This package provides a smooth mapping of multidimensional points into low-dimensional space defined by a self-organizing map. It is designed to work with FlowSOM and flow-cytometry use-cases.

This package provides functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by Lee et al. (2013) <doi:10.1016/j.ajhg.2013.05.010>. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.

This package provides a parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks.

The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions is made available with R_RegisterCCallable().

This package provides methods and algorithms for discrete optimization, e.g. knapsack and subset sum procedures, derivative-free Nelder-Mead and Hooke-Jeeves minimization, and some (evolutionary) global optimization functions.

This package provides vectorized distribution objects with tools for manipulating, visualizing, and using probability distributions. It was designed to allow model prediction outputs to return distributions rather than their parameters, allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.

This is a set of tools for dendrograms and tree plots using ggplot2. The ggplot2 philosophy is to clearly separate data from the presentation. Unfortunately the plot method for dendrograms plots directly to a plot device with out exposing the data. The ggdendro package resolves this by making available functions that extract the dendrogram plot data. The package provides implementations for tree, rpart, as well as diana and agnes cluster diagrams.

Solving a system of linear equations is one of the most fundamental computational problems for many fields of mathematical studies, such as regression problems from statistics or numerical partial differential equations. This package provides basic stationary iterative solvers such as Jacobi, Gauss-Seidel, Successive Over-Relaxation and SSOR methods. Nonstationary, also known as Krylov subspace methods are also provided. Sparse matrix computation is also supported in that solving large and sparse linear systems can be manageable using the Matrix package along with RcppArmadillo.

This package provides tools for creating, viewing, and assessing qualitative palettes with many (20-30 or more) colors. See Coombes and colleagues (2019) https://doi:10.18637/jss.v090.c01.

Render R Markdown to Markdown (without using knitr), and Markdown to lightweight HTML or LaTeX documents with the commonmark package (instead of Pandoc). Some missing Markdown features in commonmark are also supported, such as raw HTML or LaTeX blocks, LaTeX math, superscripts, subscripts, footnotes, element attributes, and appendices, but not all Pandoc Markdown features are (or will be) supported. With additional JavaScript and CSS, you can also create HTML slides and articles. This package can be viewed as a trimmed-down version of R Markdown and knitr. It does not aim at rich Markdown features or a large variety of output formats (the primary formats are HTML and LaTeX). Book and website projects of multiple input documents are also supported.

This package provides a small subset of Unicode symbols, that are useful when building command line applications. They fall back to alternatives on terminals that do not support Unicode.

This package provides a file format for storing tensors that is secure (doesn't allow for code execution), fast and simple to implement. safetensors also enables cross language and cross frameworks compatibility making it an ideal format for storing machine learning model weights.

This package provides ACE and AVAS methods for choosing regression transformations.

Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.

With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines CppAD (C++ automatic differentiation), Eigen (templated matrix-vector library) and CHOLMOD (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through BLAS and parallel user templates.

This package provides routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.

Structural equation modeling (SEM) has a long history of representing models graphically as path diagrams. The semPlot package for R fills the gap between advanced, but time-consuming, graphical software and the limited graphics produced automatically by SEM software. In addition, semPlot offers more functionality than drawing path diagrams: it can act as a common ground for importing SEM results into R. Any result usable as input to semPlot can also be represented in any of the three popular SEM frame-works, as well as translated to input syntax for the R packages sem and lavaan.

This package contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files.

This is a package for pretty-printing R code without changing the user's formatting intent.

This package implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.

This package provides a suite of functions to help ease the use of the d3.js visualization library in R. These helpers include htmltools::htmlDependency functions, hierarchy builders, and conversion tools for partykit, igraph, table, and data.frame R objects into the JSON format that the d3.js library expects.

This package provides an up-to-date copy of the Internet Assigned Numbers Authority (IANA) Time Zone Database. It is updated periodically to reflect changes made by political bodies to time zone boundaries, UTC offsets, and daylight saving time rules. Additionally, this package provides a C++ interface for working with the date library. date provides comprehensive support for working with dates and date-times, which this package exposes to make it easier for other R packages to utilize. Headers are provided for calendar specific calculations, along with a limited interface for time zone manipulations.

Assertthat is an extension to stopifnot() that makes it easy to declare the pre and post conditions that your code should satisfy, while also producing friendly error messages so that your users know what they've done wrong.

This package implements affinity propagation clustering introduced by Frey and Dueck (2007). The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results.