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.

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

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

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.

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

JDistlib is the Java Statistical Distribution Library, a Java package that provides routines for various statistical distributions.

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

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

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

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.

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

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.

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.)

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.

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.

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

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.

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.

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.

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

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.

Various definitions for a high-dimensional median exist and this Python package provides a number of fast implementations of these definitions. Medians are extremely useful due to their high breakdown point (up to 50% contamination) and have a number of nice applications in machine learning, computer vision, and high-dimensional statistics.

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

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