R is a language and environment for statistical computing and graphics. It provides a variety of statistical techniques, such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification and clustering. It also provides robust support for producing publication-quality data plots. A large amount of 3rd-party packages are available, greatly increasing its breadth and scope.

This package provides a collection of algorithms and functions to aid statistical modeling. It 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. It also includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families.

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

This package provides 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 package provides a pure R implementation of the t-SNE algorithm.

ArviZ is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.

This package provides functionalities to build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t family, and provides related statistical methods for data fitting and diagnostics, in the univariate and the multivariate case.

OpenTURNS is a scientific C++ and Python library including an internal data model and algorithms dedicated to the treatment of uncertainties. The main goal of this library is giving to specific applications all the functionalities needed to treat uncertainties in studies.

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

XLISP-STAT is a statistical environment based on a Lisp dialect called XLISP. To facilitate statistical computations, standard functions for addition, logarithms, etc., have been modified to operate on lists and arrays of numbers, and a number of basic statistical functions have been added. Many of these functions have been written in Lisp, and additional functions can be added easily by a user. Several basic forms of plots, including histograms, scatterplots, rotatable plots and scatterplot matrices are provided. These plots support various forms of interactive highlighting operations and can be linked so points highlighted in one plot will be highlighted in all linked plots. Interactions with the plots are controlled by the mouse, menus and dialog boxes. An object-oriented programming system is used to allow menus, dialogs, and the response to mouse actions to be customized.

This package provides a toolbox for working with base types, core R features like the condition system, and core Tidyverse features like tidy evaluation.

This package provides useful utilities from Seminar fuer Statistik ETH Zurich, including many that are related to graphics.

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.

This is a package for Non-Negative Linear Models (NNLM). It implements fast sequential coordinate descent algorithms for non-negative linear regression and non-negative matrix factorization (NMF). It supports mean square error and Kullback-Leibler divergence loss. Many other features are also implemented, including missing value imputation, domain knowledge integration, designable W and H matrices and multiple forms of regularizations.

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.

JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind:

1. To have a cross-platform engine for the BUGS language;

2. To be extensible, allowing users to write their own functions, distributions and samplers;

3. To be a platform for experimentation with ideas in Bayesian modelling.

George is a fast and flexible Python library for Gaussian Process (GP) Regression, focused on efficiently evaluating the marginalized likelihood of a dataset under a GP prior, even as this dataset gets Big.

This package implements a Dynamic Nested Sampling for computing Bayesian posteriors and evidences.

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.

This package provides methods for caching or memoization of objects and results. 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).

This package provides a small wrapper on regexpr to extract the matches and captured groups from the match of a regular expression to a character vector.

rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions.

This package provides tools to convert R Markdown documents into a variety of formats.

This package provides a collection of (mostly simple) functions for generating and manipulating colors in R.