This package provides an implementation of Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic single nucleotide variants (SNVs) in circulating cell free DNA (cfDNA).

These utilities facilitate the programmatic manipulations of formulas, expressions, calls, assignments and other R language objects. These objects all share the same structure: a left-hand side, operator and right-hand side. This package provides methods for accessing and modifying this structures as well as extracting and replacing names and symbols from these objects.

This package arranges data by a set of methods. It uses rearrangers to reorder data points and mutators to change their values. From basic utilities, to centering the greatest value, to swirling in 3-dimensional space, rearrr enables creativity when plotting and experimenting with data.

This package generates well-known integer sequences. The gmp package is adopted for computing with arbitrarily large numbers. Every function has a hyperlink to its corresponding item in the On-Line Encyclopedia of Integer Sequences (OEIS) in the function help page.

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

This package provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. Using this package, netCDF files can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files.

The snow package provides support for simple parallel computing on a network of workstations using R. A master R process calls makeCluster to start a cluster of worker processes; the master process then uses functions such as clusterCall and clusterApply to execute R code on the worker processes and collect and return the results on the master.

This package provides functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.

This package converts between GeoJSON and Simple Feature objects.

This package provides a production-grade AI toolkit for R featuring a layered architecture (Specification, Utilities, Providers, Core), request interception support, robust error handling with exponential retry delays, support for multiple AI model providers (OpenAI, Anthropic, etc.), local small language model inference, distributed MCP ecosystem, multi-agent orchestration, progressive knowledge loading through skills, and a global skill store for sharing AI capabilities.

This package calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (including median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package boot. Both one- and two-sided intervals are supported.

This package provides a toolkit for all URL-handling needs, including encoding and decoding, parsing, parameter extraction and modification. All functions are designed to be both fast and entirely vectorized. It is intended to be useful for people dealing with web-related datasets, such as server-side logs, although may be useful for other situations involving large sets of URLs.

This package provides tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.).

This package contains miscellaneous functions used to interpret and translate, factorize and negate Sum of Products expressions, for both binary and multi-value crisp sets, and to extract information (set names, set values) from those expressions. Other functions perform various other checks if possibly numeric (even if all numbers reside in a character vector) and coerce to numeric, or check if the numbers are whole. It also offers, among many others, a highly flexible recoding routine and a more flexible alternative to the base function with().

This package checks adherence to a given style, syntax errors and possible semantic issues. It supports on the fly checking of R code edited with RStudio IDE, Emacs and Vim.

This package provides visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).

This package provides tools for measuring inequality, concentration, and poverty measures. It provides both empirical and theoretical Lorenz curves.

This package provides implementations of the SHA-3 cryptographic hash and SHAKE256 extendable-output functions (XOF).

This package provides functions and data to construct technical trading rules with R.

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 the URL checking tools available in R 4.1+ as a package for earlier versions of R. It also uses concurrent requests so can be much faster than the serial versions.

The smurf package contains the implementation of the Sparse Multi-type Regularized Feature (SMuRF) modeling algorithm to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood. Next to the fitting procedure, following functionality is available:

• Selection of the regularization tuning parameter lambda using three different approaches: in-sample, out-of-sample or using cross-validation.

• S3 methods to handle the fitted object including visualization of the coefficients and a model summary.

This package includes functions for processing GeoJson objects relying on RFC 7946. The geojson encoding is based on json11, a tiny JSON library for C++11. Furthermore, the source code is exported in R through the Rcpp and RcppArmadillo packages.

When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions.