In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) <doi:10.1093/biomet/asv042>, we introduce GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix. The package accompanies the paper "GLMcat: An R Package for Generalized Linear Models for Categorical Responses" in the Journal of Statistical Software, Volume 114, Issue 9 (see <doi:10.18637/jss.v114.i09>).

Given a group of genomes and their relationship with each other, the package clusters the genomes and selects the most representative members of each cluster. Additional data can be provided to the prioritize certain genomes. The results can be printed out as a list or a new phylogeny with graphs of the trees and distance distributions also available. For detailed introduction see: Thomas H Clarke, Lauren M Brinkac, Granger Sutton, and Derrick E Fouts (2018), GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models, Bioinformatics, bty300, <doi:10.1093/bioinformatics/bty300>.

This package creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

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.

Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The glmnet fitting algorithm is used to fit the continuation ratio model after data restructuring.

We implement and extend the Dividing Local Gaussian Process algorithm by Lederer et al. (2020) <doi:10.48550/arXiv.2006.09446>. Its main use case is in online learning where it is used to train a network of local GPs (referred to as tree) by cleverly partitioning the input space. In contrast to a single GP, GPTreeO is able to deal with larger amounts of data. The package includes methods to create the tree and set its parameter, incorporating data points from a data stream as well as making joint predictions based on all relevant local GPs.

This package provides functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others. Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.

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.

Add trendline and confidence interval of linear or nonlinear regression model and show equation to ggplot as simple as possible. For a general overview of the methods used in this package, see Ritz and Streibig (2008) <doi:10.1007/978-0-387-09616-2> and Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.

This package provides the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).

Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.

Computes experimental designs for two-arm experiments with covariates using multiple methods, including: (0) complete randomization and randomization with forced-balance; (1) greedy optimization of a balance objective function via pairwise switching; (2) numerical optimization via gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5) exhaustive enumeration for small sample sizes; (6) binary pair matching using nbpMatching'; (7) binary pair matching plus method (1) to further optimize balance; (8) binary pair matching plus method (3) to further optimize balance; (9) Hadamard designs; and (10) simultaneous multiple kernels. For the greedy, rerandomization, and related methods, three objective functions are supported: Mahalanobis distance, standardized sums of absolute differences, and kernel distances via the kernlab library. This package is the result of a stream of research that can be found in Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random Designs with Greatly Improved Balance." Biometrika 106(3), 695-701 <doi:10.1093/biomet/asz026>. Krieger, A. M., Azriel, D. A., and Kapelner, A. (2023). "Better experimental design by hybridizing binary matching with imbalance optimization." Canadian Journal of Statistics, 51(1), 275-292 <doi:10.1002/cjs.11685>.

Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.

Interface between the GMT map-making software and R, enabling the user to manipulate geographic data within R and call GMT commands to draw and annotate maps in postscript format. The gmt package is about interactive data analysis, rapidly visualizing subsets and summaries of geographic data, while performing statistical analysis in the R console.

Process in-situ Gamma-Ray Spectrometry for Luminescence Dating. This package allows to import, inspect and correct the energy shifts of gamma-ray spectra. It provides methods for estimating the gamma dose rate by the use of a calibration curve as described in Mercier and Falguères (2007). The package only supports Canberra CNF and TKA and Kromek SPE files.

Create graticule lines and labels for maps. Control the creation of lines or tiles by setting their placement (at particular meridians and parallels) and extent (along parallels and meridians). Labels are created independently of lines.

Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.

The goal of gnonadd is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.

This package provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive RStudio plot for better guessing dynamically of initial values for ease of included optimization and simulating.

The goal of GHCNr is to provide a fast and friendly interface with the Global Historical Climatology Network daily (GHCNd) database, which contains daily summaries of weather station data worldwide (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>). GHCNd is accessed through the web API <https://www.ncei.noaa.gov/access/services/data/v1>. GHCNr main functionalities consist of downloading data from GHCNd, filter it, and to aggregate it at monthly and annual scales.

This package provides a fast and flexible general-purpose implementation of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving global minimization problems is provided. It is designed to handle complex optimization tasks with nonlinear, non-differentiable, and multi-modal objective functions defined by users. There are five types of PSO variants: Particle Swarm Optimization (PSO, Eberhart & Kennedy, 1995) <doi:10.1109/MHS.1995.494215>, Quantum-behaved particle Swarm Optimization (QPSO, Sun et al., 2004) <doi:10.1109/CEC.2004.1330875>, Locally convergent rotationally invariant particle swarm optimization (LcRiPSO, Bonyadi & Michalewicz, 2014) <doi:10.1007/s11721-014-0095-1>, Competitive Swarm Optimizer (CSO, Cheng & Jin, 2015) <doi:10.1109/TCYB.2014.2322602> and Double exponential particle swarm optimization (DExPSO, Stehlik et al., 2024) <doi:10.1016/j.asoc.2024.111913>. For the DE algorithm, six types in Storn, R. & Price, K. (1997) <doi:10.1023/A:1008202821328> are included: DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, DE/rand_to-best/1 and DE/rand_to-best/2.

This package provides methods for calculating gradient surface metrics for continuous analysis of landscape features.

Run grass growth simulations using a grass growth model based on ModVege (Jouven, M., P. Carrère, and R. Baumont "Model Predicting Dynamics of Biomass, Structure and Digestibility of Herbage in Managed Permanent Pastures. 1. Model Description." (2006) <doi:10.1111/j.1365-2494.2006.00515.x>). The implementation in this package contains a few additions to the above cited version of ModVege, such as simulations of management decisions, and influences of snow cover. As such, the model is fit to simulate grass growth in mountainous regions, such as the Swiss Alps. The package also contains routines for calibrating the model and helpful tools for analysing model outputs and performance.

An implementation of SPRE (standardised predicted random-effects) statistics in R to explore heterogeneity in genetic association meta- analyses, as described by Magosi et al. (2019) <doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision weighted residuals that indicate the direction and extent with which individual study-effects in a meta-analysis deviate from the average genetic effect. Overly influential positive outliers have the potential to inflate average genetic effects in a meta-analysis whilst negative outliers might lower or change the direction of effect. See the getspres website for documentation and examples <https://magosil86.github.io/getspres/>.