This package provides a C++11-style thread class and thread pool that can safely be interrupted from R.

This package implements faster versions of base R functions (e.g. mean, standard deviation, covariance, weighted mean), mostly written in C++, along with miscellaneous functions for various purposes (e.g. create the histogram with fitted probability density function or probability mass function curve, create the body mass index groups, assess the linearity assumption in logistic regression).

This package provides an implementation of dimensionality reduction via regression using Kernel Ridge Regression.

This package provides a micro-package for reading "passwords", i.e. reading user input with masking, so that the input is not displayed as it is typed. Currently, RStudio, the command line (every OS), and any platform where tcltk is present are supported.

This package provides a way to read, write and display bitmap images stored in the JPEG format with R. It can read and write both files and in-memory raw vectors.

Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.

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

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 estimates conditional Akaike information in mixed-effect models. These models are fitted using (g)lmer() from lme4, lme() from nlme, and gamm() from mgcv. The provided functions facilitate the computation of the conditional Akaike information for model evaluation.

This package provides an easy to use library to setup, apply and make inference with discrete time and discrete space hidden Markov models.

This package provides a toolkit for working with Biological Observation Matrix (BIOM) files. Features include reading/writing all BIOM formats, rarefaction, alpha diversity, beta diversity (including UniFrac), summarizing counts by taxonomic level, and sample subsetting. Standalone functions for reading, writing, and subsetting phylogenetic trees are also provided.

This package lets you assign, extract, or remove variable labels from R vectors.

This package provides tools for determining estimability of linear functions of regression coefficients, and epredict methods that handle non-estimable cases correctly.

This package provides tools to process and print UTF-8 encoded international text (Unicode). Input, validate, normalize, encode, format, and display.

This package provides utilities to help set and record the setting of the seed and the uniform and normal generators used when a random experiment is run. The utilities can be used in other functions that do random experiments to simplify recording and/or setting all the necessary information for reproducibility. See the vignette and reference manual for examples.

This package provides tools to fit and predict with the high-dimensional principal fitted components model. This model is described by Cook, Forzani, and Rothman (2012) doi:10.1214/11-AOS962.

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 implements beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>. Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1. For the classical beta regression model, alternative specifications are provided: Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for beta regression, see <doi:10.18637/jss.v048.i11>.

This package provides tools to estimate tail area-based false discovery rates as well as local false discovery rates for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold.

Tools to access data from the data web service of the OeNB, https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html.

Ggfittext is a ggplot2 extension for fitting text into boxes.

This package provides a more comfortable interface to work with R data or source files in a key-value fashion.

This package provides data used as examples to demonstrate GAMLSS models.

This package offers an interactive function for the detection of breakpoints in series.