This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was HG-U219\_probe\_tab.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was HG-U95C\_probe\_tab.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was HG-U95E\_probe\_tab.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was HG-U95D\_probe\_tab.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was HG-U95B\_probe\_tab.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was MG-U74B\_probe\_tab.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was MG-U74A\_probe\_tab.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was MG-U74C\_probe\_tab.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was P\_aeg1a\_probe\_tab.

This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Yeast\_2\_probe\_tab.

Validates common string formats including financial identifiers (ISIN, CUSIP, SEDOL, FIGI, IBAN, LEI), publication identifiers (ISBN, ISSN, DOI, ORCID), and general formats (email, UUID, URL, semver), with check digit verification where applicable.

Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.

Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) <doi:10.1093/oxfordjournals.aob.a083391>.

This package provides functionality for computing support intervals for univariate parameters based on confidence intervals or parameter estimates with standard errors (Pawel et al., 2022) <doi:10.48550/arXiv.2206.12290>.

This package provides four variants of three-way correspondence analysis (ca): three-way symmetrical ca, three-way non-symmetrical ca, three-way ordered symmetrical ca and three-way ordered non-symmetrical ca.

With bivariate data, it is possible to calculate 2-dimensional kernel density estimates that return polygons at given levels of probability. densityarea returns these polygons for analysis, including for calculating their area.

This package contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at <https://osf.io/b78je>.

Fuzzy string matching implementation of the fuzzywuzzy <https://github.com/seatgeek/fuzzywuzzy> python package. It uses the Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.

Download geyser eruption and observation data from the GeyserTimes site (<https://geysertimes.org>) and optionally store it locally. The vignette shows a simple analysis of downloading, accessing, and summarizing the data.

This package provides methods for nonlinear Granger causality testing in reproducing kernel Hilbert space (RKHS), using kernel ridge regression for conditional mean estimation and conditional centering for construction of the test statistic.

New multi-sample tests for testing whether multiple samples are from the same distribution. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2022) <arXiv:2205.13787>.

Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications. For more technical details, see Lyrvall et al. (2025) <doi:10.1080/00273171.2025.2473935>.

Process OpenPose human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA NewsScape archive.