This package provides 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.

This package provides 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.

rchitect provides access to R functionality from Python. Its main use is as the driver for radian, the R console.

The package allows one to compose general HTTP requests and provides convenient functions to fetch URIs, GET and POST forms, etc. and process the results returned by the Web server. This provides a great deal of control over the HTTP/FTP/... connection and the form of the request while providing a higher-level interface than is available just using R socket connections. Additionally, the underlying implementation is robust and extensive, supporting FTP/FTPS/TFTP (uploads and downloads), SSL/HTTPS, telnet, dict, ldap, and also supports cookies, redirects, authentication, etc.

Armadillo is a templated C++ linear algebra library 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. This package includes the header files from the templated Armadillo library.

This package provides the R math library as an independent package.

This package contains a set of functions for working with Random Number Generators (RNGs). In particular, it defines a generic S4 framework 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.

JDistlib is the Java Statistical Distribution Library, a Java package that provides routines for various statistical distributions.

This package lets you calculate power for generalized linear mixed models, using simulation. It was designed to work with models fit using the lme4 package. The package is described in Green and MacLeod (2016).

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.

This package 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. Provides approximate, exact searches, fixed radius searches, bd and kb trees.

This package analyzes data with robust methods such as regression methodology including model selections and multivariate statistics.

This package provides a collection of datasets used in Vega and Vega-Lite examples.

This package implements importance sampling from the truncated multivariate normal using the Geweke-Hajivassiliou-Keane (GHK) simulator. Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions for multivariate normals.

This package provides a tbl_df class that offers better checking and printing capabilities than traditional data frames.

Emacs Speaks Statistics (ESS) is an add-on package for GNU Emacs. It is designed to support editing of scripts and interaction with various statistical analysis programs such as R, Julia, and JAGS.

This package provides an implementation of Nested Sampling algorithms for evaluating Bayesian evidence.

This package provides an implementation of robust location and scatter estimation and robust multivariate analysis with high breakdown point.

This package provides an implementation of the Ensemble Slice Sampling method. Features:

• fast & Robust Bayesian Inference

• efficient Markov Chain Monte Carlo (MCMC)

• black-box inference, no hand-tuning

• excellent performance in terms of autocorrelation time and convergence rate

• scale to multiple CPUs without any extra effort

• automated Convergence diagnostics

PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms.

This package provides an implementation of the Language Server Protocol for R. The Language Server protocol is used by an editor client to integrate features like auto completion.

This package provides a library for Probabilistic Graphical Models. It can be used for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

libxls is a C library to read .xls spreadsheet files in the binary OLE BIFF8 format as created by Excel 97 and later versions. It cannot write them.

This package also provides xls2csv to export Excel files to CSV.

This package displays a progress bar in the R console for long running computations taking place in C++ code, and support for interrupting those computations even in multithreaded code, typically using OpenMP.