This package performs Gamma regression, where both mean and shape parameters follows lineal regression structures.

Connecting spatiotemporal exposure to individual and population-level risk via source-to-outcome continuum modeling. The package, methods, and case-studies are described in Messier, Reif, and Marvel (2025) <doi:10.1186/s40246-024-00711-8> and Eccles et al. (2023) <doi:10.1016/j.scitotenv.2022.158905>.

Identifies biomarkers that exhibit differential response dynamics by time across groups and estimates kinetic properties of biomarkers.

Facilitates the creation of page layout visualizations in which words are represented as rectangles with sizes relating to the length of the words. Which then is divided in lines and pages for easy overview of up to quite large texts.

This package provides a dataset about movies. This was previously contained in ggplot2, but has been moved its own package to reduce the download size of ggplot2.

Send error reports to the Google Error Reporting service <https://cloud.google.com/error-reporting/> and view errors and assign error status in the Google Error Reporting user interface.

Introduces a Copilot'-like completion experience, but it knows how to talk to the objects in your R environment. ellmer chats are integrated directly into your RStudio and Positron sessions, automatically incorporating relevant context from surrounding lines of code and your global environment (like data frame columns and types). Open the package dialog box with a keyboard shortcut, type your request, and the assistant will stream its response directly into your documents.

Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. For more information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>, Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015) <doi:10.1111/biom.12300>, or visit the package homepage <https://pbreheny.github.io/grpreg/>.

Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) <doi:10.1080/01621459.2013.824892>. Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>. Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.

This package provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day ('GSOD') weather data from the from the USA National Centers for Environmental Information ('NCEI'). The data were retired on 2025-08-29 and are no longer updated. Units are converted from from United States Customary System ('USCS') units to International System of Units ('SI'). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative humidity ('RH') are calculated from the original data using the improved August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags. For information on the GSOD data from NCEI', please see the GSOD readme.txt file available from, <https://www.ncei.noaa.gov/pub/data/gsod/readme.txt>.

Several yield stability analyses are mentioned in this package: variation and regression based yield stability analyses. Resampling techniques are integrated with these stability analyses. The function stab.mean() provides the genotypic means and ranks including their corresponding confidence intervals. The function stab.var() provides the genotypic variances over environments including their corresponding confidence intervals. The function stab.fw() is an extended method from the Finlay-Wilkinson method (1963). This method can include several other factors that might impact yield stability. Resampling technique is integrated into this method. A few missing data points or unbalanced data are allowed too. The function stab.fw.check() is an extended method from the Finlay-Wilkinson method (1963). The yield stability is evaluated via common check line(s). Resampling technique is integrated.

Estimates the parameters and nonparametric functions of an ARCH-m(X) model with exogenous covariates, estimates the parameters and nonparametric functions of an Additive ARCH-m(X) model with exogenous covariates, estimates the parameters of a GARCH-X model with exogenous covariates, performs hypothesis tests for the covariates returning the p-values, and performs stepwise variable selection on the exogenous covariates, and uses False Discovery Rate p-value corrections to select the exogenous variables.

This package provides a ggplot2 extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns.

Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.

Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.

Utilizing Generative Artificial Intelligence models like GPT-4 and Gemini Pro as coding and writing assistants for R users. Through these models, GenAI offers a variety of functions, encompassing text generation, code optimization, natural language processing, chat, and image interpretation. The goal is to aid R users in streamlining laborious coding and language processing tasks.

This package provides a user-friendly, highly customizable R package for building horizon plots in the ggplot2 environment.

Load polar volume and vertical profile data for aeroecological research directly into R. With getRad you can access data from several sources in Europe and the US and standardize it to facilitate further exploration in tools such as bioRad'.

An interactive document on the topic of goodness of fit analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.

This package provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

An extension of ggplot2 for creating complex genomic maps. It builds on the power of ggplot2 and tidyverse adding new ggplot2'-style geoms & positions and dplyr'-style verbs to manipulate the underlying data. It implements a layout concept inspired by ggraph and introduces tracks to bring tidiness to the mess that is genomics data.

This package performs genomic mediation analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/gr.216754.116>. It implements large scale mediation analysis and adaptively selects potential confounding variables to adjust for each mediation test from a pool of candidate confounders. The package is tailored for but not limited to genomic mediation analysis (e.g., cis-gene mediating trans-gene regulation pattern where an eQTL, its cis-linking gene transcript, and its trans-gene transcript play the roles as treatment, mediator and the outcome, respectively), restricting to scenarios with the presence of cis-association (i.e., treatment-mediator association) and random eQTL (i.e., treatment).

This package provides functions to identify European NUTS (Nomenclature of Territorial Units for Statistics) regions for geographic coordinates (latitude/longitude) using Eurostat geospatial boundaries. Includes map-based visualisation of the matched regions for validation and exploration. Designed for regional data analysis, reproducible workflows, and integration with common geospatial R packages.

Interact with the Google Cloud Vision <https://cloud.google.com/vision/> API in R. Part of the cloudyr <https://cloudyr.github.io/> project.