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 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.

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 a number of polymodes for working with mixed R files, including Rmarkdown files.

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

This package enables survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression.

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 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 functions to read flat or tabular text files from disk (or a connection).

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 Python package can be used to read and write SAS, SPSS and Stata files into/from Pandas DataFrames. It is a wrapper around the C library readstat.

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

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

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

Patsy is a Python package for describing statistical models and for building design matrices.

tidyr is a reframing of the reshape2 package designed to accompany the tidy data framework, and to work hand-in-hand with magrittr and dplyr to build a solid pipeline for data analysis. It is designed specifically for tidying data, not the general reshaping that reshape2 does, or the general aggregation that reshape did. In particular, built-in methods only work for data frames, and tidyr provides no margins or aggregation.

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 an implementation of Nested Sampling algorithms for evaluating Bayesian evidence.

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.

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 support for synchronization via mutexes and may eventually support interprocess communication and message passing.

This package provides a unit testing system for R designed to be fun, flexible and easy to set up.

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