This package provides a collection of psychometric methods to process item metadata and use target assessment and measurement blueprint constraints to assemble a test form. Currently two automatic test assembly (ata) approaches are enabled. For example, the weighted (positive) deviations method, wdm(), proposed by Swanson and Stocking (1993) <doi:10.1177/014662169301700205> was implemented in its full specification allowing for both item selection as well as test form refinement. The linear constraint programming approach, atalp(), uses the linear equation solver by Berkelaar et. al (2014) <http://lpsolve.sourceforge.net/5.5/> to enable a variety of approaches to select items.

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) <DOI:10.1080/10618600.2023.2231048>.

Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.

This package implements the Age Band Decomposition (ABD) method for standardizing tree ring width data while preserving both low and high frequency variability. Unlike traditional detrending approaches that can distort long term growth trends, ABD decomposes ring width series into multiple age classes, detrends each class separately, and then recombines them to create standardized chronologies. This approach improves the detection of growth signals linked to past climatic and environmental factors, making it particularly valuable for dendroecological and dendroclimatological studies. The package provides functions to perform ABD-based standardization, compare results with other common methods (e.g., BAI, C method, RCS), and facilitate the interpretation of growth patterns under current and future climate variability.

Developed for use by those tasked with the routine detection, characterisation and quantification of discrete changes in air quality time-series, such as identifying the impacts of air quality policy interventions. The main functions use signal isolation then break-point/segment (BP/S) methods based on strucchange and segmented methods to detect and quantify change events (Ropkins & Tate, 2021, <doi:10.1016/j.scitotenv.2020.142374>; Ropkins et al., 2026, <doi:10.21105/joss.08839>).

This package provides an alternative algorithm for ARIMA and seasonal ARIMA model identification based on Cartesian products of user-supplied parameter sets. Rather than relying on ACF/PACF plots or stepwise search (as in auto.arima()), the package exhaustively evaluates every candidate (p,d,q)(P,D,Q)[m] combination in the requested index sets, ranks all converged models by AIC, AICc, BIC, and HQIC simultaneously, computes Akaike weights for model uncertainty quantification, supports exogenous regressors, produces ensemble forecasts, evaluates candidate models by rolling-origin (expanding window) cross-validation, and provides publication-quality diagnostic and comparison plots. A feature-based exploratory data analysis suite computes scale-free time series characteristics (trend and seasonal strength, spectral entropy, autocorrelation, lumpiness, stability) in the spirit of Hyndman, Wang and Laptev (2015), and a feature-guided automatic search narrows the Cartesian product model space before the exhaustive search runs. The algorithm is flexible, transparent, and widely applicable for quick, reproducible ARIMA model selection in both academic research and industry forecasting pipelines. Applications are demonstrated with Nigerian macroeconomic time series data.

This package provides adaptive direct sparse regression for high-dimensional multimodal data with heterogeneous missing patterns and measurement errors. AdapDISCOM extends the DISCOM framework with modality-specific adaptive weighting to handle varying data structures and error magnitudes across blocks. The method supports flexible block configurations (any K blocks) and includes robust variants for heavy-tailed distributions ('AdapDISCOM'-Huber) and fast implementations for large-scale applications (Fast-'AdapDISCOM'). Designed for realistic multimodal scenarios where different data sources exhibit distinct missing data patterns and contamination levels. Diakité et al. (2025) <doi:10.48550/arXiv.2508.00120>.

Estimate the Å estákâ Berggren kinetic model (degradation model) from experimental data. A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit, allowing for the extrapolation of the degradation of a drug product - both in time and temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate t-distribution, and analytical formulae (the delta method) are available options to calculate the confidence and prediction intervals. The results (modelling, extrapolations and statistical intervals) can be visualised with multiple plots. The examples illustrate the accelerated stability modelling in drugs and vaccines development.

This package provides methods for high-throughput adaptive immune receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In particular, immunoglobulin (Ig) sequence lineage reconstruction, lineage topology analysis, diversity profiling, amino acid property analysis and gene usage. Citations: Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>, Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.

Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.

An ASCII ruler is for measuring text and is especially useful for sequence analysis. Included in this package are methods to create ASCII rulers and associated GenBank sequence blocks, multi-column text displays that make it easy for viewers to locate nucleotides by position.

Create ASCII line graphs of a time series directly on your terminal in an easy way. There are some configurations you can add to make the plot the way you like. This project was inspired by the original asciichart package by Igor Kroitor.

This package provides functions to calculate the assortment of vertices in social networks. This can be measured on both weighted and binary networks, with discrete or continuous vertex values.

This package provides a collection of datasets on the Alone survival TV series in tidy format. Included in the package are 4 datasets detailing the survivors, their loadouts, episode details and season information.

The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; <doi:10.1037/met0000301>), Brusco et al. (2020; <doi:10.1111/bmsp.12186>), Papenberg (2024; <doi:10.1111/bmsp.12315>), Papenberg, Wang, et al. (2025; <doi:10.1016/j.crmeth.2025.101137>), Papenberg, Breuer, et al. (2025; <doi:10.1017/psy.2025.10052>), Yang et al. (2022; <doi:10.1016/j.ejor.2022.02.003>), and Wu et al. (2025; <doi:10.1016/j.cor.2025.107030>). The optimal algorithms require that an integer linear programming solver is installed. This package will install lpSolve (<https://cran.r-project.org/package=lpSolve>) as a default solver, but it is also possible to use the package Rglpk (<https://cran.r-project.org/package=Rglpk>), which requires the GNU linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>), the package Rsymphony (<https://cran.r-project.org/package=Rsymphony>), which requires the SYMPHONY ILP solver (<https://github.com/coin-or/SYMPHONY>), or the commercial solver Gurobi, which provides its own R package that is not available via CRAN (<https://www.gurobi.com/downloads/>). Rglpk', Rsymphony', gurobi and their system dependencies have to be manually installed by the user because they are only suggested dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering.

Simplifies the application of various energy expenditure models. The package is intended as a hub that brings together methods from a variety of other, themed packages such as Sojourn and TwoRegression'. Several methods are supported locally as well, including the linear methods of Hildebrand et al. (2014) <doi:10.1249/MSS.0000000000000289> and the non-linear adaptation by Ellingson et al. (2017) <doi:10.1088/1361-6579/aa6d00>. The package can combine output from different methods and produce standardized output in a range of units.

Builds and analyses Policy Analysis Matrices ('PAMs') for agricultural production systems. Computes private and social profitability, policy transfers, the domestic resource cost ratio, nominal protection coefficients for outputs and inputs, the effective protection coefficient, the private cost ratio, the profitability coefficient, subsidy ratios, and social cost-benefit ratios. Supports itemised farm budgets, parity prices, grouped analysis, deterministic sensitivity analysis, switching values, and correlated Monte Carlo simulation. Methods follow Monke and Pearson (1989, ISBN:0801419530) and the Food and Agriculture Organization of the United Nations (2007, ISBN:9789251057476).

Government Analysis Function recommended colours for use in charts on gov.uk to help meet accessibility guidance.

Bindings to FFmpeg <http://www.ffmpeg.org/> AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. Also offers high performance tools for reading raw audio, creating spectrograms', and converting between countless audio / video formats. This package interfaces directly to the C API and does not require any command line utilities.

Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. <DOI:10.1016/j.softx.2023.101347> or the vignettes on GitHub'.

Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform one-way and two-way ANOVA with post-hoc tests (Tukey HSD and Duncan MRT), Welch ANOVA for heteroscedastic data, and the Games-Howell post-hoc test as a robust alternative when variance homogeneity fails. Normality of residuals is assessed with the Shapiro-Wilk test and homoscedasticity with the Fligner-Killeen test; the appropriate statistical path is selected automatically based on these diagnostics. Coefficients of variation and statistical power (via one-way ANOVA power analysis) are reported alongside the post-hoc letter display. High-level wrappers allow automated multi-variable analysis with optional clustering by one or two experimental factors, with support for custom level ordering and relabeling. Results are returned as ggplot2 boxplots with mean and letter annotations, wide-format summary tables ready for publication or LaTeX rendering, and structured decision summaries for rapid agronomic interpretation. Direct export to Excel spreadsheets and high-resolution image tables is also supported. Functions follow methods widely used in agronomy, field trials, and plant breeding. Key references: Tukey (1949) <doi:10.2307/3001913>; Duncan (1955) <doi:10.2307/3001478>; Welch (1951) <doi:10.2307/2332579>; Games and Howell (1976) <doi:10.2307/2529858>; Shapiro and Wilk (1965) <doi:10.2307/2333709>; Fligner and Killeen (1976) <doi:10.2307/2529096>; Cohen (1988, ISBN:9781138892899); Wickham (2016, ISBN:9783319242750) for ggplot2'; see also agricolae <https://CRAN.R-project.org/package=agricolae> and rstatix <https://CRAN.R-project.org/package=rstatix>. Version en espanol: Conjunto de herramientas para el analisis estadistico, visualizacion y generacion de reportes en ensayos agroindustriales y agricolas. Incluye ANOVA univariado y bifactorial con pruebas post-hoc (Tukey HSD y Duncan MRT), ANOVA de Welch para datos heterocedasticos y la prueba post-hoc de Games-Howell como alternativa robusta cuando falla la homogeneidad de varianzas. La normalidad de residuos se evalua con la prueba de Shapiro-Wilk y la homogeneidad de varianzas con la prueba de Fligner-Killeen; la ruta estadistica apropiada se selecciona automaticamente segun estos diagnosticos. Se reportan coeficientes de variacion y potencia estadistica junto con las letras de separacion de medias. Los envoltorios de alto nivel permiten analisis multivariable automatizado con agrupamiento opcional por uno o dos factores experimentales, con soporte para orden y etiquetado personalizado de niveles. Los resultados se devuelven como boxplots con anotaciones de medias y letras, tablas resumen en formato ancho listas para publicacion o renderizado en LaTeX, y resumenes de decision para interpretacion agronomica rapida. Tambien se soporta exportacion directa a Excel e imagenes de alta resolucion para informes tecnicos.

It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a shiny app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).<DOI:10.1007/s42452-020-3120-6>.

This package provides a color palette generator inspired by American politics, with colors ranging from blue on the left to gray in the middle and red on the right. A variety of palettes allow for a range of applications from brief discrete scales (e.g., three colors for Democrats, Independents, and Republicans) to continuous interpolated arrays including dozens of shades graded from blue (left) to red (right). This package greatly benefitted from building on the source code (with permission) from Ram and Wickham (2015).

This package provides the data sets used to build the ArchaeoPhases vignettes. The data sets were formerly distributed with ArchaeoPhases', however they exceed current CRAN policy for package size.