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

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

ArviZ (pronounced AR-vees) is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics. arviz-base is the subpackage in charge of converters and base manipulation.

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.

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 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.

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

This package implements a functionality to optionally compute statistical test and add statistical annotations on plots generated with seaborn.

This is a package to provide infrastructure for managing package parameters. Parameters are easy to get in relevant functions within a package, and error 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.

ArviZ (pronounced AR-vees) is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics. arviz-stats is the subpackage in charge of the statistical and diagnostics computations.

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.

The rpy2 package is a namespace package. This is the part of that package that covers the "high-level" interface to R used in rpy2. It uses the "low-level" interface (bindings to R's C API) to create a more Pythonic and safer interface in rpy2.robjects as well as extentions for ipython and jupyter notebooks in rpy2.ipython.

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.

GNU PSPP is a statistical analysis program. It can perform descriptive statistics, T-tests, linear regression and non-parametric tests. It features both a graphical interface as well as command-line input. PSPP is designed to interoperate with Gnumeric, LibreOffice and OpenOffice. Data can be imported from spreadsheets, text files and database sources and it can be output in text, PostScript, PDF or HTML.

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 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.

Radian is an alternative console for the R program with multiline editing and rich syntax highlight. One would consider Radian as a IPython clone for R, though its design is more aligned to Julia.

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.

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

This package provides simple utility functions that are shared across several packages maintained by the Tanay lab.

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

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

Enumerable::Statistics provides some methods to calculate statistical summary in arrays and enumerables.

stb-image is a small and self-contained library for image loading or decoding from file or memory. A variety of formats are supported.