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.

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.

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

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.

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

This package provides a toolbox for working with base types, core R features like the condition system, and core Tidyverse features like tidy evaluation.

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.

This package provides methods for caching or memoization of objects and results. With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects. The cache memory is persistent (on the file system).

OpenTURNS is a scientific C++ and Python library including an internal data model and algorithms dedicated to the treatment of uncertainties. The main goal of this library is giving to specific applications all the functionalities needed to treat uncertainties in studies.

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

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 tools to convert R Markdown documents into a variety of formats.

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

The sourcetools package provides both an R and C++ interface for the tokenization of R code, and helpers for interacting with the tokenized representation of R code.

This package provides a set of functions used to automate commonly used methods in regression analysis. This includes plotting interactions, and calculating simple slopes, standardized coefficients, regions of significance (Johnson & Neyman, 1936; cf. Spiller et al., 2012), etc.

tidyr is a reframing of the reshape2 package designed to accompany the tidy data framework, and to work hand-in-hand with magrittr and dplyr to build a solid pipeline for data analysis. It is designed specifically for tidying data, not the general reshaping that reshape2 does, or the general aggregation that reshape did. In particular, built-in methods only work for data frames, and tidyr provides no margins or aggregation.

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.)

Armadillo is a templated C++ linear algebra library that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. This package includes the header files from the templated Armadillo library.

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]

This package provides the Breiman and Cutler's random forests algorithm, based on a forest of trees using random inputs, for classification and regression.

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 implements different robust clustering algorithms (tclust) based on trimming and including some graphical diagnostic tools (ctlcurves and DiscrFact).

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

This package provides functions to read flat or tabular text files from disk (or a connection).