This package provides an R interface to the Spectra library for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n.

The SciViews svGUI package eases the management of Graphical User Interfaces (GUI) in R. It is independent from any particular GUI widgets. It centralizes info about GUI elements currently used, and it dispatches GUI calls to the particular toolkits in use in function of the context.

This package is a port of sofia-ml to R. Sofia-ml is a suite of fast incremental algorithms for machine learning that can be used for training models for classification or ranking.

This package computes two-sample confidence intervals for single, paired and independent proportions.

Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. The probably package contains tools for conducting these operations as well as calibration tools and conformal inference techniques for regression models.

This package lets you manage configuration values across multiple environments (e.g. development, test, production). It reads values using a function that determines the current environment and returns the appropriate value.

This package lets you take formulas including random-effects components (formatted as in lme4, glmmTMB, etc.) and process them. It includes various helper functions.

The ability to tune models is important. tune contains functions and classes to be used in conjunction with other tidymodels packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.

This package is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted.

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 creates a lightweight way to add markdown helpfiles to Shiny apps, using modal dialog boxes, with no need to observe each help button separately.

This is a package for constructing minimum-cost regular spanning subgraph as part of a non-parametric two-sample test for equality of distribution.

This package provides syntax highlighting of R code, specifically designed for the needs of RMarkdown packages like pkgdown, hugodown, and bookdown. It includes linking of function calls to their documentation on the web, and automatic translation of ANSI escapes in output to the equivalent HTML.

spacefillr enables generation of random and quasi-random space-filling sequences. It supports the following sequences: Halton, Sobol, Owen-scrambled Sobol, Owen-scrambled Sobol with errors distributed as blue noise, progressive jittered, progressive multi-jittered (PMJ), PMJ with blue noise, PMJ02, and PMJ02 with blue noise. The package also includes a C++ API.

This package provides an improved heatmap package. It is completely compatible with the original R function heatmap, and provides more powerful and convenient features.

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.

Feature Selection with Regularized Random Forest. This package is based on the randomForest package by Andy Liaw. The key difference is the RRF() function that builds a regularized random forest. Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener, Regularized random forest for classification by Houtao Deng, Regularized random forest for regression by Xin Guan. Reference: Houtao Deng (2013) <doi:10.48550/arXiv.1306.0237>.

Manipulate and visualize colors in a intuitive, low-dependency and functional way.

This package provides code analysis tools for R to check R code for possible problems.

This is a developer-focused, low dependency package in tidymodels that provides functions to register how models are to be used. Functions to register models are complimented with accessor functions to retrieve registered model information to aid in model fitting and error handling.

This package provides a collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics.

This package creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().

This package provides basic infrastructure and some algorithms for the traveling salesperson problem(TSP) (also known as the traveling salesman problem).

This package represents a collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.