The googleVis package provides an interface between R and the Google Charts API. Google Charts offer interactive charts which can be embedded into web pages. The functions of the googleVis package allow the user to visualise data stored in R data frames with Google Charts without uploading the data to Google. The output of a googleVis function is HTML code that contains the data and references to JavaScript functions hosted by Google. googleVis makes use of the internal R HTTP server to display the output locally.

This package was designed to find an acceptable Python binary that matches version and feature constraints.

This package represents an implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization.

This package provides an R port of the library Clipper. It performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. It computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. It computes the Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.

This package provides an R interface to the nanoarrow C library and the Apache Arrow application binary interface. Functions to import and export ArrowArray, ArrowSchema, and ArrowArrayStream C structures to and from R objects are provided alongside helpers to facilitate zero-copy data transfer among R bindings to libraries implementing the Arrow C data interface.

This package implements Barzilai-Borwein spectral methods for solving nonlinear system of equations, and for optimizing nonlinear objective functions subject to simple constraints.

This is an extension of the testthat package that lets you add parameters to your unit tests. Parameterized unit tests are often easier to read and more reliable, since they follow the DNRY (do not repeat yourself) rule.

This package provides a collection of functions useful in learning and practicing Item Response Theory (IRT), which can be combined into larger programs. It provides basic CTT analysis, a simple common interface to the estimation of item parameters in IRT models for binary responses with three different programs (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord, and the complete Hebaera method), and a rich array of parametric and non-parametric (kernel) plots. It estimates and plots Haberman's interaction model when all items are dichotomously scored.

This package orders panels in scatterplot matrices and parallel coordinate displays by some merit index. It contains various indices of merit, ordering functions, and enhanced versions of pairs and parcoord which color panels according to their merit level.

This package provides functions for numerical analysis and linear algebra, numerical optimization, differential equations, plus some special functions. It uses Matlab function names where appropriate to simplify porting.

The fstlib library provides multithreaded serialization of compressed data frames using the fst format. The fst format allows for random access of stored data and compression with the LZ4 and ZSTD compressors.

The clusterGeneration package provides functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.

This package provides easy-to-use and versatile functions to output R objects in HTML format.

This package includes functions and reference data to generate and manipulate log-ratios (also known as log size index (LSI) values) from measurements obtained on zooarchaeological material. Log ratios are used to compare the relative (rather than the absolute) dimensions of animals from archaeological contexts. The zoolog package is also able to seamlessly integrate data and references with heterogeneous nomenclature, which is internally managed by a zoolog thesaurus.

GLDEX offers fitting algorithms corresponding to two major objectives. One is to provide a smoothing device to fit distributions to data using the weighted and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, and L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.

This package provides a complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.

Ggdag is built on top of dagitty, an R package that uses the DAGitty web tool for creating and analyzing DAGs. ggdag makes it easy to tidy and plot dagitty objects using ggplot2 and ggraph, as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.

Colored terminal output on terminals that support ANSI color and highlight codes. It also works in Emacs ESS. ANSI color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the "chalk" JavaScript project.

This package computes model and semi partial R squared with confidence limits for the linear and generalized linear mixed model (LMM and GLMM). The R squared measure from L. J. Edwards et al. (2008) is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. (2016)).

This package contains tools for exploring Hardy-Weinberg equilibrium for diallelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) for Hardy-Weinberg equilibrium are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of diallelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots.

This package provides non-statistical utilities used by the software developed by the Statnet Project.

Gtable is a collection of tools to make it easier to work with "tables" of grobs.

The sqldf function is typically passed a single argument which is an SQL select statement where the table names are ordinary R data frame names. sqldf transparently sets up a database, imports the data frames into that database, performs the SQL statement and returns the result using a heuristic to determine which class to assign to each column of the returned data frame. The sqldf or read.csv.sql functions can also be used to read filtered files into R even if the original files are larger than R itself can handle.

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.