This library contains functions that calculate various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. It also implements a new methodology called DEDS (Differential Expression via Distance Summary), which selects differentially expressed genes by integrating and summarizing a set of statistics using a weighted distance approach.

The epigenomics road map describes locations of epigenetic marks in DNA from a variety of cell types. Of interest are locations of histone modifications, sites of DNA methylation, and regions of accessible chromatin. This package presents a selection of elements of the road map including metadata and outputs of the ChromImpute procedure applied to ENCODE cell lines by Ernst and Kellis.

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The NNG-powered mirai R package by Gao (2023) <doi:10.5281/zenodo.7912722> is a scheduler that efficiently processes these intense workloads. The crew package extends mirai with a unifying interface for third-party worker launchers.

Rubber is a program whose purpose is to handle all tasks related to the compilation of LaTeX documents. This includes compiling the document itself, of course, enough times so that all references are defined, and running BibTeX to manage bibliographic references. Automatic execution of dvips to produce PostScript documents is also included, as well as usage of pdfLaTeX to produce PDF documents.

Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.

This package provides the output of running Salmon on a set of 12 RNA-seq samples from King & Klose, "The pioneer factor OCT4 requires the chromatin remodeller BRG1 to support gene regulatory element function in mouse embryonic stem cells", published in eLIFE, March 2017. For details on version numbers and how the samples were processed see the package vignette.

Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients.

It performs Canonical Correlation Analysis and provides inferential guaranties on the correlation components. The p-values are computed following the resampling method developed in Winkler, A. M., Renaud, O., Smith, S. M., & Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, <doi:10.1016/j.neuroimage.2020.117065>. Furthermore, it provides plotting tools to visualize the results.

Submit and monitor batch execution of R programs across distributed computing backends including Kubernetes', SLURM', and Posit Workbench'. Provides end-user job submission functions, cluster interface functions using kubectl and SLURM commands, and a plumber API template for secure identity segregation. Supports parallel and sequential batch execution, file-based caching to skip unchanged programs, and logrx integration for execution logging.

This package provides functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.

Run computer experiments using the adaptive composite grid algorithm with a Gaussian process model. The algorithm works best when running an experiment that can evaluate thousands of points from a deterministic computer simulation. This package is an implementation of a forthcoming paper by Plumlee, Erickson, Ankenman, et al. For a preprint of the paper, contact the maintainer of this package.

Works with the Citizen Voting Age Population special tabulation from the US Census Bureau <https://www.census.gov/programs-surveys/decennial-census/about/voting-rights/cvap.html>. Provides tools to download and process raw data. Also provides a downloading interface to processed data. Implements a very basic approach to estimate block level citizen voting age population from block group data.

This package implements the exponential Factor Copula Model (eFCM) of Castro-Camilo, D. and Huser, R. (2020) for spatial extremes, with tools for dependence estimation, tail inference, and visualization. The package supports likelihood-based inference, Gaussian process modeling via Matérn covariance functions, and bootstrap uncertainty quantification. See Castro-Camilo and Huser (2020) <doi:10.1080/01621459.2019.1647842>.

When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://stats.hohoweiya.xyz/regression/2017/09/26/An-R-Package-Fit-Repeated-Linear-Regressions/>.

Fits marginal regression models for repeated or clustered responses using generalized estimating equations (GEE). Provides ordinary GEE estimators, bias-reduced and bias-corrected GEE estimators, and Jeffreys-type penalized GEE estimators for binary, count and continuous responses. Methods are described in Touloumis (2026a) <doi:10.48550/arXiv.2606.16043> and Touloumis (2026b) <doi:10.48550/arXiv.2606.16058>.

Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.

An implementation of generalized linear models (GLMs) for studying relationships among attributes in connected populations, where responses of connected units can be dependent, as introduced by Fritz et al. (2025) <doi:10.1080/01621459.2025.2565851>. iglm extends GLMs for independent responses to dependent responses and can be used for studying spillover in connected populations and other network-mediated phenomena.

This package implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.

Helpers for common data analysis tasks including missing-value summaries and filters, simple reporting and plotting utilities, Excel import and export workflows, and reading geospatial formats (for example shapefiles in zip archives, file geodatabases, KMZ, and KML) via sf and related packages. Also includes small project utilities such as creating directories, gitignore scaffolding, combined package loading, and optional lintr setup.

Share R objects across processes on the same machine via a single copy in POSIX shared memory (Linux, macOS) or a Win32 file mapping (Windows). Every process reads from the same physical pages through the R Alternative Representation ('ALTREP') framework, giving lazy, zero-copy access. Shared objects serialize compactly as their shared memory name rather than their full contents.

This package provides functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.

Nonparametric test of independence between a pair of spatial objects (random fields, point processes) based on random shifts with torus or variance correction. See MrkviÄ ka et al. (2021) <doi:10.1016/j.spasta.2020.100430>, DvoŠák et al. (2022) <doi:10.1111/insr.12503>, DvoŠák and MrkviÄ ka (2024) <doi:10.1080/10618600.2024.2357626>.

Analyse common types of plant phenotyping data, provide a simplified interface to longitudinal growth modeling and select Bayesian statistics, and streamline use of PlantCV output. Several Bayesian methods and reporting guidelines for Bayesian methods are described in Kruschke (2018) <doi:10.1177/2515245918771304>, Kruschke (2013) <doi:10.1037/a0029146>, and Kruschke (2021) <doi:10.1038/s41562-021-01177-7>.

This package implements the shared keyboard design (SKBD) for model-assisted phase I dose-finding, including decision-boundary construction, operating-characteristic simulation, and extensions for dose insertion and time-to-event settings. The package also provides an interactive Shiny interface for trial-planning workflows. For more details, see Zhao, Shi, and Xu (2026) <doi:10.48550/arXiv.2605.25043>.