Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles.

Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached.

The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.

Analysis of moderation (ANOMO) method conceptualizes the difference and equivalence tests as a moderation problem to test the difference and equivalence of two estimates (e.g., two means or two effects).

This package provides tools to read, write, parse, and analyze forest fire history data (e.g. FHX). Described in Malevich et al. (2018) <doi:10.1016/j.dendro.2018.02.005>.

Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency.

Gather boxscore and play-by-play data from the Canadian Elite Basketball League (CEBL) <https://www.cebl.ca> to create a repository of basic and advanced statistics for teams and players.

This package provides a wrapper for the CDRC API that returns data frames or sf of CDRC data. The API web reference is:<https://api.cdrc.ac.uk/swagger/index.html>.

Computes effect sizes, standard errors, and confidence intervals for total, direct, and indirect effects in continuous-time mediation models as described in Pesigan, Russell, and Chow (2025) <doi:10.1037/met0000779>.

This package implements double hierarchical generalized linear models in which the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.

This package provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.

Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).

This package contains functions for fitting hierarchical versions of EVSD, UVSD, DPSD, DPSD with d restricted to be positive, and our gamma signal detection model to recognition memory confidence-ratings data.

This package provides functionality to download and cache files from Hugging Face Hub <https://huggingface.co/models>. Uses the same caching structure so files can be shared between different client libraries.

H3 is a hexagonal hierarchical spatial index developed by Uber <https://h3geo.org/>. This package exposes the source code of H3 (written in C') to routines that are callable through R'.

This package provides tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.

Density, distribution function, quantile function, and random generation function based on Salem, H. M. (2019)<doi:10.5539/mas.v13n2p54>. In addition, a numerical method for maximum likelihood estimation is provided.

This package provides functions and classes to store, manipulate and summarise Monte Carlo Markov Chain (MCMC) samples. For more information see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.

This package provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.

Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.

This package provides a flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.

Exploration and analysis of compositional data in the framework of Aitchison (1986, ISBN: 978-94-010-8324-9). This package provides tools for chemical fingerprinting and source tracking of ancient materials.

Looks for amino acid and/or nucleotide patterns and/or small ligands coordinated to a given prosthetic centre. Files have to be in the local file system and contain proper extension.

Win ratio approach to partially ordered data, such as multivariate ordinal responses under product (consensus) or prioritized order. Two-sample tests and multiplicative regression models are implemented (Mao, 2024, under revision).