Functions implemented in this package allow coercing (i.e. convert) network data between classes provided by other R packages. Currently supported classes are those defined in packages network and igraph.

This package provides a hiredis wrapper that includes support for transactions, pipelining, blocking subscription, serialisation of all keys and values, Redis error handling with R errors. It includes an automatically generated R6 interface to the full hiredis API. Generated functions are faithful to the hiredis documentation while attempting to match R's argument semantics. Serialization must be explicitly done by the user, but both binary and text-mode serialisation is supported.

This package provides output formats and utilities for authoring books and technical documents with R Markdown.

This package provides a general toolkit for downloading, managing, analyzing, and presenting data from the U.S. Census, including SF1 (Decennial short-form), SF3 (Decennial long-form), and the American Community Survey (ACS). Confidence intervals provided with ACS data are converted to standard errors to be bundled with estimates in complex acs objects. The package provides new methods to conduct standard operations on acs objects and present/plot data in statistically appropriate ways.

This package provides the prediction() function, a type-safe alternative to predict() that always returns a data frame. The package currently supports common model types (e.g., "lm", "glm") from the stats package, as well as numerous other model classes from other add-on packages.

This package provides an implementation of an algorithm for general-purpose unconstrained non-linear optimization. The algorithm is of quasi-Newton type with BFGS updating of the inverse Hessian and soft line search with a trust region type monitoring of the input to the line search algorithm. The interface of ucminf is designed for easy interchange with the package optim.

This package provides kernel smoothers for univariate and multivariate data, including density functions, density derivatives, cumulative distributions, modal clustering, discriminant analysis, and two-sample hypothesis testing.

This package provides logicless templating, with a syntax that is not limited to R.

This package provides grid grobs that fill in a user-defined area with various patterns. It includes enhanced versions of the geometric and image-based patterns originally contained in the ggpattern package as well as original pch, polygon_tiling, regular_polygon, rose, text, wave, and weave patterns plus support for custom user-defined patterns.

This package provides full screen and partial loading screens for Shiny with spinners, progress bars, and notifications.

This is a package for the analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported.

This package implements many algorithms for statistical learning on sparse matrices: matrix factorizations, matrix completion, elastic net regressions, factorization machines. The rsparse package also enhances the Matrix package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format.

Customize Bootstrap and Bootswatch themes, like colors, fonts, grid layout, to use in Shiny applications, rmarkdown documents and flexdashboard.

This package implements an R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory).

This package defines the generic method extract and provides openMP support as needed in several packages like aws, adimpro, fmri, and dwi.

This package provides functions related to human natural ordering. It handles adjacent digits in a character sequence as a number so that natural sort function arranges a character vector by their numbers, not digit characters.

This package provides a set of functions to generate high-resolution Venn and Euler plots. It includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.

This package provides an R implementation of an extension of the BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.

This package provides a %dopar% adapter such that any type of futures can be used as backends for the foreach framework.

This package provides response time distributions (density/PDF, distribution function/CDF, quantile function, and random generation):

• Ratcliff diffusion model (Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C code by Andreas and Jochen Voss and

• linear ballistic accumulator (LBA; Brown & Heathcote, 2008, <doi:10.1016/j.cogpsych.2007.12.002>) with different distributions underlying the drift rate.

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.

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.

The R package data.table is an extension of data.frame providing functions for fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group, column listing and fast file reading.

Format dates and times flexibly and to whichever locales make sense. This package parses dates, times, and date-times in various formats (including string-based ISO 8601 constructions). The formatting syntax gives the user many options for formatting the date and time output in a precise manner. Time zones in the input can be expressed in multiple ways and there are many options for formatting time zones in the output as well. Several of the provided helper functions allow for automatic generation of locale-aware formatting patterns based on date/time skeleton formats and standardized date/time formats with varying specificity.