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.

Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>; Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).

This package provides functions to fit geostatistical data. The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods.

Defines window or bin boundaries for the analysis of genomic data. Boundaries are based on the inflection points of a cubic smoothing spline fitted to the raw data. Along with defining boundaries, a technique to evaluate results obtained from unequally-sized windows is provided. Applications are particularly pertinent for, though not limited to, genome scans for selection based on variability between populations (e.g. using Wright's fixations index, Fst, which measures variability in subpopulations relative to the total population).

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.

Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, calculating inbreeding coefficients (Meuwissen & Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.

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

This package performs end-to-end analysis of gene clustersâ such as photosynthesis, carbon/nitrogen/sulfur cycling, carotenoid, antibiotic, or viral marker genes (e.g., capsid, polymerase, integrase)â from genomes and metagenomes. It parses Basic Local Alignment Search Tool (BLAST) results in tab-delimited format produced by tools like NCBI BLAST+ and Diamond BLASTp, filters Open Reading Frames (ORFs) by length, detects contiguous clusters of reference genes, optionally extracts genomic coordinates, merges functional annotations, and generates publication-ready arrow plots. The package works seamlessly with or without the coding sequences input and skips plotting when no functional groups are found. For more details see Li et al. (2023) <doi:10.1038/s41467-023-42193-7>.

Organize a so-called ragged array as generalized arrays, which is simply an array with sub-dimensions denoting the subdivision of dimensions (grouping of members within dimensions). By the margins (names of dimensions and sub-dimensions) in generalized arrays, operators and utility functions provided in this package automatically match the margins, doing map-reduce style parallel computation along margins. Generalized arrays are also cooperative to R's native functions that work on simple arrays.

New multi-sample tests for testing whether multiple samples are from the same distribution. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2022) <arXiv:2205.13787>.

This package provides multiple palettes based on pride flags with tailored themes.

This package contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.

This package provides tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables, and functions to work with hierarchical/multilevel batches of parameters (Fernández-i-Marà n, 2016 <doi:10.18637/jss.v070.i09>).

Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.

This package provides functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

This package provides a collection of Geoms for R's ggplot2 library. geom_shadowpath(), geom_shadowline(), geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a neon glow around lines to get a steampunk style.

This package provides adaptive association tests for SNP level, gene level and pathway level analyses.

Focused on extracting important data from track points such as speed, distance, elevation difference and azimuth.(PLAZA, J. et al., 2022) <doi:10.1016/j.applanim.2022.105643>.

This package implements the G-Formula method for causal inference with time-varying treatments and confounders using Bayesian multiple imputation methods, as described by Bartlett et al (2025) <doi:10.1177/09622802251316971>. It creates multiple synthetic imputed datasets under treatment regimes of interest using the mice package. These can then be analysed using rules developed for analysing multiple synthetic datasets.

Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

This package provides tools for estimating forest metrics such as stem volume, biomass, and carbon using regional allometric equations. The package implements widely used models including Dagnelie P., Rondeux J. & Palm R. (2013, ISBN:9782870161258) "Cubage des arbres et des peuplements forestiers - Tables et equations" <https://orbi.uliege.be/handle/2268/155356>, Vallet P., Dhote J.-F., Le Moguedec G., Ravart M. & Pignard G. (2006) "Development of total aboveground volume equations for seven important forest tree species in France" <doi:10.1016/j.foreco.2006.03.013>, Pauwels D. & Rondeux J. (1999, ISSN:07779992) "Tarifs de cubage pour les petits bois de meleze (Larix sp.) en Ardenne" <https://orbi.uliege.be/handle/2268/96128>, Massenet J.-Y. (2006) "Chapitre IV: Estimation du volume" <https://jymassenet-foret.fr/cours/dendrometrie/Coursdendrometriepdf/Dendro4-2009.pdf>, France Valley (2025) "Bilan Carbone Forestier - Methodologie" <https://www.france-valley.com/hubfs/Bilan%20Carbone%20Forestier.pdf>. Its modular structure allows transparent integration of bibliographic or user-defined allometric relationships.

Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) <DOI:10.1038/s41598-019-50229-6>.

Represents generalized geometric ellipsoids with the "(U,D)" representation. It allows degenerate and/or unbounded ellipsoids, together with methods for linear and duality transformations, and for plotting. Thus ellipsoids are naturally extended to include lines, hyperplanes, points, cylinders, etc. This permits exploration of a variety to statistical issues that can be visualized using ellipsoids as discussed by Friendly, Fox & Monette (2013), Elliptical Insights: Understanding Statistical Methods Through Elliptical Geometry <doi:10.1214/12-STS402>.

This package provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) <https://www3.stat.sinica.edu.tw/statistica/J12N1/J12N11/J12N11.html> and further extended by Wu, Tien, and Chen (2010) <doi:10.1016/j.csda.2008.09.029>.