An R interface to estimate structured additive regression (STAR) models with BayesX'.

Focused on (but not exclusive to) data sets hosted on PhysioNet (<https://physionet.org>), ricu provides utilities for download, setup and access of intensive care unit (ICU) data sets. In addition to functions for running arbitrary queries against available data sets, a system for defining clinical concepts and encoding their representations in tabular ICU data is presented.

The rank distance correlation <doi:10.1080/01621459.2020.1782223> is computed. Included also is a function to perform permutation based testing.

This package provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.

An Rcpp interface for Eunjeon project <http://eunjeon.blogspot.com/>. The mecab-ko and mecab-ko-dic is based on a C++ library, and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct. This package provides part-of-speech tagging and tokenization function for Korean text.

Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature. The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix (Archimbaud, Alfons, and Wilms (2025) <doi:10.48550/arXiv.2412.20802>). In addition, the package provides wrapper functions for softImpute (Mazumder, Hastie, and Tibshirani, 2010, <https://www.jmlr.org/papers/v11/mazumder10a.html>; Hastie, Mazumder, Lee, Zadeh, 2015, <https://www.jmlr.org/papers/v16/hastie15a.html>) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation.

Sample size and confidence interval calculations in reversible catalytic models, with applications in malaria research. Further details can be found in the paper by Sepúlveda and Drakeley (2015, <doi:10.1186/s12936-015-0661-z>).

Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion.

This package provides tools to read, write, visualize Protein Data Bank (PDB) files and perform some structural manipulations.

An R interface to the Chemistry Development Kit, a Java library for chemoinformatics. Given the size of the library itself, this package is not expected to change very frequently. To make use of the CDK within R, it is suggested that you use the rcdk package. Note that it is possible to directly interact with the CDK using rJava'. However rcdk exposes functionality in a more idiomatic way. The CDK library itself is released as LGPL and the sources can be obtained from <https://github.com/cdk/cdk>.

This package performs regularization of differential item functioning (DIF) parameters in item response theory (IRT) models (Belzak & Bauer, 2020) <https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization algorithm.

This package provides a tool to exchange data between R and Raven sound analysis software (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package warbleR'.

An integrated package for constructing random forest prediction intervals using a fast implementation package ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.

Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. Estimation is made using the design of Bottazzi (2004) <https://ideas.repec.org/p/ssa/lemwps/2004-14.html>, where the likelihood is maximized by several optimization procedures using the GNU Scientific Library (GSL)', translated to C++ code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric Laplace distributions.

Easily compute an aggregate ranking (also called a median ranking or a consensus ranking) according to the axiomatic approach presented by Cook et al. (2007). This approach minimises the number of violations between all candidate consensus rankings and all input (partial) rankings, and draws on a branch and bound algorithm and a heuristic algorithm to drastically improve speed. The package also provides an option to bootstrap a consensus ranking based on resampling input rankings (with replacement). Input rankings can be either incomplete (partial) or complete. Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.

Wrapper for widely used SUNDIALS software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its CVODES solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made R friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in R and in C++ ('RcppArmadillo flavor). In case of C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case rmumps package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as R or C++ functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. SUNDIALS library was published in Hindmarsh et al. (2005) <doi:10.1145/1089014.1089020>.

Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? A lineup is supposed to consist of a suspect and a number of additional members, or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup measures are descriptive, in the first instance, but since the earliest articles in the literature researchers have recognised the importance of reasoning inferentially about them. This package contains functions to compute various properties of laboratory or police lineups, and is intended for use by researchers in forensic psychology and/or eyewitness testimony research. Among others, the r4lineups package includes functions for calculating lineup proportion, functional size, various estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity ratio, ROC curves for confidence x accuracy data and the degree of similarity of faces in a lineup.

R Markdown format for reveal.js presentations, a framework for easily creating beautiful presentations using HTML.

Convert README.md to vignettes when installing packages without vignettes.

This package provides functions to read and write ImageJ (<https://imagej.net>) Region of Interest (ROI) files, to plot the ROIs and to convert them to spatstat (<https://spatstat.org/>) spatial patterns.

Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the igraph package of Csárdi, et al. (2006 <doi:10.5281/zenodo.7682609>).

Supports automated Markov chain Monte Carlo for arbitrarily structured correlation matrices. The user supplies data, a correlation matrix in symbolic form, the current state of the chain, a function that computes the log likelihood, and a list of prior distributions. The package's flagship function then carries out a parameter-at-a-time update of all correlation parameters, and returns the new state. The method is presented in Hughes (2023), in preparation.

Fast and efficient computation of rolling and expanding statistics for time-series data.

An R Interface to EPP-lab v1.0. EPP-lab is a Java program for projection pursuit using genetic algorithms written by Alain Berro and S. Larabi Marie-Sainte and is included in the package.