Implement frequent-directions algorithm for efficient matrix sketching. (Edo Liberty (2013) <doi:10.1145/2487575.2487623>).

Many functions to easily vizualise and estimate indicators such as proportions, means, medians and continuous/discrete distributions from complex survey data. The package also estimates confidence intervals for all indicators, compares different groups and computes different statistical tests.

This package provides a handy tool to calculate carbon footprints from air travel based on three-letter International Air Transport Association (IATA) airport codes or latitude and longitude. footprint first calculates the great-circle distance between departure and arrival destinations. It then uses the Department of Environment, Food & Rural Affairs (DEFRA) greenhouse gas conversion factors for business air travel to estimate the carbon footprint. These conversion factors consider trip length, flight class (e.g. economy, business), and emissions metric (e.g. carbon dioxide equivalent, methane).

This package provides clean, tidy access to economic data from the Federal Reserve Economic Data ('FRED') API <https://fred.stlouisfed.org/docs/api/fred/>. FRED is maintained by the Federal Reserve Bank of St. Louis and contains over 800,000 time series from 118 sources covering GDP, employment, inflation, interest rates, trade, and more. Dedicated functions fetch series observations, search for series, browse categories, releases, and tags, and retrieve series metadata. Multiple series can be fetched in a single call, in long or wide format. Server-side unit transformations (percent change, log, etc.) and frequency aggregation are supported, with readable transform aliases such as yoy_pct and log_diff'. Real-time and vintage helpers (built on ALFRED') return a series as it appeared on a given date, the first-release version, every revision, or a panel of selected vintages. An offline curated catalogue of around fifty popular series, NBER recession reference dates, and FOMC meeting dates support discoverability and event-study workflows. Default plot method shades NBER recession periods. Reproducibility helpers produce BibTeX or plain-text citations and YAML manifests with per-object hashes. Data is cached locally for subsequent calls. This product uses the FRED API but is not endorsed or certified by the Federal Reserve Bank of St. Louis'.

Draws forest plots of exposure effects from fitted regression models. Name an exposure and foresty plots its effect. Name an effect modifier as well and it refits the model with the interaction term, estimates the exposure effect within each level of the modifier as a linear combination of the coefficients, and reports the joint interaction test beside those estimates. It takes one exposure and one modifier at a time, so the interaction is always a two-way one. Rows of the plot and of the table beside it share one scale, in a layout that can follow a journal's house style. The same results go to a self-contained HTML page holding the subgroup estimates, the joint test and the coefficient table. The car package computes the linear combinations and their tests. Models fitted by stats::glm(), stats::lm(), the survival package, the lme4 package, the geepack package and the survey package are supported, as is any fit supplying coef() and vcov(). Ordinal outcomes are supported through the MASS package and nominal ones through the nnet package, where the figure carries one row per level of the outcome and the interaction is tested jointly across the equations. Fits from the rms package are refused, naming the function that fits the same model in their place. The estimation of an exposure effect within a level of a modifier, and the test of the difference between such estimates, follow Altman and Bland (2003) <doi:10.1136/bmj.326.7382.219> and VanderWeele and Knol (2014) <doi:10.1515/em-2013-0005>; the reporting of subgroup effects beside the interaction test follows Wang et al. (2007) <doi:10.1056/NEJMsr077003>, and the figure itself the forest plot described by Lewis and Clarke (2001) <doi:10.1136/bmj.322.7300.1479>.

This package provides a shiny web application developed by the Breeding Insight team to support pedigree validation and ancestry assessment of plant and animal populations. The app integrates Mendelian error analysis, parentage assignment and genetic composition/ancestry methods to help researchers evaluate genomic relationships through an accessible, web-based interface without requiring command-line tools. Pedigree validation, Mendelian error analysis and parentage assignment build on the BIGpopA package (<https://CRAN.R-project.org/package=BIGpopA>) and support diploid and polyploid data. Ancestry estimation uses the sparse non-negative matrix factorization method of Frichot et al. (2014) <doi:10.1534/genetics.113.160572> as implemented in the LEA package by Frichot and Francois (2015) <doi:10.1111/2041-210X.12382>. Line and breed composition are estimated using the breed composition regression method of Funkhouser et al. (2017) <doi:10.2527/tas2016.0003>, extended to polyploid species by Sandercock et al. (2025) <doi:10.1002/tpg2.70067>.

This package provides a C++ API for routinely used numerical tools such as integration, root-finding, and optimization, where function arguments are given as lambdas. This facilitates Rcpp programming, enabling the development of R'-like code in C++ where functions can be defined on the fly and use variables in the surrounding environment.

This package provides a simple way to unload none-base packages and remove all global variables.

Calculation of AHP (Analytic Hierarchy Process - <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights.

Compute inbreeding coefficients using the method of Meuwissen and Luo (1992) <doi:10.1186/1297-9686-24-4-305>, and numerator relationship coefficients between individuals using the method of Van Vleck (2007) <https://pubmed.ncbi.nlm.nih.gov/18050089/>.

This tree-based method deals with high dimensional longitudinal data with correlated features through the use of a piecewise random effect model. FREE tree also exploits the network structure of the features, by first clustering them using Weighted Gene Co-expression Network Analysis ('WGCNA'). It then conducts a screening step within each cluster of features and a selecting step among the surviving features, which provides a relatively unbiased way to do feature selection. By using dominant principle components as regression variables at each leaf and the original features as splitting variables at splitting nodes, FREE tree delivers easily interpretable results while improving computational efficiency.

This package provides high-performance implementations of Small Area Estimation (SAE) methods leveraging C++ ('Rcpp', RcppArmadillo') and OpenMP multi-threading. Supports standard area-level Fay-Herriot models (Fay and Herriot, 1979 <doi:10.1080/01621459.1979.10482505>), Spatial Fay-Herriot models (Pratesi and Salvati, 2008 <doi:10.1002/env.861>), Spatio-Temporal Fay-Herriot models (Marhuenda et al., 2013 <doi:10.1016/j.csda.2013.01.016>), and unit-level Battese-Harter-Fuller models (Battese et al., 1988 <doi:10.1080/01621459.1988.10478561>). Features include empirical best linear unbiased prediction (EBLUP), analytical and bootstrap Mean Squared Error (MSE) estimation, automatic handling of unsampled domains, and modern S3 diagnostic methods.

This package implements a fuzzy clustering approach for ordinal Likert-type data using triangular fuzzy numbers (TFNs). The package extends the classical fuzzy C-means algorithm to better handle uncertainty in ordinal scales and includes automatic selection of the number of clusters using the Xie-Beni validity index. References: Coppi, R., D'Urso, P., and Giordani, P. (2012), "Fuzzy and possibilistic clustering for fuzzy data", <doi:10.1016/j.csda.2010.09.013>. Xie, X. L. and Beni, G. (1991), "A validity measure for fuzzy clustering", <doi:10.1109/34.85677>.

Plotting flood quantiles and their corresponding probabilities (return periods) on the probability papers. The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688).

This package provides a provenance-aware framework for contextual reconstruction from file systems and related digital resource collections. The package creates reproducible snapshots of file-level metadata, paths, repository context, and optional content signatures. It supports contextual grouping, structural abstraction, temporal analysis, semantic stabilization, duplicate and reuse detection, and lightweight workflow reconstruction from file system observations. The framework deliberately separates observational evidence, contextual abstraction, semantic interpretation, and analytical reconstruction, enabling reproducible workflows that can be inspected by reviewers. It is designed to support future alignment with archival and contextual knowledge representation models, including the World Wide Web Consortium Provenance Ontology (PROV-O): Lebo et al. (2013) <https://www.w3.org/TR/prov-o/> and Records in Contexts developed by the International Council on Archives Expert Group on Archival Description (EGAD) <https://www.ica.org/ica-network/expert-groups/egad/records-in-contexts-ric/>.

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) <doi:10.48550/arXiv.1904.10265>. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

This package provides a collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.

This package provides a collection of functions for linear and non-linear regression modelling. It implements a wrapper for several regression models available in the base and contributed packages of R.

Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a shiny app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.

The Futureverse is a set of packages for parallel and distributed processing with the future package at its core (Bengtsson, 2021, <doi:10.32614/RJ-2021-048>). Another notable component is the futurize package (Bengtsson, 2026, <doi:10.48550/arXiv.2601.17578>) for turning common sequential calls into parallel ones via a single function futurize(). Similarly, the progressify() of the progressify package makes common calls to report of progress. This package is designed to make it easy to install common Futureverse packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each Futureverse package as a dependency as needed.

Uses three different correlation coefficients to calculate measurement-level adequate correlations in a feature matrix: Pearson product-moment correlation coefficient, Intraclass correlation and Cramer's V.

Allows the user to implement easily canvas elements within a shiny app or an RMarkdown document. The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes. The package relies on the fabricjs JavaScript library. See <http://fabricjs.com/>.

An implementation of the methodologies described in Xi Liu, Afshin A. Divani, and Alexander Petersen (2022) <doi:10.1016/j.csda.2022.107421>, including truncated functional linear and truncated functional logistic regression models.

This package provides functions to fit regression models for bounded continuous and discrete responses. In case of bounded continuous responses (e.g., proportions and rates), available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) <doi:10.1214/17-BA1079>), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) <doi:10.1177/1471082X18821213>), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) <doi:10.1080/0266476042000214501>), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) <doi:10.1002/sim.8406>) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials), available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) <doi:10.1002/sim.9005>), the beta-binomial, and the binomial are implemented. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) <doi:10.1201/b16018>). Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided.