Statistical and biological validation of clustering results. This package implements Dunn Index, Silhouette, Connectivity, Stability, BHI and BSI. Further information can be found in Brock, G et al. (2008) <doi: 10.18637/jss.v025.i04>.

This package provides a shared resource interface for the bigmemory and synchronicity packages.

This package implements various measures of information theory based on several entropy estimators.

HTML widgets is a framework for creating HTML widgets that render in various contexts including the R console, R Markdown documents, and Shiny web applications.

This package provides an implementation of sparse linear discriminant analysis, which is a supervised classification method for multiple classes. Various novel optimization approaches to this problem are implemented including alternating direction method of multipliers (ADMM), proximal gradient (PG) and accelerated proximal gradient (APG). Functions for performing cross validation are also supplied along with basic prediction and plotting functions. Sparse zero variance discriminant (SZVD) analysis is also included in the package.

Annoy is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from and save to disk. This package provides an R interface.

This package lets you fit pedigree-based mixed-effects models.

This package provides tools for clustering and principal component analysis (with robust methods, and parallelized functions).

This is an implementation of the Future API on top of the callr package. This allows you to process futures, as defined by the future package, in parallel out of the box, on your local machine. Contrary to backends relying on the parallel package (e.g. future::multisession) and socket connections, the callr backend provided here can run more than 125 parallel R processes.

This package provides a set of functions for sparse matrix algebra. Differences with other sparse matrix packages are:

1. it only supports (essentially) one sparse matrix format;

2. it is based on transparent and simple structure(s);

3. it is tailored for MCMC calculations within G(M)RF;

4. and it is fast and scalable (with the extension package spam64).

int64 values can be created and accessed via the bit64 package and its integer64 class which package the int64 representation cleverly into a double. The nanotime package builds on this to support nanosecond-resolution timestamps. This package helps conversions between R and C++ via several helper functions provided via a single header file. A complete example client package is included as an illustration.

This package carries out a mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided.

Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015)

This package provides a dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets.

This package provides a comprehensive collection for structural multivariate function estimation using smoothing splines.

This package provides functions to handle basic input output. These functions always read and write UTF-8 (8-bit Unicode Transformation Format) files and provide more explicit control over line endings.

This is a framework for fitting multiple caret models. It uses the same re-sampling strategy as well as creating ensembles of such models. Use caretList to fit multiple models and then use caretEnsemble to combine them greedily or caretStack to combine them using a caret model.

This package computes optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

This package provides functions to compute the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.

This package provides tools and functions for managing the download of binary files. Binary repositories are defined in the YAML format. Defining new pre-download, download and post-download templates allow additional repositories to be added.

This package provides Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.

httpcode provides functionality for finding and explaining the meaning of HTTP status codes. Functions are included for searching for codes by full or partial number, by message, and to get appropriate dog and cat images for many status codes.

This package provides tools for data frame summaries, cross-tabulations, weight-enabled frequency tables and common univariate statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.

This package provides alternative implementations of some base R functions, including sort, order, and match. The functions are simplified but can be faster or have other advantages.