Helper functions to accompany the Blair, Coppock, and Humphreys (2022) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" <https://book.declaredesign.org>. rdss includes datasets, helper functions, and plotting components to enable use and replication of the book.

Generates polygon straight skeletons and 3D models. Provides functions to create and visualize interior polygon offsets, 3D beveled polygons, and 3D roof models.

This package creates interactive JavaScript-based quizzes as HTML widgets. Offers three quiz types: a single question with instant feedback (singleQuestion()), a multi-question quiz with navigation, timer, and results (multiQuestions()), and fill-in-the-blank cloze exercises (fillBlanks()). All quizzes auto-detect single-choice and multiple-choice modes from the input data, support customizable styling, keyboard navigation, and multilingual UI (English, German, French, Spanish). Designed for use in R Markdown', Quarto', and Shiny applications. The singleQuestion() quiz design was inspired by Ozzie Kirkby <https://codepen.io/ozzie/pen/pvrVLm>. The multiQuestions() quiz design was inspired by Abhilash Narayan <https://codepen.io/abhilashn/pen/BRepQz>.

Reads data files acquired by Bruker Daltonics matrix-assisted laser desorption/ionization-time-of-flight mass spectrometer of the *flex series.

R functions for the computation of the truncated maximum likelihood and the robust accelerated failure time regression for gaussian and log-Weibull case.

The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in radiant.data'.

This package implements random forest Super Greedy Trees (SGTs) for regression. SGTs extend classification and regression tree splitting by fitting lasso-penalized local parametric models at tree nodes, producing sparse univariate and multivariate geometric cuts such as axis-aligned splits, hyperplanes, ellipsoids, hyperboloids, and interaction-based cuts. Trees are grown best-split-first by selecting cuts that reduce empirical risk, and ensembles provide out-of-bag error estimation, prediction on new data, variable filtering, tuning of the hcut complexity parameter, coordinate-descent lasso fitting, variable importance, and local coefficient summaries. For the underlying method, see Ishwaran (2026) <doi:10.1007/s10462-026-11541-6>.

Provide helper functions for package developers to create active bindings that looks like data embedded in the package, but are downloaded from remote sources.

The TRUST4 or MiXCR is used to identify the clonotypes. The goal of rTCRBCRr is to process the results from these clonotyping tools, and analyze the clonotype repertoire metrics based on chain names and IGH isotypes. The manuscript is still under preparation for publication for now. The references describing the methods in this package will be added later.

This package provides a low-level interface for analysing Agricultural Production Systems sIMulator ('APSIM') Next Generation simulation outputs to support structured decision-making workflows.

This package provides functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion.

Implementation of various spirometry equations in R, currently the GLI-2012 (Global Lung Initiative; Quanjer et al. 2012 <doi:10.1183/09031936.00080312>), the race-neutral GLI global 2022 (Global Lung Initiative; Bowerman et al. 2023 <doi:10.1164/rccm.202205-0963OC>), the NHANES3 (National Health and Nutrition Examination Survey; Hankinson et al. 1999 <doi:10.1164/ajrccm.159.1.9712108>) and the JRS 2014 (Japanese Respiratory Society; Kubota et al. 2014 <doi:10.1016/j.resinv.2014.03.003>) equations. Also the GLI-2017 diffusing capacity equations <doi:10.1183/13993003.00010-2017> are implemented. Contains user-friendly functions to calculate predicted and LLN (Lower Limit of Normal) values for different spirometric parameters such as FEV1 (Forced Expiratory Volume in 1 second), FVC (Forced Vital Capacity), etc, and to convert absolute spirometry measurements to percent (%) predicted and z-scores.

Bindings to kernel methods for enforcing security restrictions. AppArmor can apply mandatory access control (MAC) policies on a given task (process) via security profiles with detailed ACL definitions. In addition this package implements bindings for setting process resource limits (rlimit), uid, gid, affinity and priority. The high level R function eval.secure builds on these methods to perform dynamic sandboxing: it evaluates a single R expression within a temporary fork which acts as a sandbox by enforcing fine grained restrictions without affecting the main R process. A portable version of this function is now available in the unix package.

Generates plots from a database connection and a SGL statement. SGL is a graphics language designed to look and feel like SQL'. It is especially useful for those familiar with SQL who want to specify plots in a similar manner. The SGL language is described in Chapman (2025) <doi:10.48550/arXiv.2505.14690>.

Create tests and tasks compliant with the Question & Test Interoperability (QTI) information model version 2.1. Input sources are Rmd/md description files or S4-class objects. Output formats include standalone zip or xml files. Supports the generation of basic task types (single and multiple choice, order, pair association, matching tables, filling gaps and essay) and provides a comprehensive set of attributes for customizing tests.

This package provides a wrapper around the react-router-dom React library for use in Shiny applications and Quarto documents. Enables client-side routing with hash, memory, and browser history strategies, nested routes, dynamic segments, data loaders, actions, and navigation hooks.

Make it easy to use React in R with htmlwidget scaffolds, helper dependency functions, an embedded Babel transpiler', and examples.

This package provides tools for regression-based Boolean rule inference in artificial intelligence studies. The package fits ridge regression models on conjunction expansions and composes interpretable rule sets. Parallel execution is supported for multi-CPU environments.

This package implements identification, estimation, inference, and specification procedures for random limited-attention models, including the Random Attention Model of Cattaneo, Ma, Masatlioglu, and Suleymanov (2020) <doi:10.1086/706861> and the Attention Overload Model of Cattaneo, Cheung, Ma, and Masatlioglu (2026) <doi:10.48550/arXiv.2110.10650>. The methods use standard choice data to partially identify preferences and attention and provide simulation-based procedures for statistical inference.

Machine learning and visualization package with an S7 backend featuring comprehensive type checking and validation, paired with an efficient functional user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and unsupervised learning. All configuration steps are performed using setup functions and validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing with nested resampling. Supports data.frame', data.table', and tibble inputs, parallel execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017) <https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.

Allows one to use Osmium Tool (<https://osmcode.org/osmium-tool/>) from R. Osmium is a multipurpose command line tool that enables one to manipulate and analyze OpenStreetMap files through several different commands. Currently, this package does not aim to offer functions that cover the entire Osmium API, instead making available functions that wrap only a very limited set of its features.

Regularised discriminant analysis functions. The classical regularised discriminant analysis proposed by Friedman in 1989, including cross-validation, of which the linear and quadratic discriminant analyses are special cases. Further, the regularised maximum likelihood linear discriminant analysis, including cross-validation. References: Friedman J.H. (1989): "Regularized Discriminant Analysis". Journal of the American Statistical Association 84(405): 165--175. <doi:10.2307/2289860>. Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>. Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>.

This package provides a single method implementing multiple approaches to generate pseudo-random vectors whose components sum up to one (see, e.g., Maziero (2015) <doi:10.1007/s13538-015-0337-8>). The components of such vectors can for example be used for weighting objectives when reducing multi-objective optimisation problems to a single-objective problem in the socalled weighted sum scalarisation approach.

Load data by campaigns, ads, ad sets and insights, ad account and business manager from Facebook Marketing API into R. For more details see official documents by Facebook Marketing API <https://developers.facebook.com/documentation/ads-commerce/marketing-api>.