Create R functions that interact with OAuth2 Google APIs <https://developers.google.com/apis-explorer/> easily, with auto-refresh and Shiny compatibility.

This package provides a simple way to translate text elements in ggplot2 plots using a dictionary-based approach.

Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around gbm (gradient boosting machine) functions in dismo (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around gbm (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 Working Guide <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows Appendix S3. See <https://www.simondedman.com/> for published guides and papers using this package.

Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).

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.

Geostatistical modelling facilities using SpatRaster and SpatVector objects are provided. Non-Gaussian models are fit using INLA', and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The RandomFields package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.

This package provides R functions to access the API of the project and repository management web application GitLab'. For many common tasks (repository file access, issue assignment and status, commenting) convenience wrappers are provided, and in addition the full API can be used by specifying request locations. GitLab is open-source software and can be self-hosted or used on <https://about.gitlab.com>.

This package provides methods for recursive partitioning based on the Graded Response Model ('GRM'), extending the MOB algorithm from the partykit package. The package allows for fitting GRM trees that partition the population into homogeneous subgroups based on item response patterns and covariates. Includes specialized plotting functions for visualizing GRM trees with different terminal node displays (threshold regions, parameter profiles, and factor score distributions). For more details on the methods, see Samejima (1969) <doi:10.1002/J.2333-8504.1968.TB00153.X>, Komboz et al. (2018) <doi:10.1177/0013164416664394> and Arimoro et al. (2025) <doi:10.1007/s11136-025-04018-6>.

Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.

Computational intensive calculations for Generalized Additive Models for Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.

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 easy way to create responsive layouts with just a few lines of code. You can create boxes that are draggable and resizable and load predefined Layouts. The package serves as a wrapper to allow for easy integration of the gridstack.js functionalities <https://github.com/gridstack/gridstack.js>.

This package provides tools to adjust estimates of learning for guessing-related bias in educational and survey research. Implements standard guessing correction methods and a sophisticated latent class model that leverages informative pre-post test transitions to account for guessing behavior. The package helps researchers obtain more accurate estimates of actual learning when respondents may guess on closed-ended knowledge items. For theoretical background and empirical validation, see Cor and Sood (2018) <https://gsood.com/research/papers/guess.pdf>.

This package provides a collection of functions for testing randomness (or mutual independence) in linear and circular data as proposed in Gehlot and Laha (2025a) <doi:10.48550/arXiv.2506.21157> and Gehlot and Laha (2025b) <doi:10.48550/arXiv.2506.23522>, respectively.

We consider the ultrahigh-dimensional and error-prone data. Our goal aims to estimate the precision matrix and identify the graphical structure of the random variables with measurement error corrected. We further adopt the estimated precision matrix to the linear discriminant function to do classification for multi-label classes.

This package provides a comprehensive suite of functions for processing and visualizing taxonomic data. It includes functionality to clean and transform taxonomic data, categorize it into hierarchical ranks (such as Phylum, Class, Order, Family, and Genus), and calculate the relative abundance of each category. The package also generates a color palette for visual representation of the taxonomic data, allowing users to easily identify and differentiate between various taxonomic groups. Additionally, it features a river plot visualization to effectively display the distribution of individuals across different taxonomic ranks, facilitating insights into taxonomic visualization.

The multiple contrast tests for univariate were proposed by Munko, Ditzhaus, Pauly, Smaga, and Zhang (2023) <doi:10.48550/arXiv.2306.15259>. Recently, they were extended to the multivariate functional data in Munko, Ditzhaus, Pauly, and Smaga (2024) <doi:10.48550/arXiv.2406.01242>. These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.

William S. Cleveland's book Visualizing Data is a classic piece of literature on Exploratory Data Analysis. Although it was written several decades ago, its content is still relevant as it proposes several tools which are useful to discover patterns and relationships among the data under study, and also to assess the goodness of fit o a model. This package provides functions to produce the ggplot2 versions of the visualization tools described in this book and is thought to be used in the context of courses on Exploratory Data Analysis.

Gitea is a community managed, lightweight code hosting solution were projects and their respective git repositories can be managed <https://gitea.io>. This package gives an interface to the Gitea API to access and manage repositories, issues and organizations directly in R.

Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (in press) <doi:10.1016/j.ecosta.2025.09.003>.

Utility functions to read, manipulate, analyse and write transit feeds in the General Transit Feed Specification (GTFS) data format.

This package provides a reproducible pipeline to conduct genomeâ wide association studies (GWAS) and extract singleâ nucleotide polymorphisms (SNPs) for a human trait or disease. Given aggregated GWAS dataset(s) and a userâ defined significance threshold, the package retrieves significant SNPs from the GWAS Catalog and the Experimental Factor Ontology (EFO), annotates their gene context, and can write a harmonised metadata table in comma-separated values (CSV) format, genomic intervals in the Browser Extensible Data (BED) format, and sequences in the FASTA (text-based sequence) format with user-defined flanking regions for clustered regularly interspaced short palindromic repeats (CRISPR) guide design. For details on the resources and methods see: Buniello et al. (2019) <doi:10.1093/nar/gky1120>; Sollis et al. (2023) <doi:10.1093/nar/gkac1010>; Jinek et al. (2012) <doi:10.1126/science.1225829>; Malone et al. (2010) <doi:10.1093/bioinformatics/btq099>; Experimental Factor Ontology (EFO) <https://www.ebi.ac.uk/efo>.

Generalized Entropy Calibration produces calibration weights using generalized entropy as the objective function for optimization. This approach, as implemented in the GECal package, is based on Kwon, Kim, and Qiu (2024) <doi:10.48550/arXiv.2404.01076>. GECal incorporates design weights into the constraints to maintain design consistency, rather than including them in the objective function itself.

Uses simple Bayesian conjugate prior update rules to calculate the win probability of each option, value remaining in the test, and percent lift over the baseline for various marketing objectives. References: Fink, Daniel (1997) "A Compendium of Conjugate Priors" <https://www.johndcook.com/CompendiumOfConjugatePriors.pdf>. Stucchio, Chris (2015) "Bayesian A/B Testing at VWO" <https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf>.