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 Python/C(++) implementation of Hartigan & Hartigan's dip test for unimodality.

The dip test measures multimodality in a sample by the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference. Other than unimodality, it makes no further assumptions about the form of the null distribution.

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

This package is a model building aid for nonlinear mixed-effects (population) model analysis using NONMEM, facilitating data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison. The methods are described in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) <doi:10.1016/s0169-2607(98)00067-4>.

emcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC).

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

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

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

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.

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

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.

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

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.

This package provides a resampling-based inference based on data resampling and permutation.

Features:

• Bootstrap resampling: ordinary or balanced with optional stratification

• Extended bootstrap resampling: also varies sample size

• Parametric resampling: Gaussian, Poisson, gamma, etc.)

• Jackknife estimates of bias and variance of any estimator

• Compute bootstrap confidence intervals (percentile or BCa) for any estimator

• Permutation-based variants of traditional statistical tests (USP test of independence and others)

• Tools for working with empirical distributions (CDF, quantile, etc.)

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.

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.

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

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.

PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms.

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

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

ArviZ is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.

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.