An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.

R provides fantastic tools for changepoint analysis, but plots generated by the tools do not have the ggplot2 style. This tool, however, combines changepoint', changepoint.np and ecp together, and uses ggplot2 to visualize changepoints.

Full descriptive statistics, physical description of sediment, metric or phi sieves. Includes a Shiny web application for interactive grain size analysis and visualization.

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>.

Provide specialized ggplot2 layers and scales for spatial uncertainty visualization, including bivariate choropleth maps, pixel maps, glyph maps, and exceedance probability maps.

Perform the Blinder-Oaxaca decomposition for generalized linear model with bootstrapped standard errors. The twofold and threefold decomposition are given, even the generalized linear model output in each group.

Implementation of a common set of punctual solutions for Cooperative Game Theory.

Estimation of covariance matrices as solutions of continuous time Lyapunov equations. Sparse coefficient matrix and diagonal noise are estimated with a proximal gradient method for an l1-penalized loss minimization problem. Varando G, Hansen NR (2020) <arXiv:2005.10483>.

This package provides functions for greenhouse gas flux calculation from chamber measurements.

Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from GENEActiv <https://activinsights.com/>, .bin-format from GENEA devices (not for sale), and .cwa-format from Axivity <https://axivity.com>. Further, it has functions for reading text files with epoch level aggregates from Actical', Fitbit', Actiwatch', ActiGraph', and PhilipsHealthBand'. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.

Providing access to the API for Gas Infrastructure Europe's natural gas transparency platforms <https://agsi.gie.eu/> and <https://alsi.gie.eu/>. Lets the user easily download metadata on companies and gas storage units covered by the API as well as the respective data on regional, country, company or facility level.

Modified versions of the lag() and summary() functions: glag() and gsummary(). The prefix g is a reminder of who to blame if things do not work as they should.

Reconstruction of muscle fibers from image stacks using textural analysis. Includes functions for tracking, smoothing, cleaning, plotting and exporting muscle fibers. Also calculates basic fiber properties (e.g., length and curvature).

Allows for easy creation of diagnostic plots for a variety of model objects using the Grammar of Graphics. Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.

The ggplot2 package provides a powerful set of tools for visualising and investigating data. The ggsoccer package provides a set of functions for elegantly displaying and exploring soccer event data with ggplot2'. Providing extensible layers and themes, it is designed to work smoothly with a variety of popular sports data providers.

This package provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.

Data sets included here are for use with package GEOmap. These include world map, USA map, Coso map, Japan Map.

Turn irregular polygons (such as geographical regions) into regular or hexagonal grids. This package enables the generation of regular (square) and hexagonal grids through the package sp and then assigns the content of the existing polygons to the new grid using the Hungarian algorithm, Kuhn (1955) (<doi:10.1007/978-3-540-68279-0_2>). This prevents the need for manual generation of hexagonal grids or regular grids that are supposed to reflect existing geography.

Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids. Two main functions: Run_Clustering() that regroups individuals with a same genotype based on proximity and Run_Genotyping() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny().

This package provides functions for estimating a generalized partial linear model, a semiparametric variant of the generalized linear model (GLM) which replaces the linear predictor by the sum of a linear and a nonparametric function.

Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.

Generalized Turnbull's estimator proposed by Dehghan and Duchesne (2011).

This package provides a suite of tools for specifying and examining experimental designs related to choice response time models (e.g., the Diffusion Decision Model). This package allows users to define how experimental factors influence one or more model parameters using R-style formula syntax, while also checking the logical consistency of these associations. Additionally, it integrates with the ggdmc package, which employs Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampling to optimise model parameters. For further details on the model-building approach, see Heathcote, Lin, Reynolds, Strickland, Gretton, and Matzke (2019) <doi:10.3758/s13428-018-1067-y>.

Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>. The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the ncvreg package (Breheny and Huang (2011) <doi:10.1214/10-AOAS388>) and grpreg package (Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>), with appropriate modifications to account for the estimation and penalization of the random effects. The ncvreg and grpreg packages also describe the MCP, SCAD, and LASSO penalties.