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.

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

This package provides functions to query the main R repository to find the versions that r-release and r-oldrel refer to, and also all previous R versions and their release dates.

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

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]

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 support for synchronization via mutexes and may eventually support interprocess communication and message passing.

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.

This package provides a backend for the selecting functions of the tidyverse. It makes it easy to implement select-like functions in your own packages in a way that is consistent with other tidyverse interfaces for selection.

This package provides an R wrapper around the fast T-distributed Stochastic Neighbor Embedding using a Barnes-Hut implementation.

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.

Similarity Weighted Nonnegative Embedding (SWNE) is a method for visualizing high dimensional datasets. SWNE uses Nonnegative Matrix Factorization to decompose datasets into latent factors, projects those factors onto 2 dimensions, and embeds samples and key features in 2 dimensions relative to the factors. SWNE can capture both the local and global dataset structure, and allows relevant features to be embedded directly onto the visualization, facilitating interpretation of the data.

This package provides a generic infrastructure for creating and using R package registries.

This package provides a small wrapper on regexpr to extract the matches and captured groups from the match of a regular expression to a character vector.

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

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

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

The RSP markup language provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. Today's date is <%=Sys.Date()%>. Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries. RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things. RSP is ideal for self-contained scientific reports and R package vignettes.

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

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 an implementation of Nested Sampling algorithms for evaluating Bayesian evidence.

This package provides useful utilities from Seminar fuer Statistik ETH Zurich, including many that are related to graphics.

This package provides functionalities to build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t family, and provides related statistical methods for data fitting and diagnostics, in the univariate and the multivariate case.