DING-LIBS (DING Is Not Glib) are a set of small, useful libraries that the System Security Services Daemon (SSSD) uses and makes available to other projects. They include: libdhash, an implementation of a dynamic hash table which will dynamically resize to achieve optimal storage and access time properties; ini_config, a library for parsing and managing INI files; path_utils, a library to manage UNIX paths and subsets of paths; collection, a generic, hierarchical grouping mechanism for complex data sets; ref_array, a dynamically-growing, reference-counted array; libbasicobjects, a set of fundamental object types for C.

SSSD is a system daemon. Its primary function is to provide access to identity and authentication remote resource through a common framework that can provide caching and offline support to the system. It provides PAM and NSS modules, and in the future will support D-BUS based interfaces for extended user information. It also provides a better database to store local users as well as extended user data.

adcli is a command‐line tool to join a computer to an Active Directory domain. It can also update the machine password and manage user, group and computer accounts for a domain.

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.

Chaospy is a numerical toolbox for performing uncertainty quantification using polynomial chaos expansions, advanced Monte Carlo methods implemented in Python. It also include a full suite of tools for doing low-discrepancy sampling, quadrature creation, polynomial manipulations, and a lot more.

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

Roxygen2 is a Doxygen-like in-source documentation system for Rd, collation, and NAMESPACE files.

This package provides a generic infrastructure for creating and using R package registries.

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.

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 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.

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

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.

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 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.

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.

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

The snow package provides support for simple parallel computing on a network of workstations using R. A master R process calls makeCluster to start a cluster of worker processes; the master process then uses functions such as clusterCall and clusterApply to execute R code on the worker processes and collect and return the results on the master.

The R6 package allows the creation of 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.

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.

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

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

This package implements different robust clustering algorithms (tclust) based on trimming and including some graphical diagnostic tools (ctlcurves and DiscrFact).

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.