This package provides utility functions useful when programming and developing R packages.

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

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 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).

This is a package to provide infrastructure for managing package parameters. Parameters are easy to get in relevant functions within a package, and rrror 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.

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

This package helps accessing files relative to a project root. It provides helpers for robust, reliable and flexible paths to files below a project root. The root of a project is defined as a directory that matches a certain criterion, e.g., it contains a certain regular file.

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

SPAMS (SPArse Modeling Software) is an optimization toolbox for solving various sparse estimation problems. It includes tools for the following problems:

1. Dictionary learning and matrix factorization (NMF, sparse principle component analysis (PCA), ...)

2. Solving sparse decomposition problems with LARS, coordinate descent, OMP, SOMP, proximal methods

3. Solving structured sparse decomposition problems (l1/l2, l1/linf, sparse group lasso, tree-structured regularization, structured sparsity with overlapping groups,...).

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 the R math library as an independent package.

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

This package provides R functions implementing a standard unit testing framework, with additional code inspection and report generation tools.

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

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.)

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

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

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.

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

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

This package provides methods that simplify the setup of S3 generic functions and S3 methods. Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers. For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible. The method setMethodS3() is a good start for those who in the future may want to migrate to S4.

Given a regression model, segmented updates the model by adding one or more segmented (i.e., piecewise-linear) relationships. Several variables with multiple breakpoints are allowed.

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.