Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc.

The variable importance is calculated using knock off variables. Then output can be provided in numerical and graphical form. Meredith L Wallace (2023) <doi:10.1186/s12874-023-01965-x>.

This package provides a collection of methods for the robust analysis of univariate and multivariate functional data, possibly in high-dimensional cases, and hence with attention to computational efficiency and simplicity of use. See the R Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an in-depth presentation of the roahd package. See Aleman-Gomez et al. (2021) <arXiv:2103.08874> for details about the concept of depthgram.

This package provides tools for qPCR data analysis using Delta Ct and Delta Delta Ct methods, including t-test, Wilcoxon-test, ANOVA models, and publication-ready visualizations. The package supports multiple target, and multiple reference genes, and uses a calculation framework adopted from Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods.

This package provides a programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/developer/summary>). GBIF is a database of species occurrence records from sources all over the globe. rgbif includes functions for searching for taxonomic names, retrieving information on data providers, getting species occurrence records, getting counts of occurrence records, and using the GBIF tile map service to make rasters summarizing huge amounts of data.

This package provides a parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package. The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in RMSD package. See Wada and Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> for the detail of the algorithm.

This package provides an implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available.

This package provides functionality to define and train neural networks similar to PyTorch but written entirely in R using the libtorch library. It also supports low-level tensor operations and GPU acceleration.

This package provides a unified R graphics backend. Render R graphics fast and easy to many common file formats. It provides a thread safe C interface for asynchronous rendering of R graphics.

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.

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>.

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.

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'.