This package provides methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics.

This package provides a ggplot2 extension for creating 3D figures. Provides 3D geoms, stats, and a coord_3d() coordinate system supporting rotation, perspective, and lighting.

This package provides a set of geometries to make line plots a little bit nicer. Use along with ggplot2 to: - Improve the clarity of line plots with many overlapping lines - Draw more realistic worms.

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.

Fits geographically weighted regression (GWR) models and has tools to diagnose and remediate collinearity in the GWR models. Also fits geographically weighted ridge regression (GWRR) and geographically weighted lasso (GWL) models. See Wheeler (2009) <doi:10.1068/a40256> and Wheeler (2007) <doi:10.1068/a38325> for more details.

This package provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models. Documentation about gRim is provided by vignettes included in this package and the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>); see citation("gRim") for details.

Extensions of "hyper data frame" (from package spatstat.geom') with a grouping structure. The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in <doi:10.1093/bioinformatics/btaf430> is not maintained in versions >= 0.4.0 (June 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.

Providing publication-ready graphs for Multiple sequence alignment. Moreover, it provides a unique solution for visualizing the multiple sequence alignment without the need to do the alignment in each run which is a big limitation in other available packages.

An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.

Reads corporate data such as board composition and compensation for companies traded at B3, the Brazilian exchange <https://www.b3.com.br/>. All data is downloaded and imported from the ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/DOC/FRE/>.

This is an add on package to GAMLSS. The purpose of this package is to allow users to defined truncated distributions in GAMLSS models. The main function gen.trun() generates truncated version of an existing GAMLSS family distribution.

This package provides tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries.

The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others.

Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" <doi:10.1101/2022.09.08.507080>.

This package provides methods and tools for the analysis of Genome Wide Identity-by-Descent ('gwid') mapping data, focusing on testing whether there is a higher occurrence of Identity-By-Descent (IBD) segments around potential causal variants in cases compared to controls, which is crucial for identifying rare variants. To enhance its analytical power, gwid incorporates a Sliding Window Approach, allowing for the detection and analysis of signals from multiple Single Nucleotide Polymorphisms (SNPs).

Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by LEP.

Fits unimodal and multimodal gambin distributions to species-abundance distributions from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. gambin is short for gamma-binomial'. The main function is fit_abundances(), which estimates the alpha parameter(s) of the gambin distribution using maximum likelihood. Functions are also provided to generate the gambin distribution and for calculating likelihood statistics.

This package provides geometric methods for nonparametric regression and density estimation on data represented as coordinate matrices or weighted graphs. Methods include local polynomial smoothing, model-averaged local polynomial smoothing, local polynomial lifting trend filtering, synchronized local polynomial lifting trend filtering, graph low-pass filtering, and Hessian-energy regression. Methodological references include Gajer and Ravel (2025) "Adaptive Geometric Regression for High-Dimensional Structured Data" <doi:10.48550/arXiv.2511.03817>, Fan and Gijbels (1996, ISBN:9780412983214), Wang et al. (2016) "Trend Filtering on Graphs" <https://www.jmlr.org/papers/v17/15-147.html>, and Kim et al. (2009) "Semi-Supervised Regression Using Hessian Energy" <https://papers.nips.cc/paper/3741-semi-supervised-regression-using-hessian-energy-with-an-application-to-semi-supervised-dimensionality-reduction>.

Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations. See: "GENLIB: an R package for the analysis of genealogical data" Gauvin et al. (2015) <doi:10.1186/s12859-015-0581-5>.

Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.

Interfaces GAMS data (*.gdx) files with data.table's using the GAMS R package gdxrrw'. The gdxrrw package is available on the GAMS wiki: <https://support.gams.com/doku.php?id=gdxrrw:interfacing_gams_and_r>.

This package provides a comprehensive generalized framework for parameter estimation and Generalized Process Capability Indices (GPCIs) under uncensored data using Importance Sampling (ImpSam). Supports user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), and survival functions (SF). Computes classical and generalized capability indices including Cpy (Maiti et al., 2010 <doi:10.1080/16843703.2010.11673233>), Spmk (Dey & Saha, 2019 <doi:10.1080/00949655.2019.1671980>), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 <doi:10.1080/02664763.2021.1971632>), CNpmc (Alotaibi et al., 2022 <doi:10.1155/2022/3135264>), CNpmkc (Saha et al., 2024 <doi:10.1142/S021853932450013X>), CNpk (Saha et al., 2018 <doi:10.1080/21681015.2018.1437793>), and Vannman's Cp(u,v) family. Generates parameter and GPCI MCMC chains via Sampling Importance Resampling (SIR) after burn-in and thinning. Provides point estimates, bias, MSE, risk values, Highest Posterior Density (HPD) intervals at 90, 95, and 99 percent levels of significance, Heidelberger and Welch MCMC convergence diagnostic, and convergence probability.

This package contains functions to create life history parameter plots from raw data. The plots are created using ggplot2', and calculations done using the tidyverse collection of packages. The package contains references to FishBase (Froese R., Pauly D., 2023) <https://www.fishbase.se/>.

Toolset to create perpendicular profile graphs and swath profiles. Method are based on coordinate rotation algorithm by Schaeben et al. (2024) <doi:10.1002/mma.9823>.