Embeds sources and headers from Tina's Random Number Generator ('TRNG') C++ library. Exposes some functionality for easier access, testing and benchmarking into R. Provides examples of how to use parallel RNG with RcppParallel'. The methods and techniques behind TRNG are illustrated in the package vignettes and examples. Full documentation is available in Bauke (2021) <https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.

This package provides estimation and data generation tools for several new regression models, including the gamma, beta, inverse gamma and beta prime distributions. These models can be parameterized based on the mean, median, mode, geometric mean and harmonic mean, as specified by the user. For details, see Bourguignon and Gallardo (2025a) <doi:10.1016/j.chemolab.2025.105382> and Bourguignon and Gallardo (2025b) <doi:10.1111/stan.70007>.

This is a package to support identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space.

This package allows building the hierarchy of domains starting from Hi-C data. Each hierarchical level is identified by a minimum value of physical insulation between neighboring domains.

This package provides tools for reading .xls and .sbj files which are written by the proprietary program z-Tree for developing and carrying out economic experiments.

This package provides functions and data accompanying the second edition of the book "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.

This package provides methods for species distribution modeling, i.e., predicting the environmental similarity of any site to that of the locations of known occurrences of a species.

This is a generic data receiver, mainly for decoding radio transmissions from devices on the 433 MHz, 868 MHz, 315 MHz, 345 MHz and 915 MHz ISM bands.

Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.

Fits and simulates multi-optima Ornstein-Uhlenbeck models to phylogenetic comparative data using Bayesian reversible-jump methods. See Uyeda and Harmon (2014) <DOI:10.1093/sysbio/syu057>.

Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) <arXiv:1807.05926>.

This package contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.

Extra strength glue for data-driven templates. String interpolation for Shiny apps or R Markdown and knitr'-powered Quarto documents, built on the glue and whisker packages.

Given the values of sampled units and selection probabilities the desraj function in the package computes the estimated value of the total as well as estimated variance.

Access and retrieve vocabulary data Finto API <https://api.finto.fi/>, which is a centralized service for interoperable thesauri, ontology and classification schemes for different subject areas.

Accompanying package of the book Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.

This package implements GINA-X, a genome-wide iterative fine-mapping method designed for non-Gaussian traits. It supports the identification of credible sets of genetic variants.

This package provides methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.

Data files and a few functions used in the book Linear Models and Regression with R: An Integrated Approach by Debasis Sengupta and Sreenivas Rao Jammalamadaka (2019).

Tests of comparison of two or more survival curves. Allows for comparison of more than two survival curves whether the proportional hazards hypothesis is verified or not.

This package provides tools that allow developers to write functions for prediction error estimation with minimal programming effort and assist users with model selection in regression problems.

Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).

Use the R console as an interactive learning environment. Users receive immediate feedback as they are guided through self-paced lessons in data science and R programming.

Stacked ensemble for regression tasks based on mlr3 framework with a pipeline for preprocessing numeric and factor features and hyper-parameter tuning using grid or random search.