This package provides functions to handle basic input output. These functions always read and write UTF-8 (8-bit Unicode Transformation Format) files and provide more explicit control over line endings.

This package provides functionality for random generation of spatial data in the spatstat family of packages. It generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes (simple sequential inhibition, Matern inhibition models, Matern cluster process, Neyman-Scott cluster processes, log-Gaussian Cox processes, product shot noise cluster processes) and simulation of Gibbs point processes (Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler).

This package provides a dependency management toolkit for R. Using renv, you can create and manage project-local R libraries, save the state of these libraries to a lockfile, and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.

This package provides tools to accurately benchmark and analyze execution times for R expressions.

This package provides a simple and light-weight API for memory profiling of R expressions. The profiling is built on top of R's built-in memory profiler utils::Rprofmem(), which records every memory allocation done by R (also native code).

This package generates ROC plots. Most ROC curve plots obscure the cutoff values and inhibit interpretation and comparison of multiple curves. This attempts to address those shortcomings by providing plotting and interactive tools. Functions are provided to generate an interactive ROC curve plot for web use, and print versions. A Shiny application implementing the functions is also included.

This package provides drop-in replacements for the base system2() function with fine control and consistent behavior across platforms. It supports clean interruption, timeout, background tasks, and streaming STDIN / STDOUT / STDERR over binary or text connections. The package also provides functions for evaluating expressions inside a temporary fork. Such evaluations have no side effects on the main R process, and support reliable interrupts and timeouts. This provides the basis for a sandboxing mechanism.

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

This package provides classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).

ACDm is a package for Autoregressive Conditional Duration (ACD, Engle and Russell, 1998) models. It creates trade, price or volume durations from transactions (tic) data, performs diurnal adjustments, fits various ACD models and tests them.

This package provides tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). The area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

This package is a collection of baseline correction algorithms. Beside those it provides a framework and a Tcl/Tk enabled GUI for optimizing baseline algorithm parameters. Typical use is the removal of the background effects from spectra, which are originating from various types of spectroscopy and spectrometry. Also, there is a possibility of optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.

This package provides an R interface to Google's BigQuery database.

Servr provides an HTTP server in R to serve static files, or dynamic documents that can be converted to HTML files (e.g., R Markdown) under a given directory.

This package provides tools for the maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); it includes inference and basic methods.

The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally.

This package is interface to NLopt, a library for nonlinear optimization. NLopt is a library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms.

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 is a collection of data analysis tools. It includes tools for regression outlier detection in a fitted linear model, stationary bootstrap using a truncated geometric distribution, a comprehensive test for weak stationarity, column means by group, weighted biplots, and a heuristic to obtain a better initial configuration in non-metric MDS.

This package contains supporting data sets that are used in other packages maintained by Torsten Hothorn.

This package provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.

This package provides bitmapped vectors of booleans (no NAs), coercion from and to logicals, integers and integer subscripts, fast boolean operators and fast summary statistics. With bit class vectors of true binary booleans, TRUE and FALSE can be stored with 1 bit only.

This lightweight package that adds progress bar to vectorized R functions apply. The implementation can easily be added to functions where showing the progress is useful e.g. bootstrap.

This is a package for variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities.