This package provides a set of predicates and assertions for checking the properties of sets. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package provides R bindings for Google's s2 library for geometric calculations on the sphere. High-performance constructors and exporters provide high compatibility with existing spatial packages, transformers construct new geometries from existing geometries, predicates provide a means to select geometries based on spatial relationships, and accessors extract information about geometries.

This package provides useful functions to deal with the haven_labelled and haven_labelled_spss classes introduced by the haven package.

With this package you can add in-app user authentication to Shiny, allowing you to secure publicly hosted apps and build dynamic user interfaces from user information.

This package provides functions for regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER initiative to bring PASSTEC 2000 functionalities to R.

This package simplifies custom CSS styling of both shiny and rmarkdown via Bootstrap Sass. It supports both Bootstrap 3 and 4 as well as their various Bootswatch themes. An interactive widget is also provided for previewing themes in real time.

This package computes fast (relative to other implementations) approximate Shapley values for any supervised learning model. Shapley values help to explain the predictions from any black box model using ideas from game theory; see doi.org/10.1007/s10115-013-0679-x for details.

This package converts between a number of bibliography formats, including BibTeX, BibLaTeX and Bibentry. It includes a port of the bibutils utilities and supports all bibliography formats and character encodings implemented in bibutils.

This package provides tools for the variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). The main applications are in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications).

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.

Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in ggplot2.

Genomic analysis of model organisms often requires the use of databases based on human data or making comparisons to patient-derived resources. This requires converting genes between human and non-human analogues. The babelgene R package provides predicted gene orthologs/homologs for frequently studied model organisms in an R-friendly tidy/long format. The package integrates orthology assertion predictions sourced from multiple databases as compiled by the HGNC Comparison of Orthology Predictions (HCOP).

Allow numbers to be presented in an English language version, one, two, three, ... Ordinals are also available, first, second, third, ... and indefinite article choice, "a" or "an".

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 library for rolling windows operations. The package enables full control over the window length, window lag, and time indices. With a runner one can apply any R function on rolling windows. The package eases work with equally and unequally spaced time series.

This package provides fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.

This package provides functions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data. It was designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.

This package provides functions for simple fixed and random effects meta-analysis for two-sample comparisons and cumulative meta-analyses. It draws standard summary plots, funnel plots, and computes summaries and tests for association and heterogeneity.

This package provides support for simple features, a standardized way to encode spatial vector data. It binds to GDAL for reading and writing data, to GEOS for geometrical operations, and to PROJ for projection conversions and datum transformations.

The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. The ggraph package is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer.

The tidyverse is a set of packages that work in harmony because they share common data representations and API design. This package is designed to make it easy to install and load multiple tidyverse packages in a single step.

This package provides functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. It boasts support for many additional probability distributions to model insurance loss amounts and loss frequency: 19 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. It also supports phase-type distributions commonly used to compute ruin probabilities.

The main function biclust() provides several algorithms to find biclusters in two-dimensional data, spectral, plaid model, xmotifs, and bimax. In addition, the package provides methods for data preprocessing (normalization and discretization), visualization, and validation of bicluster solutions.

This package implements the libyaml YAML 1.1 parser and emitter (http://pyyaml.org/wiki/LibYAML) for R.