Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches).

This package provides a simple, consistent interface to working with XML files in R. It is built on top of the libxml2 C library.

This package provides a set of handy functions. It includes a versatile one line progress bar, one line function timer with detailed output, time delay function, text histogram, object preview, CRAN package search, simpler package installer, Linux command install check, a flexible Mode function, top function, simulation of correlated data, and more.

This package contains the data set for the crowd-sourced benchmarks from running the benchmarkme package.

This package provides a set of functions for sparse matrix algebra. Differences with other sparse matrix packages are:

1. it only supports (essentially) one sparse matrix format;

2. it is based on transparent and simple structure(s);

3. it is tailored for MCMC calculations within G(M)RF;

4. and it is fast and scalable (with the extension package spam64).

This package provides functions for reading ontologies into R as lists and manipulating sets of ontological terms.

Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualization for interrogating clusterings as resolution increases.

This package provides R implementations of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models.

This package lets you edit and simplify geojson, Spatial, and sf objects. This is a wrapper around the mapshaper JavaScript library to perform topologically-aware polygon simplification, as well as other operations such as clipping, erasing, dissolving, and converting multi-part to single-part geometries.

This package enables conversions between R objects and JavaScript Object Notation (JSON) using the rapidjsonr library.

This package implements an approximate string matching version of R's native match function. It can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal string alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences.

This package provides

1. pseudo random generators, such as general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators)

2. quasi random generators, such as the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence), and

3. some generator tests: the gap test, the serial test, the poker test.

See e.g. Gentle (2003) doi:10.1007/b97336.

This package provides vectorized distribution objects with tools for manipulating, visualizing, and using probability distributions. It was designed to allow model prediction outputs to return distributions rather than their parameters, allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.

This package implements the Figueiredo machine learning algorithm for adaptive sparsity and the Wong algorithm for adaptively sparse Gaussian geometric models.

This package implements core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with DE matrices and count matrices, a collection of functions for manipulating and plotting data via ggplot2, and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP, collapsing vertices of each cluster in the graph, and propagating graph labels.

This package provides a computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available.

This package is an extension to the testthat package that makes it easy to add graphical unit tests. It provides a Shiny application to manage the test cases.

Algebraic procedures for analyses of multiple social networks are delivered with this package. multiplex makes possible, among other things, to create and manipulate multiplex, multimode, and multilevel network data with different formats. Effective ways are available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup with decomposition procedures or semiring structures with the relational bundles occurring in different types of multivariate networks. multiplex provides also an algebraic approach for affiliation networks through Galois derivations between families of the pairs of subsets in the two domains of the network with visualization options.

The vegan package provides tools for descriptive community ecology. It has most basic functions of diversity analysis, community ordination and dissimilarity analysis. Most of its multivariate tools can be used for other data types as well.

Create interactive 3D scatter plots, network plots, and globes in R using the three.js visualization library.

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.

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the INLA package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

This package provides a parallel backend for the %dopar% function using the snow package.

The first day of any MMWR week is Sunday. MMWR week numbering is sequential beginning with 1 and incrementing with each week to a maximum of 52 or 53. MMWR week #1 of an MMWR year is the first week of the year that has at least four days in the calendar year. This package provides functionality to convert dates to MMWR day, week, and year and the reverse.