The R package ggplot2 is a plotting system based on the grammar of graphics. GGally extends ggplot2 by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

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.

This package provides functions for working with magnetic resonance images. It supports reading and writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH); interactive and non-interactive visualization; flexible image manipulation; metadata and sparse image handling.

This package implements S4 classes and various tools for financial time series. Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions are provided.

This package defines the generic method extract and provides openMP support as needed in several packages like aws, adimpro, fmri, and dwi.

This package offers methods for estimating statistical changes in time series. These are used for identifying nearby critical transitions.

This package provides header library and R functions to solve minimum cost bipartite matching problem using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>; Kuhn (1955) <doi:10.1002/nav.3800020109>).

This package provides a mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalized linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers.

This package provides functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.

The spdlog library is a widely-used and very capable header-only C++ library for logging. This package includes its headers as an R package to permit other R packages to deploy it via a simple LinkingTo: RcppSpdlog. As of version 0.0.9, it also provides both simple R logging functions and compiled functions callable by other packages.

This package includes functions for processing GeoJson objects relying on RFC 7946. The geojson encoding is based on json11, a tiny JSON library for C++11. Furthermore, the source code is exported in R through the Rcpp and RcppArmadillo packages.

Extract metadata from NetCDF data sources; these can be files, file handles or servers. This package leverages and extends the lower level functions of the RNetCDF package providing a consistent set of functions that all return data frames.

This package provides a suite of flexible and versatile model fitting and after-fitting functions for the analysis of dose-response data.

This package lets you fit a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an R6 object and can be easily updated with new data. There are options to run in parallel, and Rcpp has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.

This package contains the program ttf2pt1, for use with the extrafont package.

Alternating least squares is often used to resolve components contributing to data with a bilinear structure; the basic technique may be extended to alternating constrained least squares. This package provides an implementation of multivariate curve resolution alternating least squares (MCR-ALS).

Commonly applied constraints include unimodality, non-negativity, and normalization of components. Several data matrices may be decomposed simultaneously by assuming that one of the two matrices in the bilinear decomposition is shared between datasets.

This package covers many important models used in marketing and micro-econometrics applications, including Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity, and Bayesian Analysis of Aggregate Random Coefficient Logit Models.

This package offers extensive tools for phylogenetic analysis. It focuses on phylogenetic comparative biology but also includes methods for visualizing, analyzing, manipulating, reading, writing, and inferring phylogenetic trees. Functions for comparative biology include ancestral state reconstruction, model fitting, and phylogeny and trait data simulation. A broad range of plotting methods includes mapping trait evolution on trees, projecting trees into phenotype space or geographic maps, and visualizing correlated speciation between trees. Additional functions allow for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. Examples include computing consensus trees, simulating trees and data under various models, and attaching species or clades to a tree either randomly or non-randomly. This package provides numerous tools for tree manipulations and analyses that are valuable for phylogenetic research.

This package provides a toolbox for working with base types, core R features like the condition system, and core Tidyverse features like tidy evaluation.

This package estimates previously compiled regression models using the rstan package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

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 carries out a mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided.

This package performs the Baumgartner-Weiss-Schindler two-sample test of equal probability distributions (doi:10.2307/2533862). It also performs similar rank-based tests for equal probability distributions due to Neuhauser (doi:10.1080/10485250108832874) and Murakami (doi:10.1080/00949655.2010.551516).

This is a package for the analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported.