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.

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

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

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.

Vega-Altair is a declarative statistical visualization library for Python.

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.

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.

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 provides an implementation of Nested Sampling algorithms for evaluating Bayesian evidence.

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

Nautilus is an pure-Python package for Bayesian posterior and evidence estimation. It utilizes importance sampling and efficient space exploration using neural networks. Compared to traditional MCMC and Nested Sampling codes, it often needs fewer likelihood calls and produces much larger posterior samples. Additionally, nautilus is highly accurate and produces Bayesian evidence estimates with percent precision. It is widely used in many areas of astrophysical research.

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.

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

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.

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.

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.

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

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

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 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.

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

This package contains a variety of small independent libraries for the C programming language.