This package provides two convenience functions assert() and test_pkg() to facilitate testing R packages.

This package provides simple and crisp publication-quality graphics for the ExPosition family of packages. See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.

This package provides infrastructure for seriation with an implementation of several seriation/sequencing techniques to reorder matrices, dissimilarity matrices, and dendrograms. It also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT).

This package provides David Scott's ASH routines ported from S-PLUS to R.

The clusterCrit package provides an implementation of the following indices: Czekanowski-Dice, Folkes-Mallows, Hubert Γ, Jaccard, McNemar, Kulczynski, Phi, Rand, Rogers-Tanimoto, Russel-Rao or Sokal-Sneath. ClusterCrit defines several functions which compute internal quality indices or external comparison indices. The partitions are specified as an integer vector giving the index of the cluster each observation belongs to.

This package provides data sets and scripts to accompany Time Series Analysis and Its Applications: With R Examples (4th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017, https://doi.org/10.1007/978-3-319-52452-8, and Time Series: A Data Analysis Approach Using R. Chapman-Hall, 2019, https://doi.org/10.1201/9780429273285.

This package provides various methods to conduct Spatio-Temporal Analysis and Modelling, including Exploratory Spatio-Temporal Analysis and Inferred Spatio-Temporal Modelling.

This package provides functions for extracting feature contributions from a random forest model from package randomForest. Feature contributions provide detailed information about the relationship between data variables and the predicted value returned by random forest model.

Content-preserving transformations transformations of PDF files such as split, combine, and compress. This package interfaces directly to the qpdf C++ API and does not require any command line utilities. Note that qpdf does not read actual content from PDF files: to extract text and data you need the pdftools package.

This package provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous testing bands to asses significance of sample deviation from a reference distribution.

This package provides optimized functions and flexible combinatorial iterators implemented in C++ for solving problems in combinatorics and computational mathematics. It utilizes the RMatrix class from RcppParallel for thread safety. There are combination/permutation functions with constraint parameters that allow for generation of all results of a vector meeting specific criteria. It is capable of generating specific combinations/permutations which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large. Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics.

The lattice package provides a powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements.

This package provides lots of plotting, various labeling, axis and color scaling functions for R.

Perform common useful JavaScript operations in Shiny apps that will greatly improve your apps without having to know any JavaScript. Examples include: hiding an element, disabling an input, resetting an input back to its original value, delaying code execution by a few seconds, and many more useful functions for both the end user and the developer. Shinyjs can also be used to easily call your own custom JavaScript functions from R.

This package provides a close to zero dependency package to draw and display Venn diagrams up to 7 sets, and any Boolean union of set intersections.

This package provides an implementation of both the exact and approximation methods for computing the cumulative distribution function (CDF) of the Poisson binomial distribution. It also provides the probability mass function (PMF), quantile function, and random number generation for the Poisson binomial distribution.

This package implements the Python leidenalg module to be called in R. It enables clustering using the Leiden algorithm for partitioning a graph into communities. See also Traag et al (2018) "From Louvain to Leiden: guaranteeing well-connected communities." <arXiv:1810.08473>.

This package provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc.

This package provides tools for the analysis of high-dimensional data developed/implemented at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME). The main focus is on SNP data, but most of the functions can also be applied to other types of categorical data.

This package provides qualitative methods for the validation of dynamic models. It contains

1. an orthogonal set of deviance measures for absolute, relative and ordinal scale and

2. approaches accounting for time shifts.

The first approach transforms time to take time delays and speed differences into account. The second divides the time series into interval units according to their main features and finds the longest common subsequence (LCS) using a dynamic programming algorithm.

Designed for simplicity, a mirai evaluates an R expression asynchronously in a parallel process, locally or distributed over the network. The result is automatically available upon completion. Modern networking and concurrency, built on nanonext and NNG (Nanomsg Next Gen), ensures reliable and efficient scheduling over fast inter-process communications or TCP/IP secured by TLS. Distributed computing can launch remote resources via SSH or cluster managers. An inherently queued architecture handles many more tasks than available processes, and requires no storage on the file system. Innovative features include support for otherwise non-exportable reference objects, event-driven promises, and asynchronous parallel map.

This package provides miscellaneous small tools and utilities. Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. Other tools facilitate the work with regression models, e.g. extracting the standard errors, obtaining the number of (estimated) parameters, or calculating R-squared values.

This package provides an easy to use library to setup, apply and make inference with discrete time and discrete space hidden Markov models.

This package extends mlr3 with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported.