Geometry shapes in R are typically represented by matrices (points, lines), with more complex shapes being lists of matrices (polygons). Geometries will convert various R objects into these shapes. Conversion functions are available at both the R level, and through Rcpp.

mlr3tuning implements methods for hyperparameter tuning, e.g. Grid Search, Random Search, or Simulated Annealing. Various termination criteria can be set and combined. The class AutoTuner provides a convenient way to perform nested resampling in combination with mlr3.

This package provides tools to infer the code style (which style rules are followed and which ones are not) from one package and use it to check another. This makes it easier to find and correct the most important problems first.

This package provides a wrapper around the C++ library polylabel from Mapbox, providing an efficient routine for finding the approximate pole of inaccessibility of a polygon, which usually serves as an excellent candidate for labeling of a polygon.

The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.

Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.

This package provides functions to extract low-correlation variable subsets using exact graph-theoretic algorithms (e.g., Eppsteinâ Löfflerâ Strash, Bronâ Kerbosch) as well as greedy and spectral heuristics. Supports both numeric and mixed-type data using generalized association measures.

This package provides a collection of functions that make it easier to understand crime (or other) data, and assist others in understanding it. The package helps you read data from various sources, clean it, fix column names, and graph the data.

This package produces SPSS- and SAS-like output for linear discriminant function analysis and canonical correlation analysis. The methods are described in Manly & Alberto (2017, ISBN:9781498728966), Rencher (2002, ISBN:0-471-41889-7), and Tabachnik & Fidell (2019, ISBN:9780134790541).

Description of statistical associations between variables : measures of local and global association between variables (phi, Cramér V, correlations, eta-squared, Goodman and Kruskal tau, permutation tests, etc.), multiple graphical representations of the associations between variables (using ggplot2') and weighted statistics.

Generalised model for population dynamics of invasive Aedes mosquitoes. Rationale and model structure are described here: Da Re et al. (2021) <doi:10.1016/j.ecoinf.2020.101180> and Da Re et al. (2022) <doi:10.1101/2021.12.21.473628>.

This package provides a convenient API interface to access immunological data within the CAVD DataSpace'(<https://dataspace.cavd.org>), a data sharing and discovery tool that facilitates exploration of HIV immunological data from pre-clinical and clinical HIV vaccine studies.

This package performs Bayesian wavelet analysis using individual non-local priors as described in Sanyal & Ferreira (2017) <DOI:10.1007/s13571-016-0129-3> and non-local prior mixtures as described in Sanyal (2025) <DOI:10.48550/arXiv.2501.18134>.

Shrinkage estimator for polygenic risk prediction (PRS) models based on summary statistics of genome-wide association (GWA) studies. Based upon the methods and original PANPRS package as found in: Chen, Chatterjee, Landi, and Shi (2020) <doi:10.1080/01621459.2020.1764849>.

This package provides a formula sub is a subformula of formula if all the terms on the right hand side of sub are terms of formula and their left hand sides are identical. This package aids in the creation of subformulas.

This package provides functions for creating and manipulating 12-tone (i.e., dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism technique. This package can generate random 12-tone matrices and can generate matrices using a pre-determined sequence of notes.

Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication.

This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format.

ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes.

FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting.

Example data for the GPA package, consisting of the p-values of 1,219,805 SNPs for five psychiatric disorder GWAS from the psychiatric GWAS consortium (PGC), with the annotation data using genes preferentially expressed in the central nervous system (CNS).

`orthosData` is the companion ExperimentData package to the `orthos` R package for mechanistic studies using differential gene expression experiments. It provides functions for retrieval from ExperimentHub and local caching of the models and datasets used internally in orthos.

This package contains the shared libraries and Python modules of Ren'py. While functional, they are not meaningful on their own without the launcher and common Ren'py code provided by the renpy package and are only used to bootstrap it.

IPC::Run allows you run and interact with child processes using files, pipes, and pseudo-ttys. Both system()-style and scripted usages are supported and may be mixed. Likewise, functional and OO API styles are both supported and may be mixed.