This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals. Visualizations are also available for most of these settings.

This is a package for graphical and statistical analyses of environmental data, with a focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. It provides major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. It comes with numerous built-in data sets from regulatory guidance documents and environmental statistics literature. It includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, https://link.springer.com/book/10.1007/978-1-4614-8456-1).

This package provides an implementation of sparse linear discriminant analysis, which is a supervised classification method for multiple classes. Various novel optimization approaches to this problem are implemented including alternating direction method of multipliers (ADMM), proximal gradient (PG) and accelerated proximal gradient (APG). Functions for performing cross validation are also supplied along with basic prediction and plotting functions. Sparse zero variance discriminant (SZVD) analysis is also included in the package.

This package provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class family, link-glm, lm, glm and betareg. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the enriched_glm function that has the same interface as glm but results in objects of class enriched_glm. In addition to the usual components in a glm object, enriched_glm objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.

Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi10.1007/s11222-005-4070-y>) and the Fourier inversion (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.

This package provides a collection of functions to help in the analysis of right-censored survival data. These extend the methods available in the survival package.

This package provides various functions for classification, including k-nearest neighbour, Learning Vector Quantization and Self-Organizing Maps.

This package provides an R library to generate Sankey network graphs in R and Shiny via the D3 visualization library.

Stringr is a consistent, simple and easy to use set of wrappers around the fantastic stringi package. All function and argument names (and positions) are consistent, all functions deal with "NA"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.

This package provides the means to compile user-supplied C++ functions with Rcpp and retrieve an XPtr that can be passed to other C++ components.

This package provides functions for manipulation of R documentation objects, including functions reprompt() and ereprompt() for updating Rd documentation for functions, methods and classes; it also includes Rd macros for citations and import of references from bibtex files for use in Rd files and roxygen2 comments, as well as many functions for manipulation of references and Rd files.

This package provides pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. It currently supports rexp.proto for serialized R objects, geobuf.proto for binary geojson, and mvt.proto for vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, hence the entire serialization is optimized at compile time. The RProtoBuf package on the other hand uses the protobuf runtime library to provide a general-purpose toolkit for reading and writing arbitrary protocol-buffer data in R.

This R package provides functions to create formattable vectors and data frames. Formattable vectors are printed with text formatting, and formattable data frames are printed with multiple types of formatting in HTML to improve the readability of data presented in tabular form rendered in web pages.

This is a package for fast and user-friendly estimation of econometric models with multiple fixed-effects. It includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018). It further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.

This package provides all final tables of Germany's highest football league, the Bundesliga. It contains data from 1964 to 2016.

This package provides a cross-platform command-line argument parser written purely in R with no external dependencies. It is useful with the Rscript front-end and facilitates turning an R script into an executable script.

This package provides S3 classes and methods for one-dimensional normal mixture models, for, e.g., density estimation or clustering algorithms research and teaching; it provides the widely used Marron-Wand densities. It also provides tools for efficient random number generation and graphics.

Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With this package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print to the console with their total size and can be easily tested locally before being sent to a remote.

This package converts between GeoJSON and Simple Feature objects.

This package provides color schemes for maps (and other graphics) designed by Cynthia Brewer as described at http://colorbrewer2.org

Visualise complex relations in texts. This is done by providing functionalities for displaying text co-occurrence networks, text correlation networks, dependency relationships as well as text clustering. Feel free to join the effort of providing interesting text visualisations.

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 simple HTTP client, with tools for making HTTP requests, and mocking HTTP requests. The package is built on R6, and takes inspiration from Ruby's faraday gem.

The tictoc package provides the timing functions tic and toc that can be nested. It provides an alternative to system.time() with a different syntax similar to that in another well-known software package. tic and toc are easy to use, and are especially useful when timing several sections in more than a few lines of code.