Automated model selection and model-averaging. Provides a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.

An extension of ggplot2 to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.

Group Bayesian Networks: This package implements the inference of group Bayesian networks based on hierarchical feature clustering, and the adaptive refinement of the grouping regarding an outcome of interest, as described in Becker et. al (2021) <doi: 10.1371/journal.pcbi.1008735>.

William S. Cleveland's book Visualizing Data is a classic piece of literature on Exploratory Data Analysis. Although it was written several decades ago, its content is still relevant as it proposes several tools which are useful to discover patterns and relationships among the data under study, and also to assess the goodness of fit o a model. This package provides functions to produce the ggplot2 versions of the visualization tools described in this book and is thought to be used in the context of courses on Exploratory Data Analysis.

Generates a file, containing the main scientific references, prepared to be automatically inserted into an academic paper. The articles present in the list are chosen from the main references generated, by function principal_lister(), of the package bibliorefer'. The generated file contains the list of metadata of the principal references in BibTex format. Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>. Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>. Hamid DerviÅ . (2019) <doi:10.5530/jscires.8.3.32>.

In computationally demanding data analysis pipelines, the targets R package (2021, <doi:10.21105/joss.02959>) maintains an up-to-date set of results while skipping tasks that do not need to rerun. This process increases speed and increases trust in the final end product. However, it also overwrites old output with new output, and past results disappear by default. To preserve historical output, the gittargets package captures version-controlled snapshots of the data store, and each snapshot links to the underlying commit of the source code. That way, when the user rolls back the code to a previous branch or commit, gittargets can recover the data contemporaneous with that commit so that all targets remain up to date.

Design of group sequential trials, including non-binding futility analysis at multiple time points (Gallo, Mao, and Shih, 2014, <doi:10.1080/10543406.2014.932285>).

This package implements a variant of the Self-Organizing Map (SOM) algorithm designed for mixed-attribute datasets. Similarity between observations is computed using the Gower distance, and categorical prototypes are updated via heuristic strategies (weighted mode and multinomial sampling). Provides functions for model fitting, mapping, visualization (U-Matrix and component planes), and evaluation, making SOM applicable to heterogeneous real-world data. For methodological details see Sáez and Salas (2026) <doi:10.1007/s41060-025-00941-6>.

Easy access to official spatial data sets of Brazil as sf objects in R. The package includes a wide range of geospatial data available at various geographic scales and for various years with harmonized attributes, projection and fixed topology.

This package provides tools for planning and simulating recurrent event trials with overdispersed count endpoints analyzed using negative binomial (or Poisson) rate models. Implements sample size and power calculations for fixed designs with variable accrual, dropout, maximum follow-up, and event gaps, including methods of Zhu and Lakkis (2014) <doi:10.1002/sim.5947> and Friede and Schmidli (2010) <doi:10.3414/ME09-02-0060>. Supports group sequential designs by adding calendar-time analysis schedules compatible with the gsDesign package and by estimating blinded information at interim looks. Includes simulation utilities for recurrent events (including seasonal rates), interim data truncation, and Wald-based inference for treatment rate ratios.

This package provides a reproducible pipeline to conduct genomeâ wide association studies (GWAS) and extract singleâ nucleotide polymorphisms (SNPs) for a human trait or disease. Given aggregated GWAS dataset(s) and a userâ defined significance threshold, the package retrieves significant SNPs from the GWAS Catalog and the Experimental Factor Ontology (EFO), annotates their gene context, and can write a harmonised metadata table in comma-separated values (CSV) format, genomic intervals in the Browser Extensible Data (BED) format, and sequences in the FASTA (text-based sequence) format with user-defined flanking regions for clustered regularly interspaced short palindromic repeats (CRISPR) guide design. For details on the resources and methods see: Buniello et al. (2019) <doi:10.1093/nar/gky1120>; Sollis et al. (2023) <doi:10.1093/nar/gkac1010>; Jinek et al. (2012) <doi:10.1126/science.1225829>; Malone et al. (2010) <doi:10.1093/bioinformatics/btq099>; Experimental Factor Ontology (EFO) <https://www.ebi.ac.uk/efo>.

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations errors association.

This small collection of functions provides what we call elemental graphics for display of analysis of variance results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) <doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich <https://jse.amstat.org/v17n1/helmreich.html>. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular analysis of variance methods. These functions can be particularly helpful for students and non-statistician analysts. But these methods should be quite generally helpful for work-a-day applications of all kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data.

This General Regression Neural Networks Package uses various distance functions. It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous published paper Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>. This package includes various functions, although "euclidean" distance is used traditionally.

Add mean comparison annotations to a ggplot'. This package provides an easy way to indicate if two or more groups are significantly different in a ggplot'. Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).

This package implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. glmmfields uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with Stan'. References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.

This package creates bar plots with rounded corners using ggplot2'. The code in this package was adapted from a solution provided by Stack Overflow user sthoch in the following post <https://stackoverflow.com/questions/62176038/r-ggplot2-bar-chart-with-round-corners-on-top-of-bar>.

Calculate, plot and animate the configuration of Jupiter's four largest satellites (known as Galilean satellites) for a given date and time (UTC - Coordinated Universal Time). The galsat() function returns numerical values of the satellitesâ positions. x â the apparent rectangular coordinate of the satellite with respect to the center of Jupiterâ s disk in the equatorial plane in the units of Jupiterâ s equatorial radius; X is positive toward the west, y â the apparent rectangular coordinate of the satellite with respect to the center of Jupiterâ s disk from the equatorial plane in the units of Jupiterâ s equatorial radius; Y is positive toward the north. For more details see Meeus (1988, ISBN 0-943396-22-0) "Astronomical Formulae for Calculators". The galsat_animate() function creates an animation of the Galilean satellites positions. You provide the starting time, duration, the time step between frames, and the pause between frames. The function delta_t() returns the value of delta-T in units of seconds.

Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. Converts images to GIF animations using pngquant's efficient cross-frame palettes and temporal dithering with thousands of colors per frame.

This package provides tools for decomposing Global Value Chain (GVC) participation and value-added trade. It implements the frameworks proposed by Borin and Mancini (2023) 10.1080/09535314.2022.2153221> for source-based and sink-based decompositions, and by Borin, Mancini, and Taglioni (2025) 10.1093/wber/lhaf017> for tripartite and output-based GVC measures.

Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

This package implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm (Robins (1986) <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). The g-formula can estimate an outcome's counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders. This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments. See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.

The geom_rain() function adds different geoms together using ggplot2 to create raincloud plots.

This package provides functions and methods for: splitting large raster objects into smaller chunks, transferring images from a binary format into raster layers, transferring raster layers into an RData file, calculating the maximum gap (amount of consecutive missing values) of a numeric vector, and fitting harmonic regression models to periodic time series. The homoscedastic harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000) <doi:10.1080/014311600209814>.