensor-stack/Xtensor.R/archive/0.16.0.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"R bindings for xtensor, the C++ tensor algebra library","text_prefix":false,"timestamp":1550873116,"version":"0.16.0"},"r-xtractomatic":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/rmendels/xtractomatic","license":"CC0-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/xtractomatic/xtractomatic_3.4.2.tar.gz","https://cran.r-project.org/src/contrib/xtractomatic_3.4.2.tar.gz"],"subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Contains three functions that access environmental data from ERD's ERDDAP service <http://coastwatch.pfeg.noaa.gov/erddap>. The xtracto() function extracts data along a trajectory for a given \"radius\" around the point. The xtracto_3D() function extracts data in a box. The xtractogon() function extracts data in a polygon. There are also two helper functions to obtain information about available data, and plotting functions to plot the results.","text_prefix":false,"timestamp":0,"version":"3.4.2"},"r-xts":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/joshuaulrich/xts","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cloud.r-project.org/src/contrib/Archive/xts/xts_0.14.3.tar.gz","https://cloud.r-project.org/src/contrib/xts_0.14.3.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.","text_prefix":false,"timestamp":0,"version":"0.14.3"},"r-yaimpute":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/jeffreyevans/yaImpute","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/yaImpute/yaImpute_1.0-36.tar.gz","https://cran.r-project.org/src/contrib/yaImpute_1.0-36.tar.gz"],"subdirs":["linux-64","osx-64","win-64"],"summary":"Performs nearest neighbor-based imputation using one or more alternative  approaches to processing multivariate data. These include methods based on canonical  correlation analysis, canonical correspondence analysis, and a multivariate adaptation  of the random forest classification and regression techniques of Leo Breiman and Adele  Cutler. Additional methods are also offered. The package includes functions for  comparing the results from running alternative techniques, detecting imputation targets  that are notably distant from reference observations, detecting and correcting  for bias, bootstrapping and building ensemble imputations, and mapping results.","text_prefix":false,"timestamp":1563517122,"version":"1.0_36"},"r-yaml":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"home":"https://github.com/viking/r-yaml/","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/yaml/yaml_2.3.12.tar.gz","https://cran.r-project.org/src/contrib/yaml_2.3.12.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Implements the 'libyaml' 'YAML' 1.1 parser and emitter (<http://pyyaml.org/wiki/LibYAML>) for R.","text_prefix":false,"timestamp":1666103469,"version":"2.3.12"},"r-yardstick":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"dev_url":"https://github.com/tidymodels/yardstick","home":"https://yardstick.tidymodels.org","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/yardstick/yardstick_1.4.0.tar.gz","https://cran.r-project.org/src/contrib/yardstick_1.4.0.tar.gz"],"subdirs":["linux-64","linux-aarch64","linux-ppc64le","noarch","osx-64","osx-arm64","win-64"],"summary":"Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).","text_prefix":false,"timestamp":1654586962,"version":"1.4.0"},"r-yarrr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"www.thepiratesguidetor.com","license":"GPL-2.0-only","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":["https://cran.r-project.org/src/contrib/Archive/yarrr/yarrr_0.1.14.tar.gz","https://cran.r-project.org/src/contrib/yarrr_0.1.14.tar.gz"],"subdirs":["noarch"],"summary":"Contains a mixture of functions and data sets referred to in the introductory e-book \"YaRrr!: The Pirate's Guide to R\". 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Installing the Lime Suite enables many SDR applications such as GQRX to work with supported hardware through the bundled SoapySDR support module.  The `liblimesuite` package contains the main library that other packages should develop against. The `soapysdr-module-lms7` package contains the Soapy SDR module that supports Lime devices. The `limesuite` package provides the entire software suite, depending on the previous packages and also providing GUI tools.  For Windows users of `liblimesuite` with a LimeSDR (not mini!) device, this package uses libusb to communicate over USB instead of the standard CyUSB library which is not open source. If you have used your LimeSDR with another software package, you will have to switch USB drivers to one compatible with WinUSB/libusb by installing the WinUSB driver with Zadig (https://zadig.akeo.ie/) and selecting your Lime device.  For Linux users of `liblimesuite`, you will likely want to link the provided udev rule into your system installation in order for the hardware to have the correct permissions:      sudo ln -s $CONDA_PREFIX/lib/udev/rules.d/64-limesuite.rules /etc/udev/rules.d/     sudo udevadm control --reload     sudo udevadm trigger","dev_url":"https://github.com/myriadrf/LimeSuite","doc_url":"https://wiki.myriadrf.org/Lime_Suite","home":"http://wiki.myriadrf.org/Lime_Suite","license":"Apache-2.0 AND MIT AND BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/myriadrf/LimeSuite/archive/v23.11.0.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Driver and GUI for LMS7002M-based (Lime) SDR platforms","text_prefix":false,"timestamp":1662786761,"version":"23.11.0"},"soapysdr-module-netsdr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The Soapy NetSDR project provides a plugin module to use the NetSDR devices within the SoapySDR API and software that supports SoapySDR.","dev_url":"https://github.com/pothosware/SoapyNetSDR","doc_url":"https://github.com/pothosware/SoapyNetSDR/wiki","home":"https://github.com/pothosware/SoapyNetSDR/wiki","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/pothosware/SoapyNetSDR/archive/soapy-netsdr-0.2.0.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"SoapySDR Support Module for NetSDR protocol","text_prefix":false,"timestamp":1676659896,"version":"0.2.0"},"soapysdr-module-plutosdr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The Soapy Pluto SDR project provides a plugin module to use the Pluto SDR device within the SoapySDR API and software that supports SoapySDR.","dev_url":"https://github.com/pothosware/SoapyPlutoSDR","doc_url":"https://github.com/pothosware/SoapyPlutoSDR/wiki","home":"https://github.com/pothosware/SoapyPlutoSDR/wiki","license":"LGPL-2.1-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.2.0":{"weak":["soapysdr-module-plutosdr >=0.2.0,<0.3.0a0"]},"0.2.1":{"weak":["soapysdr-module-plutosdr >=0.2.1,<0.3.0a0"]},"0.2.1.post20230724":{"weak":["soapysdr-module-plutosdr >=0.2.1.0inf.20230724,<0.3.0a0"]},"0.2.2":{"weak":["soapysdr-module-plutosdr >=0.2.2,<0.3.0a0"]},"0.2.3":{"weak":["soapysdr-module-plutosdr >=0.2.3,<0.3.0a0"]}},"source_url":"https://github.com/pothosware/SoapyPlutoSDR/archive/refs/tags/soapy-plutosdr-0.2.3.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Soapy SDR plugin for PlutoSDR","text_prefix":false,"timestamp":1637688737,"version":"0.2.3"},"soapysdr-module-redpitaya":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The Soapy Red Pitaya project provides a plugin module to use the Red Pitaya device within the SoapySDR API and software that supports SoapySDR.","dev_url":"https://github.com/pothosware/SoapyRedPitaya","doc_url":"https://github.com/pothosware/SoapyRedPitaya/wiki","home":"https://github.com/pothosware/SoapyRedPitaya/wiki","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/pothosware/SoapyRedPitaya/archive/soapy-redpitaya-0.1.2.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"SoapySDR RedPitaya Support Module","text_prefix":false,"timestamp":1791658411,"version":"0.1.2"},"soapysdr-module-remote":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Use any SoapySDR supported device transparently over a local network link. The remote support feature can turn any SDR into a network peripheral.","dev_url":"https://github.com/pothosware/SoapyRemote","doc_url":"https://github.com/pothosware/SoapyRemote/wiki","home":"https://github.com/pothosware/SoapyRemote/wiki","license":"BSL-1.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.5.2":{"weak":["soapysdr-module-remote >=0.5.2,<0.6.0a0"]}},"source_url":"https://github.com/pothosware/SoapyRemote/archive/soapy-remote-0.5.2.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"SoapySDR remote module, use any Soapy SDR remotely","text_prefix":true,"timestamp":1619636653,"version":"0.5.2"},"soapysdr-module-rtlsdr":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"The Soapy RTL-SDR project provides a plugin module to use the RTL-SDR dongle within the SoapySDR API and software that supports SoapySDR.","dev_url":"https://github.com/pothosware/SoapyRTLSDR","doc_url":"https://github.com/pothosware/SoapyRTLSDR/wiki","home":"https://github.com/pothosware/SoapyRTLSDR/wiki","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.3.0":{"weak":["soapysdr-module-rtlsdr >=0.3.0,<0.4.0a0"]},"0.3.3":{"weak":["soapysdr-module-rtlsdr >=0.3.3,<0.4.0a0"]}},"source_url":"https://github.com/pothosware/SoapyRTLSDR/archive/refs/tags/soapy-rtl-sdr-0.3.3.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"SoapySDR RTL-SDR Support Module","text_prefix":false,"timestamp":1619636714,"version":"0.3.3"},"soapysdr-module-uhd":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"The Soapy UHD project provides a plugin module to use UHD devices within the SoapySDR API and software that supports SoapySDR. In addition, the project provides a UHD module to use any SoapySDR device within the UHD API and UHD supported software.","dev_url":"https://github.com/pothosware/SoapyUHD","doc_url":"https://github.com/pothosware/SoapyUHD/wiki","home":"https://github.com/pothosware/SoapyUHD/wiki","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"0.3.6":{"weak":["soapysdr-module-uhd >=0.3.6,<0.4.0a0"]},"0.4.1":{"weak":["soapysdr-module-uhd >=0.4.1,<0.5.0a0"]}},"source_url":"https://github.com/pothosware/SoapyUHD/archive/soapy-uhd-0.4.1.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"Soapy SDR plugin for UHD supported SDR devices","text_prefix":false,"timestamp":1591771103,"version":"0.4.1"},"soapysdr-module-volk-converters":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"This Soapy module adds vectorized numeric conversion functions, providing more efficient conversions in many cases than with stock SoapySDR. Once this module is installed, nothing more needs to be done; Soapy modules that use SoapySDR's converter infrastructure will automatically default to these new converters.","dev_url":"https://github.com/pothosware/SoapyVOLKConverters","doc_url":"https://github.com/pothosware/SoapyVOLKConverters/wiki","home":"https://github.com/pothosware/SoapyVOLKConverters/wiki","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/pothosware/SoapyVOLKConverters/archive/soapy-volk-converters-0.1.1.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"SoapySDR module that registers type converters implemented in VOLK","text_prefix":false,"timestamp":1772743764,"version":"0.1.1"},"soba_reference_repo":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://pypi.org/project/soba-reference-repo/","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["noarch"],"summary":"Recipe-driven SOBA reference TEST/TARGET dataset exporter","text_prefix":false,"timestamp":1790867736,"version":"0.2.0"},"sober":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"dev_url":"https://github.com/airallergy/sober","doc_url":"https://airallergy.github.io/sober","home":"https://github.com/airallergy/sober","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/s/sober/sober-0.4.0.tar.gz","subdirs":["noarch"],"summary":"sober optimises built environment robustly","text_prefix":false,"timestamp":1781524242,"version":"0.4.1"},"socat":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"description":"socat is a relay for bidirectional data transfer between two independent data channels. Each of these data channels may be a file, pipe, device (serial line etc. or a pseudo terminal), a socket (UNIX, IP4, IP6 - raw, UDP, TCP), an SSL socket, proxy CONNECT connection, a file descriptor (stdin etc.), the GNU line editor (readline), a program, or a combination of two of these. These modes include generation of \"listening\" sockets, named pipes, and pseudo terminals.  socat can be used, e.g., as TCP port forwarder (one-shot or daemon), as an external socksifier, for attacking weak firewalls, as a shell interface to UNIX sockets, IP6 relay, for redirecting TCP oriented programs to a serial line, to logically connect serial lines on different computers, or to establish a relatively secure environment (su and  chroot) for running client or server shell scripts with network connections.","dev_url":"http://repo.or.cz/r/socat.git","doc_url":"http://www.dest-unreach.org/socat/doc/README","home":"http://www.dest-unreach.org/socat/","license":"GPL-2.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"http://www.dest-unreach.org/socat/download/socat-1.8.1.3.tar.bz2","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64"],"summary":"A relay for bidirectional transfer between data channels (netcat++)","text_prefix":false,"timestamp":1646760904,"version":"1.8.1.3"},"soci-core":{"activate.d":false,"binary_prefix":true,"deactivate.d":false,"doc_source_url":"http://soci.sourceforge.net/doc/release/4.0/","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{"4.0.1":{"weak":["soci-core >=4.0.1,<4.1.0a0"]},"4.0.2":{"weak":["soci-core >=4.0.2,<4.1.0a0"]},"4.0.3":{"weak":["soci-core >=4.0.3,<4.1.0a0"]},"4.1.3":{"weak":["soci-core >=4.1.3,<4.2.0a0"]},"4.1.4":{"weak":["soci-core >=4.1.4,<4.2.0a0"]}},"source_url":"https://github.com/SOCI/soci/archive/v4.1.4.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"SOCI is a general purpose database written in C++.","text_prefix":true,"timestamp":1622482796,"version":"4.1.4"},"soci-mysql":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_source_url":"http://soci.sourceforge.net/doc/release/4.0/","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SOCI/soci/archive/v4.1.4.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"soci-mysql contains SOCI core and the implementation of MySQL backend.","text_prefix":false,"timestamp":1614495867,"version":"4.1.4"},"soci-postgresql":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_source_url":"http://soci.sourceforge.net/doc/release/4.0/","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SOCI/soci/archive/v4.1.4.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64"],"summary":"soci-postgresql contains SOCI core and the implementation of PostgreSQL backend.","text_prefix":false,"timestamp":1782225461,"version":"4.1.4"},"soci-sqlite":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"doc_source_url":"http://soci.sourceforge.net/doc/release/4.0/","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://github.com/SOCI/soci/archive/v4.1.4.tar.gz","subdirs":["linux-64","linux-aarch64","linux-ppc64le","osx-64","osx-arm64","win-64"],"summary":"soci-sqlite3 contains SOCI core and the implementation of SQLite3 backend.","text_prefix":false,"timestamp":1622482893,"version":"4.1.4"},"social-analyzer":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"Social Analyzer - API, CLI & Web App for analyzing & finding a person's profile across +900 social media \\ websites. It includes different string analysis and detection modules, you can choose which combination of modules to use during the investigation process.","dev_url":"https://github.com/qeeqbox/social-analyzer","doc_url":"https://github.com/qeeqbox/social-analyzer/wiki","home":"https://github.com/qeeqbox/social-analyzer","license":"AGPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://files.pythonhosted.org/packages/source/s/social-analyzer/social-analyzer-0.45.tar.gz","subdirs":["linux-64","osx-64","win-64"],"summary":"API, CLI & Web App for analyzing & finding a persons profile across +900 social media \\ websites (Detections are updated regularly)","text_prefix":true,"timestamp":1645036820,"version":"0.45"},"social-auth-app-django":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/python-social-auth/social-app-django","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/s/social-auth-app-django/social_auth_app_django-6.0.1.tar.gz","subdirs":["noarch"],"summary":"Python Social Authentication, Django integration.","text_prefix":false,"timestamp":1540768807,"version":"7.1.0"},"social-auth-core":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/python-social-auth/social-core","license":"BSD-3-Clause","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/s/social-auth-core/social_auth_core-5.1.0.tar.gz","subdirs":["linux-64","noarch","osx-64","win-64"],"summary":"Python social authentication made simple.","text_prefix":false,"timestamp":1602633635,"version":"6.1.1"},"socketify":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"home":"https://github.com/cirospaciari/socketify.py","license":"MIT AND Apache-2.0","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"subdirs":["linux-64","osx-64"],"summary":"Bringing WebSockets, Http/Https High Performance servers for PyPy3 and Python3","text_prefix":false,"timestamp":1788732276,"version":"0.0.31"},"socketio-client-nexus":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"A socket.io 2.x client library for Python","dev_url":"https://github.com/nexus-devs/socketIO-client-2.0.3","home":"https://github.com/nexus-devs/socketIO-client-2.0.3","license":"MIT","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/s/socketIO-client-nexus/socketIO-client-nexus-0.7.6.tar.gz","subdirs":["noarch"],"summary":"A socket.io 2.x client library for Python","text_prefix":false,"timestamp":1735184934,"version":"0.7.6"},"sockio":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"A python concurrency agnostic socket library.","dev_url":"https://github.com/tiagocoutinho/sockio/","doc_url":"https://tiagocoutinho.github.io/sockio/","home":"https://tiagocoutinho.github.io/sockio/","license":"GPL-3.0-or-later","post_link":false,"pre_link":false,"pre_unlink":false,"run_exports":{},"source_url":"https://pypi.org/packages/source/s/sockio/sockio-0.15.0.tar.gz","subdirs":["noarch"],"summary":"Concurrency agnostic socket API","text_prefix":false,"timestamp":1619023376,"version":"0.15.0"},"sockjs-tornado":{"activate.d":false,"binary_prefix":false,"deactivate.d":false,"description":"SockJS-tornado is a Python server side counterpart of SockJS-client browser library running on top of Tornado 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