This package provides functions to query the main R repository to find the versions that r-release and r-oldrel refer to, and also all previous R versions and their release dates.

The RSP markup language 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. RSP is ideal for self-contained scientific reports and R package vignettes.

This package provides the Breiman and Cutler's random forests algorithm, based on a forest of trees using random inputs, for classification and regression.

This package is a port of the S+ "Robust Library". It provides methods for robust statistics, notably for robust regression and robust multivariate analysis.

This package embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine (version 3.8.8.2) is included.

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 performs KDE operations on multidimensional data to calculate estimated PDFs (probability distribution functions), and resample new data from those PDFs.

This package helps accessing files relative to a project root. It provides helpers for robust, reliable and flexible paths to files below a project root. The root of a project is defined as a directory that matches a certain criterion, e.g., it contains a certain regular file.

SPAMS (SPArse Modeling Software) is an optimization toolbox for solving various sparse estimation problems. It includes tools for the following problems:

1. Dictionary learning and matrix factorization (NMF, sparse principle component analysis (PCA), ...)

2. Solving sparse decomposition problems with LARS, coordinate descent, OMP, SOMP, proximal methods

3. Solving structured sparse decomposition problems (l1/l2, l1/linf, sparse group lasso, tree-structured regularization, structured sparsity with overlapping groups,...).

This package provides utility functions useful when programming and developing R packages.

Command-line tool and C library for reading files from popular stats packages like SAS, Stata and SPSS.

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.

Mixedpower uses pilotdata and a linear mixed model fitted with lme4 to simulate new data sets. Power is computed separate for every effect in the model output as the relation of significant simulations to all simulations. More conservative simulations as a protection against a bias in the pilotdata are available as well as methods for plotting the results.

This package provides an R wrapper around the fast T-distributed Stochastic Neighbor Embedding using a Barnes-Hut implementation.

This package provides an integration of Eigen in R using 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.

MAPIE allows you to easily estimate prediction intervals (or prediction sets) using your favourite scikit-learn-compatible model for single-output regression or multi-class classification settings.

Prediction intervals output by MAPIE encompass both aleatoric and epistemic uncertainties and are backed by strong theoretical guarantees thanks to conformal prediction methods intervals.

This is a package to provide infrastructure for managing package parameters. Parameters are easy to get in relevant functions within a package, and rrror is thrown if a parameter is missing. Developers are able to register parameters and set their default value in a config file that is part of the package in YAML format, and users are able to override parameters using their own YAML. Users get an exception when trying to override a parameter that was not registered, and can load multiple parameters to the current environment.

This package provides a set of functions used to automate commonly used methods in regression analysis. This includes plotting interactions, and calculating simple slopes, standardized coefficients, regions of significance (Johnson & Neyman, 1936; cf. Spiller et al., 2012), etc.

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

This package provides R functions implementing a standard unit testing framework, with additional code inspection and report generation tools.

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.

GetDist is a Python package for analysing Monte Carlo samples, including correlated samples from Markov Chain Monte Carlo (MCMC).

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 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.