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.

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

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

This package provides the R math library as an independent package.

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 an implementation of Nested Sampling algorithms for evaluating Bayesian evidence.

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

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

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

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

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.

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

libxls is a C library to read .xls spreadsheet files in the binary OLE BIFF8 format as created by Excel 97 and later versions. It cannot write them.

This package also provides xls2csv to export Excel files to CSV.

The rpy2 package is a namespace package. This is the part of that package that covers the "low-level" interface to R used in rpy2. This provides mappings to access R's C-API and utilities to do so safely. It is otherwise relatively easily to crash (segfault) a process by calling R's C-API.

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

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 an implementation of the Ensemble Slice Sampling method. Features:

• fast & Robust Bayesian Inference

• efficient Markov Chain Monte Carlo (MCMC)

• black-box inference, no hand-tuning

• excellent performance in terms of autocorrelation time and convergence rate

• scale to multiple CPUs without any extra effort

• automated Convergence diagnostics

Did you ever wish you could make scatter plots with cat shaped points? Now you can!

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.

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.

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

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