Discrete factor analysis for dependent Poisson and negative binomial models with truncation, zero inflation, and zero inflated truncation.

This package provides methods for fitting macroevolutionary models to phylogenetic trees Pennell (2014) <doi:10.1093/bioinformatics/btu181>.

This package provides a collection of statistical tests for genetic association studies and summary data based Mendelian randomization.

Integrated toolbox supporting common file formats used for intracranial Electroencephalography (iEEG) and deep-brain stimulation (DBS) study.

Create tables from within R directly on Google Slides presentations. Currently supports matrix, data.frame and flextable objects.

Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.

Quickly create numeric matrices for machine learning algorithms that require them. It converts factor columns into onehot vectors.

This package provides tools for (automated and manual) quality control of the results of Epigenome-Wide Association Studies.

This package provides tools for (automated and manual) quality control of the results of Genome Wide Association Studies.

This package provides a ggplot2 theme and colour palettes to create accessible data visualisations in the Scottish Government.

Proposes application of spectral analysis and jack-knife resampling for multivariate sequence forecasting using only base R functionality.

This package provides a platform for computing competition indices and experimenting with spatially explicit individual-based vegetation models.

This package provides tools for scraping information from webpages and other XML contents, using XPath or CSS selectors.

Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.

Fast flattening of hierarchical data structures (e.g. JSON, XML) into data.frames with a flexible spec language.

This package provides a Stata-style `webuse()` function for importing named datasets from Stata's online collection.

ast is a Ruby library for working with Abstract Syntax Trees. It does this through immutable data structures.

This package provides a collection of methods for quantifying representational similarity between learned features or multivariate data. The package offers an efficient C++ backend, designed for applications in machine learning, computational neuroscience, and multivariate statistics. See Klabunde et al. (2025) <doi:10.1145/3728458> for a comprehensive overview of the topic.

Prediction of claim counts using the feature based development factors introduced in the manuscript Hiabu M., Hofman E. and Pittarello G. (2023) <doi:10.48550/arXiv.2312.14549>. Implementation of Neural Networks, Extreme Gradient Boosting, and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.

Detect and remove chimeras from your amplicon sequence analysis using reference-based or de novo approaches. The rchime package implements the VSEARCH algorithms described in Rognes et al. (2016) <doi:10.7717/peerj.2584>. VSEARCH builds on the work of Edgar,R.C. et al. (2011) <doi:10.1093/bioinformatics/btr381>.

The goal of rlowdb is to provide a lightweight, file-based JSON database. Inspired by LowDB in JavaScript', it generates an intuitive interface for storing, retrieving, updating, and querying structured data without requiring a full-fledged database system. Ideal for prototyping, small-scale applications, and lightweight data management needs.

R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group.

For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) <doi:10.3102/10769986024001070>.

Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, Insect. Conserv. Divers. 5:159-168 <doi:10.1111/j.1752-4598.2011.00148.x>; Leroy et al. 2013, Divers. Distrib. 19:794-803 <doi:10.1111/ddi.12040>).