We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.

We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.

This package provides tools for regression-based Boolean rule inference in artificial intelligence studies. The package fits ridge regression models on conjunction expansions and composes interpretable rule sets. Parallel execution is supported for multi-CPU environments.

Using a CSV, LaTeX and R to easily build attractive resumes.

This package provides lentil-specific functions, processes and parameter sets used by lentil model in the Agricultural Production Systems sIMulator ('APSIM') Next Generation. Includes tools for crop process calculations, parameter management and customisation, enabling users to explore, modify and apply lentil model components.

Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for randomForest results.

An R API Client for Valve's Dota2. RDota2 can be easily used to connect to the Steam API and retrieve data for Valve's popular video game Dota2. You can find out more about Dota2 at <http://store.steampowered.com/app/570/>.

Generate stimuli and analyze data of reverse correlation image classification experiments (psychophysical tasks aimed at visualizing cognitive mental representations of faces). For the method see Dotsch and Todorov (2012) <doi:10.1177/1948550611430272>; for a practical primer see Brinkman, Todorov and Dotsch (2017) <doi:10.1080/10463283.2017.1381469>.

This package provides statistical tools for evaluating how covariates influence the strength of Pearson correlation coefficients between two response variables. Supports bivariate normal and bivariate binary responses, with likelihood-based inference and bootstrap-based significance testing. The methodology is based on Dufera, Liu and Xu (2023) "Regression models of Pearson correlation coefficient" <doi:10.1080/24754269.2023.2164970>.

Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. This package uses portfolio theories to give investor tools to evaluate their portfolio performance. For more information see, Markowitz, H.M. (1952), <doi:10.2307/2975974>. Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748].

This package provides a design-based toolkit for survey ranking questions. Estimates average ranks, marginal rank probabilities, pairwise comparisons, and ranking distributions, with optional bias correction for random responding via anchor-ranking items or user-supplied random-response rates. Includes Plackett-Luce simulation, visualization, format conversion, and diagnostic checks. Methods are described in Atsusaka and Kim (2025) <doi:10.1017/pan.2024.33>.

This package provides a collection of methods for quantifying representational similarity between learned features or multivariate data. The package offers an efficient C++ backend, designed for applications in machine learning, computational neuroscience, and multivariate statistics. See Klabunde et al. (2025) <doi:10.1145/3728458> for a comprehensive overview of the topic.

Implementation of the methods described in the paper with the above title: Langsrud, Ã . (2019) <doi:10.1007/s11222-018-9848-9>. The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included.

This package provides a programmatic interface to openfisheries.org'. This package is part of the rOpenSci suite (http://ropensci.org).

This package provides an R interface to the JuliaBUGS.jl package (<https://github.com/TuringLang/JuliaBUGS.jl>) for Bayesian inference using the BUGS modeling language. Allows R users to run models in Julia and return results as familiar R objects. Visualization and posterior analysis are supported via the bayesplot and posterior packages.

Convert digital images to spatially referenced SpatRaster objects, as defined by the terra package, using coordinates from supported spatial input classes. Supported inputs include numeric coordinate vectors and objects from the sf', terra and stars packages. The main function is an S3 generic, allowing other packages to extend support to additional spatial classes.

Combine multiple data files from a common directory. The data files will be read into R and bound together, creating a single large data.frame. A general function is provided along with a specific function for data that was collected using the open-source experiment builder OpenSesame <https://osdoc.cogsci.nl/>.

This package provides methods for calculation and visualization of the Repertoire Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) <doi:10.1186/s12859-017-1556-5>.

The goal of Rigma is to provide a user friendly client to the Figma API <https://www.figma.com/developers/api>. It uses the latest `httr2` for a stable interface with the REST API. More than 20 methods are provided to interact with Figma files, and teams. Get design data into R by reading published components and styles, converting and downloading images, getting access to the full Figma file as a hierarchical data structure, and much more. Enhance your creativity and streamline the application development by automating the extraction, transformation, and loading of design data to your applications and HTML documents.

Simplified scenario testing and sensitivity analysis, redesigned to use packages future and furrr'. Provides functions for generating function argument sets using one-factor-at-a-time (OFAT) and (sampled) permutations.

This is a library to access the current API of the web speed test service GTmetrix'. It provides a convenient wrapper to start tests, get reports, and access all kinds of meta data. For more information about using the API please visit <https://gtmetrix.com/api/docs/2.0/>.

Utilities for reading, writing, and managing RCDF files, including encryption and decryption support. It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using AES and RSA encryptions.

Implementation of the MaxRank normalization method, which enables standardization of Rank Abundance Distributions (RADs) to a specified number of ranks. Rank abundance distributions are widely used in biology and ecology to describe species abundances, and are mathematically equivalent to complementary cumulative distribution functions (CCDFs) used in physics, linguistics, sociology, and other fields. The method is described in Saeedghalati et al. (2017) <doi:10.1371/journal.pcbi.1005362>.

Queries data from RDAP servers.