Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties as described in Helwig (2025) <doi:10.1080/10618600.2024.2362232>. Implements group LASSO, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), multivariate regression (multigaussian), smoothed support vector machines (svm1), squared support vector machines (svm2), logistic regression (binomial), proportional odds logistic regression (ordinal), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.

Collection of tools that facilitates data access and workflow for spatial analysis of Argentina. Includes historical information from censuses, administrative limits at different levels of aggregation, location of human settlements, among others. Since it is expected that the majority of users will be Spanish-speaking, the documentation of the package prioritizes this language, although an effort is made to also offer annotations in English.

The Genie algorithm (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>) is a robust and outlier-resistant hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). This package features its faster and more powerful version. It allows clustering with respect to mutual reachability distances, enabling it to act as a noise point detector or a version of HDBSCAN* that can identify a predefined number of clusters. The package also features an implementation of the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The Python version of genieclust is available via PyPI'.

Genomic biology is not limited to the confines of the canonical B-forming DNA duplex, but includes over ten different types of other secondary structures that are collectively termed non-B DNA structures. Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included.

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>.

Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.

This package provides functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.

This package provides a collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the INLA framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) <doi:10.1016/S2542-5196(20)30292-8>.

Maximum likelihood estimation under relational models, with or without the overall effect.

This package provides R bindings to the GGML tensor library for efficient machine learning computation. Implements core tensor operations including element-wise arithmetic, reshaping, and matrix multiplication. Supports neural network layers (attention, convolutions, normalization), activation functions, and quantization. Features optimization/training API with AdamW (Adam with Weight decay) and SGD (Stochastic Gradient Descent) optimizers, MSE (Mean Squared Error) and cross-entropy losses. Multi-backend support with CPU and optional Vulkan GPU (Graphics Processing Unit) acceleration. See <https://github.com/ggml-org/ggml> for more information about the underlying library.

Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in Thrun, M.C. and Ultsch, A.: "Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods" (2020) <DOI:10.1016/j.mex.2020.101093>.

Gaussian processes are flexible distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. This package implements two methods for scaling Gaussian process inference in Stan'. First, a sparse approximation of the likelihood that is generally applicable and, second, an exact method for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility functions are provided to compile and fit Stan models using the cmdstanr interface. References: Hoffmann and Onnela (2025) <doi:10.18637/jss.v112.i02>.

This package performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) <http://repository.ias.ac.in/89299/> and Diallel analysis as per Griffing, B. (1956) <https://www.publish.csiro.au/bi/pdf/BI9560463>.

This package provides functions to compute various germination indices such as germinability, median germination time, mean germination time, mean germination rate, speed of germination, Timson's index, germination value, coefficient of uniformity of germination, uncertainty of germination process, synchrony of germination etc. from germination count data. Includes functions for fitting cumulative seed germination curves using four-parameter hill function and computation of associated parameters. See the vignette for more, including full list of citations for the methods implemented.

Graphical tools and goodness-of-fit tests for right-censored data: 1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests, which use the empirical distribution function for complete data and are extended for right-censored data. 2. Generalized chi-squared-type test, which is based on the squared differences between observed and expected counts using random cells with right-censored data. 3. A series of graphical tools such as probability or cumulative hazard plots to guide the decision about the most suitable parametric model for the data. These functions share several features as they can handle both complete and right-censored data, and they provide parameter estimates for the distributions under study.

Annotation of ggplot2 plots with arbitrary TikZ code, using absolute data or relative plot coordinates.

Population-averaged models have been increasingly used in the design and analysis of cluster randomized trials (CRTs). To facilitate the applications of population-averaged models in CRTs, the package implements the generalized estimating equations (GEE) and matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by Li et al. (2022) <doi:10.1093/biostatistics/kxaa056> specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives a simple and efficient estimating equations approach based on the cluster-period means to estimate the intervention effects as well as correlation parameters. In addition, the package also provides functions for generating correlated binary data with specific mean vector and correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.

The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean and the standard deviation of a quantitative variable. It also flags potential outliers (together with their frequencies) that are c standard deviations away from the mean.

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.

This package provides a set of wrapper functions that mainly re-produces most of the sequence plots rendered with TraMineR::seqplot(). Whereas TraMineR uses base R to produce the plots this library draws on ggplot2'. The plots are produced on the basis of a sequence object defined with TraMineR::seqdef(). The package automates the reshaping and plotting of sequence data. Resulting plots are of class ggplot', i.e. components can be added and tweaked using + and regular ggplot2 functions.

This package provides ggplot2 extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes geom_dice() for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously.

This package provides a set of high efficient functions to decode identifiers of National Football League players.

It provides a custom ggplot2 geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. It is useful for visualizing data that spans multiple days and for highlighting diurnal patterns.