This package provides a collection of efficient, vectorized algorithms for the creation and investigation of magic squares and hypercubes, including a variety of functions for the manipulation and analysis of arbitrarily dimensioned arrays.

This package provides a genetic algorithm plus derivative optimizer.

This package provides tools for the analysis of complex survey samples. The provided features include: summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples; variances by Taylor series linearisation or replicate weights; post-stratification, calibration, and raking; two-phase subsampling designs; graphics; PPS sampling without replacement; principal components, and factor analysis.

This package lets you replace the standard x-axis in ggplots with a combination matrix to visualize complex set overlaps. UpSet has introduced a new way to visualize the overlap of sets as an alternative to Venn diagrams. This package provides a simple way to produce such plots using ggplot2. In addition it can convert any categorical axis into a combination matrix axis.

This package provides a solution for analyzing digital images of plankton. In combination with ImageJ, an image analysis system, it processes digital images, measures individuals, trains for automatic classification of taxa, and finally, measures plankton samples (abundances, total and partial size spectra or biomasses, etc.).

This package provides a fast match replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory.

This package offers an interactive function for the detection of breakpoints in series.

This package provides tools to export R data as LaTeX and HTML tables.

This package lets you manage Google Drive files from R.

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).

This package provides an implementation of bee swarm plots. The bee swarm plot is a one-dimensional scatter plot like stripchart, but with closely-packed, non-overlapping points.

This package analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

This package performs projection predictive feature selection for generalized linear models and generalized linear and additive multilevel models. The package is compatible with the rstanarm and brms packages, but other reference models can also be used. See the package vignette for more information and examples.

This is a framework for construction and analysis of 2D Monte-Carlo simulations. In addition, this package includes various distributions.

This is an extension to Shiny that brings interactions and animation effects from the jQuery UI library.

ActiLife generates activity counts from data collected by Actigraph accelerometers. Actigraph is one of the most common research-grade accelerometers. There is considerable research validating and developing algorithms for human activity using ActiLife counts. Unfortunately, ActiLife counts are proprietary and difficult to implement if researchers use different accelerometer brands. The code creates ActiLife counts from raw acceleration data for different accelerometer brands.

This package provides an implementation of robust nonnegative matrix factorization (rNMF). The rNMF algorithm decomposes a nonnegative high dimension data matrix into the product of two low rank nonnegative matrices, while detecting and trimming outliers. The main function is rnmf(). The package also includes a visualization tool, see(), that arranges and prints vectorized images.

This package provides a developer-facing interface to Arrow Database Connectivity (ADBC) for the purposes of driver development, driver testing, and building high-level database interfaces for users. ADBC is an API standard for database access libraries that uses Arrow for result sets and query parameters.

This is a package for drawing calibrated scales with tick marks on (non-orthogonal) variable vectors in scatterplots and biplots.

This package provides themes for use with Shiny. It includes several Bootstrap themes, which are packaged for use with Shiny applications.

This package contains methods described by Dennis Helsel in his book Nondetects and Data Analysis: Statistics for Censored Environmental Data.

This package provides functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304.

Read large text files by splitting them in smaller files. This package also provides some convenient wrappers around fread() and fwrite() from package data.table.

This package provides a micro-package for reading "passwords", i.e. reading user input with masking, so that the input is not displayed as it is typed. Currently, RStudio, the command line (every OS), and any platform where tcltk is present are supported.