The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST) person parameters for the 1,2,3,4-PL model and the GPCM (generalized partial credit model). The parameters are estimated under the assumption that the item parameters are known and fixed. The package is useful e.g. in the case that items from an item pool / item bank with known item parameters are administered to a new population of test-takers and an ability estimation for every test-taker is needed.

Free UK geocoding using data from Office for National Statistics. It is using several functions to get information about post codes, outward codes, reverse geocoding, nearest post codes/outward codes, validation, or randomly generate a post code. API wrapper around <https://postcodes.io>.

Fits the Poisson-Tweedie generalized linear mixed model described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>). Likelihood approximation based on adaptive Gauss Hermite quadrature rule.

This package provides functions to compute the potential model as defined by Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available to customize the model, such as the possibility to fine-tune the distance friction functions or to use custom distance matrices. Some computations are parallelized to improve their efficiency.

Explore various dependencies of a packages (on the Comprehensive R Archive Network Like repositories). The functions get_neighborhood() and get_dependencies() provide dependencies of packages and as_graph() can be used to convert into a igraph object for further analysis and plotting.

This package provides tools for Bayesian estimation of meta-analysis models that account for publications bias or p-hacking. For publication bias, this package implements a variant of the p-value based selection model of Hedges (1992) <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also implements the mixture of truncated normals model for p-hacking described in Moss and De Bin (2019) <arXiv:1911.12445>.

Bayesian regularized quantile regression utilizing two major classes of shrinkage priors (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian shrinkage estimation, variable selection and valid statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) <doi:10.1002/sta4.70078>). Besides, robust sparse Bayesian regression with the horseshoe family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented and yielded valid inference results under heavy-tailed model errors (Fan et al.(2026) <doi:10.1016/j.csda.2026.108358>). The Markov chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++.

This package implements the Principal Components Difference-in-Differences estimators as described in Chan, M. K., & Kwok, S. S. (2022) <doi:10.1080/07350015.2021.1914636>.

Spectral emission data for some frequently used light emitting diodes available as electronic components. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

An interface to compute poverty and inequality indicators for more than 160 countries and regions from the World Bank's database of household surveys, through the Poverty and Inequality Portal (PIP).

This package provides a toolkit of functions to help: i) effortlessly transform collected data into a publication ready format, ii) generate insightful visualizations from clinical data, iii) report summary statistics in a publication-ready format, iv) efficiently export, save and reload R objects within the framework of R projects.

This package provides a unified and user-friendly framework for applying the principal sufficient dimension reduction methods for both linear and nonlinear cases. The package has an extendable power by varying loss functions for the support vector machine, even for an user-defined arbitrary function, unless those are convex and differentiable everywhere over the support (Li et al. (2011) <doi:10.1214/11-AOS932>). Also, it provides a real-time sufficient dimension reduction update procedure using the principal least squares support vector machine (Artemiou et al. (2021) <doi:10.1016/j.patcog.2020.107768>).

To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classification of Diseases (ICD). The same PheCode hierarchy is displayed in two ways: as a sunburst plot and as a tree.

Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.

Use Pokemon(R) inspired palettes with additional ggplot2 scales. Palettes are the colours in each Pokemon's sprite, ordered by how common they are in the image. The first 386 Pokemon are currently provided.

Access the data of the Catalogue of the Timber Forest Species of the Peruvian Amazon Vásquez Martà nez, R., & Rojas Gonzáles, R.D.P.(2022)<doi:10.21704/rfp.v37i3.1956>.

This package provides essential checklists for R package developers, whether you're creating your first package or beginning a new project. This tool guides you through each step of the development process, including specific considerations for submitting your package to the Comprehensive R Archive Network (CRAN). Simplify your workflow and ensure adherence to best practices with packagepal'.

This package provides a Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) <doi:10.1186/1478-811X-11-46>, stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) <doi:10.1371/journal.pcbi.1010536>. This package includes source code derived from the BoolNet package, which is licensed under the Artistic License 2.0.

This package provides a framework for creating interactive figures for data exploration. All plots are automatically linked and support several kinds of interactive features, including selection, zooming, panning, and parameter manipulation. The figures can be interacted with either manually, using a mouse and a keyboard, or by running code from inside an active R session.

This package provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true sequence of binary splits).

This package provides an interactive RStudio gadget for working with an AI assistant during package and script development. The gadget can use selected editor text, the active source file, package metadata, and uploaded files as context for code explanation, code generation, documentation, and review workflows. It offers model presets, assistant behavior settings, responsive code-focused output, and explicit copy, insert, and replace actions for the active source editor. API interactions via the httr package are performed asynchronously using promises and future to avoid blocking the R console. The backend is configured via the OPENAI_API_KEY environment variable.

Cluster analysis via nonparametric density estimation is performed. Operationally, the kernel method is used throughout to estimate the density. Diagnostics methods for evaluating the quality of the clustering are available. The package includes also a routine to estimate the probability density function obtained by the kernel method, given a set of data with arbitrary dimensions.

This package provides a comprehensive collection of tools for creating, manipulating and visualising pedigrees and genetic marker data. Pedigrees can be read from text files or created on the fly with built-in functions. A range of utilities enable modifications like adding or removing individuals, breaking loops, and merging pedigrees. An online tool for creating pedigrees interactively, based on pedtools', is available at <https://magnusdv.shinyapps.io/quickped>. pedtools is the hub of the pedsuite', a collection of packages for pedigree analysis. A detailed presentation of the pedsuite is given in the book Pedigree Analysis in R (Vigeland, 2021, ISBN:9780128244302).

Bayesian hierarchical methods for pathway analysis of genomewide association data: Normal/Bayes factors and Sparse Normal/Adaptive lasso. The Frequentist Fisher's product method is included as well.