This package provides a collection of perceptually uniform color maps made by Peter Kovesi (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> at the Centre for Exploration Targeting (CET).

This package provides a non-linear model, termed ACME, that reflects a parsimonious biological model for allelic contributions of cis-acting eQTLs. With non-linear least-squares algorithm the maximum likelihood parameters can be estimated. The ACME model provides interpretable effect size estimates and p-values with well controlled Type-I error.

This package constructs basis functions of B-splines, M-splines, I-splines, convex splines (C-splines), periodic splines, natural cubic splines, generalized Bernstein polynomials, their derivatives, and integrals (except C-splines) by closed-form recursive formulas. It also contains a C++ head-only library integrated with Rcpp.

This package provides an interface to Amazon Web Services management and governance services, including CloudWatch application and infrastructure monitoring, Auto Scaling for automatically scaling resources, and more.

This package provides tools to visualize simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, visualizing webs, electrical networks, etc. It also includes supporting material for the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter M.J. Herman (2009) and the book "Solving Differential Equations in R" by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012).

This package provides vector map data from https://www.naturalearthdata.com/. Access functions are provided in the accompanying package rnaturalearth.

This package provides a Shiny app that can disconnect for a variety of reasons: an unrecoverable error occurred in the app, the server went down, the user lost internet connection, or any other reason that might cause the Shiny app to lose connection to its server. With shinydisconnect, you can call disonnectMessage anywhere in a Shiny app's UI to add a nice message when this happens. It works locally (running Shiny apps within RStudio) and on Shiny servers.

Phylogenetic trees generally contain multiple components including nodes, edges, branches and associated data. This package provides an approach to convert tree objects to tidy data frames. It also provides tidy interfaces to manipulate tree data.

This package implements the diffusion map method of data parametrization, including creation and visualization of diffusion maps, clustering with diffusion K-means and regression using the adaptive regression model.

This package contains functions useful for correlation theory, meta-analysis (validity-generalization), reliability, item analysis, inter-rater reliability, and classical utility.

The Rsolnp package implements a general non-linear augmented Lagrange multiplier method solver, a sequential quadratic programming (SQP) based solver).

This package handles very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. The package includes a vignette that gives a step-by-step introduction to using S4 methods.

This package provides a shared resource interface for the bigmemory and synchronicity packages.

This package provides primitives for visualizing distributions using ggplot2 that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized.

This package contains functions to estimate L-moments and trimmed L-moments from the data. It also contains functions to estimate the parameters of the normal polynomial quantile mixture and the Cauchy polynomial quantile mixture from L-moments and trimmed L-moments.

This package provides common functionality for the dynverse packages. dynverse is created to support the development, execution, and benchmarking of trajectory inference methods.

This package provides a unified R graphics backend. Render R graphics fast and easy to many common file formats. It provides a thread safe C interface for asynchronous rendering of R graphics.

This package provides an implementation of the FastICA algorithm to perform independent component analysis (ICA) and projection pursuit.

This package provides geometries to plot network objects with the ggplot2 package.

This package provides utility functions that enhance the parallel package and support the built-in parallel backends of the future package. For example, availableCores gives the number of CPU cores available to your R process as given by R options and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores. Another example is makeClusterPSOCK, which is backward compatible with parallel::makePSOCKcluster while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.

R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. The conflicted package takes a different approach, making every conflict an error and forcing you to choose which function to use.

This package is a collection of search spaces for hyperparameter optimization in the mlr3 ecosystem. It features ready-to-use search spaces for many popular machine learning algorithms. The search spaces are from scientific articles and work for a wide range of data sets.

This package provides a menu-driven program and library of functions for carrying out convergence diagnostics and statistical and graphical analysis of Markov chain Monte Carlo (MCMC) sampling output.

This package performs score test using saddlepoint approximation to estimate the null distribution. It also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results.