Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors.

The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers etc. Because of its unified API, there is no need to modify any code in order to switch from sequential on the local machine to, say, distributed processing on a remote compute cluster.

This package parses a fitted R model object, and returns a formula in Tidy Eval code that calculates the predictions. It works with several database backends because it leverages dplyr and dbplyr for the final SQL translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.

This is a package for slanted matrices and ordered clustering for better visualization of similarity data.

R-hub uses GitHub Actions to run R CMD check and similar package checks. The rhub package helps you set up R-hub for your R package, and start running checks.

This package provides functions to build tables with advanced layout elements such as row spanners, column spanners, table spanners, zebra striping, and more. While allowing advanced layout, the underlying CSS-structure is simple in order to maximize compatibility with word processors such as LibreOffice. The package also contains a few text formatting functions that help outputting text compatible with HTML or LaTeX.

This package contains:

• facilities for working with grouped data: do something to data stratified by some variables.

• implementations of least-squares means, general linear contrasts, and

• miscellaneous other utilities.

This package provides functions for testing affine hypotheses on the regression coefficient vector in regression models with autocorrelated errors.

This package provides a convenience wrapper that uses the rmarkdown package to render small snippets of code to target formats that include both code and output. The goal is to encourage the sharing of small, reproducible, and runnable examples on code-oriented websites or email. reprex also extracts clean, runnable R code from various common formats, such as copy/paste from an R session.

This package provides syntax highlighting of R code, specifically designed for the needs of RMarkdown packages like pkgdown, hugodown, and bookdown. It includes linking of function calls to their documentation on the web, and automatic translation of ANSI escapes in output to the equivalent HTML.

This package provides functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types. Switching output format requires minimal changes in code. This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.

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

This package provides functionality to define and train neural networks similar to PyTorch but written entirely in R using the libtorch library. It also supports low-level tensor operations and GPU acceleration.

This package provides an R Markdown format for converting an R Markdown document to a grid-oriented dashboard. The dashboard flexibly adapts the size of its components to the containing web page.

This package provides functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.

When testing multiple hypotheses simultaneously, this package provides functionality to calculate a lower bound for the number of correct rejections (as a function of the number of rejected hypotheses), which holds simultaneously -with high probability- for all possible number of rejections. As a special case, a lower bound for the total number of false null hypotheses can be inferred. Dependent test statistics can be handled for multiple tests of associations. For independent test statistics, it is sufficient to provide a list of p-values.

This package fits multivariate generalized linear mixed models and related models. This is done using Markov chain Monte Carlo techniques.

This package provides a collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The BigCamelCase style was consequently applied to functions borrowed from contributed R packages as well.

For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code. Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).

This package provides functions for computing the density and the distribution function of multivariate normal and "t" random variables, and for generating random vectors sampled from these distributions. Probabilities are computed via non-Monte Carlo methods.

This is a package for stubbing and setting expectations on HTTP requests. It includes tools for stubbing HTTP requests, including expected request conditions and response conditions. You can match on HTTP method, query parameters, request body, headers and more. It can be used for unit tests or outside of a testing context.

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 is an implementation of about 6 major classes of statistical regression models. Currently only fixed-effects models are implemented, i.e., no random-effects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate generalised linear models.

The choices of color palettes in R can be quite overwhelming with palettes spread over many packages with many different API's. This package aims to collect all color palettes across the R ecosystem under the same package with a streamlined API.