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

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

The snow package provides support for simple parallel computing on a network of workstations using R. A master R process calls makeCluster to start a cluster of worker processes; the master process then uses functions such as clusterCall and clusterApply to execute R code on the worker processes and collect and return the results on the master.

This package provides a tbl_df class that offers better checking and printing capabilities than traditional data frames.

The trimmed k-means clustering method by Cuesta-Albertos, Gordaliza and Matran (1997). This optimizes the k-means criterion under trimming a portion of the points.

This package provides functions to access the RStudio API and provide informative error messages when it's not available.

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 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.)

This package contains a set of functions for working with Random Number Generators (RNGs). In particular, it defines a generic S4 framework for getting/setting the current RNG, or RNG data that are embedded into objects for reproducibility. Notably, convenient default methods greatly facilitate the way current RNG settings can be changed.

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.

Roxygen2 is a Doxygen-like in-source documentation system for Rd, collation, and NAMESPACE files.

This package implements importance sampling from the truncated multivariate normal using the Geweke-Hajivassiliou-Keane (GHK) simulator. Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions for multivariate normals.

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

This package provides methods and classes for object-oriented programming in R with or without references. Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.

dcor is distance correlation and energy statistics in Python.

E-statistics are functions of distances between statistical observations in metric spaces. Distance covariance and distance correlation are dependency measures between random vectors introduced in [SRB07] with a simple E-statistic estimator.

This package offers functions for calculating several E-statistics such as:

• estimator of the energy distance [SR13]

• biased and unbiased estimators of distance covariance and distance correlation [SRB07]

• estimators of the partial distance covariance and partial distance covariance [SR14]

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 unit testing system for R designed to be fun, flexible and easy to set up.

This package finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library. Provides approximate, exact searches, fixed radius searches, bd and kb trees.

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

This package provides some basic linear algebra functionality for sparse matrices. It includes Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.

The R6 package allows the creation of classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. These classes allow public and private members, and they support inheritance, even when the classes are defined in different packages.

This package provides an implementation of robust location and scatter estimation and robust multivariate analysis with high breakdown point.

This package provides a fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently.

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