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.

This package provides support for rendering of formatted text using Grid graphics. Text can be formatted via a minimal subset of Markdown, HTML, and inline CSS directives, and it can be rendered both with and without word wrap.

This package provides an implementation of multiscale bootstrap resampling for assessing the uncertainty in hierarchical cluster analysis. It provides an AU (approximately unbiased) P-value as well as a BP (bootstrap probability) value for each cluster in a dendrogram.

This package provides functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with various layout options.

This package lets you estimate fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and compute average partial effects. Incidental parameter bias can be reduced with an asymptotic bias correction proposed by Fernandez-Val (2009) <doi:10.1016/j.jeconom.2009.02.007>.

The Munsell package contains functions for exploring and using the Munsell colour system.

This package lets you access services specified in OpenAPI (formerly Swagger) format. It is not a code generator. The client is generated dynamically as a list of R functions.

This package provides a collection of regular expression tools associated with the qdap package that may be useful outside of the context of discourse analysis. Tools include removal/extraction/replacement of abbreviations, dates, dollar amounts, email addresses, hash tags, numbers, percentages, citations, person tags, phone numbers, times, and zip codes.

This r-abctools package provides tools for approximate Bayesian computation including summary statistic selection and assessing coverage. This includes recent dimension reduction algorithms to tune the choice of summary statistics, and coverage methods to tune the choice of threshold.

Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.

This package provides a fully DBI-compliant Rcpp-backed interface to PostgreSQL, a relational database.

This package provides two convenience functions assert() and test_pkg() to facilitate testing R packages.

This package extends the functionality of ggplot2, providing the capability to plot ternary diagrams for (a subset of) the ggplot2 geometries. Additionally, ggtern has implemented several new geometries which are unavailable to the standard ggplot2 release.

This package can compute multivariate normal and t-probabilities, quantiles, random deviates and densities.

GAMs, GAMMs and other generalized ridge regression with multiple smoothing parameter estimation by GCV, REML or UBRE/AIC. The library includes a gam() function, a wide variety of smoothers, JAGS support and distributions beyond the exponential family.

This package provides helper functions that act as wrappers to more advanced statistical methods with the advantage of having sane defaults for quick reporting.

This package provides various tools for creating iterators, many patterned after functions in the Python itertools module, and others patterned after functions in the snow package.

This package provides procedures for model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects. Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), are supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models.

This package provides an R interface to the NCBI's EUtils API, allowing users to search databases like GenBank PubMed, process the results of those searches and pull data into their R sessions.

The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at http://dmg.org/. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products.

This is yet another command-line argument parser which wraps the powerful Perl module Getopt::Long and with some adaptation for easier use in R. It also provides a simple way for variable interpolation in R.

This package provides a recursively partitioned mixture model for Beta and Gaussian mixtures. This is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchical clustering, but also similar to finite mixture models.

This package provides an mlr3 extension that provides various resampling-based confidence interval (CI) methods for estimating the generalization error. These CI methods are implemented as mlr3 measures, enabling the evaluation of individual algorithms on specific tasks as well as the comparison of different learning algorithms.

This package estimates the parameters in Dirichlet-Multinomial and computes log-likelihoods.