This package provides a wrapper for several FFTW functions. It provides access to the two-dimensional FFT, the multivariate FFT, and the one-dimensional real to complex FFT using the FFTW3 library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The FFT functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.

This package implements several Approximate Bayesian Computation (ABC) algorithms for performing parameter estimation, model selection, and goodness-of-fit. Cross-validation tools are also available for measuring the accuracy of ABC estimates, and to calculate the misclassification probabilities of different models.

This package allows for the imputation of the last largest censored observantions. This method brings less bias and more efficient estimates for AFT models.

This package provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. It additionally supplies a modelling function (wdm) and further methods for the resulting object.

This package implements an empirical Bayes approach for large-scale hypothesis testing and false discovery rate estimation.

With this package it is possible to define parameter spaces, constraints and dependencies for arbitrary algorithms, and to program on such spaces. It also includes statistical designs and random samplers. Objects are implemented as R6 classes.

This package contains functions useful for data screening, testing moderation, mediation and estimating power.

The fstlib library provides multithreaded serialization of compressed data frames using the fst format. The fst format allows for random access of stored data and compression with the LZ4 and ZSTD compressors.

This package provides cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key.

This package contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library. All models return coda mcmc objects that can then be summarized using the coda package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

This package provides advanced tryCatch and try functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).

Pdist computes the euclidean distance between rows of a matrix X and rows of another matrix Y. Previously, this could be done by binding the two matrices together and calling dist, but this creates unnecessary computation by computing the distances between a row of X and another row of X, and likewise for Y. Pdist strictly computes distances across the two matrices, not within the same matrix, making computations significantly faster for certain use cases.

This package provides tools for data frame summaries, cross-tabulations, weight-enabled frequency tables and common univariate statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.

This package provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous testing bands to asses significance of sample deviation from a reference distribution.

This package provides an improved implementation (based on k-nearest neighbors) of the density peak clustering algorithm, originally described by Alex Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets (> 100,000 samples) very efficiently.

This package provides functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

This package provides classes and methods for spatial objects that have a registered time column, in particular for irregular spatiotemporal data. The time column can be of any type, but needs to be ordinal. Regularly laid out spatiotemporal data (vector or raster data cubes) are handled by package stars'.

This package is an R wrapper around the cubature C library for adaptive multivariate integration over hypercubes. This version provides both hcubature and pcubature routines in addition to a vector interface.

Call job::job(<code here>) to run R code as an RStudio job and keep your console free in the meantime. This allows for a productive workflow while testing (multiple) long-running chunks of code. It can also be used to organize results using the RStudio Jobs GUI or to test code in a clean environment. Two RStudio Addins can be used to run selected code as a job.

The R kernel for the Jupyter environment executes R code which the front-end (Jupyter Notebook or other front-ends) submits to the kernel via the network.

This package provides a collection of helper functions designed to help you to better understand object oriented programming in R, particularly using S3.

This package provides a lightweight unit testing framework. Main features:

1. install tests with the package;

2. test results are treated as data that can be stored and manipulated;

3. test files are R scripts interspersed with test commands, that can be programmed over;

4. fully automated build-install-test sequence for packages;

5. skip tests when not run locally (e.g. on CRAN);

6. flexible and configurable output printing;

7. compare computed output with output stored with the package;

8. run tests in parallel;

9. extensible by other packages;

10. report side effects.

This package extends the ggplot2 plotting system to support network visualization. Inspired by ggtree, ggtangle is designed to work with network associated data.

This package provides tools that allow you to recreate the parsing, evaluation and display of R code, with enough information that you can accurately recreate what happens at the command line. The tools can easily be adapted for other output formats, such as HTML or LaTeX.