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Standard regression functions in R enabled for parallel processing over large data-frames

Bioconductor version: Release (3.19)

In many analyses, a large amount of variables have to be tested independently against the trait/endpoint of interest, and also adjusted for covariates and confounding factors at the same time. The major bottleneck in these is the amount of time that it takes to complete these analyses. With RegParallel, a large number of tests can be performed simultaneously. On a 12-core system, 144 variables can be tested simultaneously, with 1000s of variables processed in a matter of seconds via 'nested' parallel processing. Works for logistic regression, linear regression, conditional logistic regression, Cox proportional hazards and survival models, and Bayesian logistic regression. Also caters for generalised linear models that utilise survey weights created by the 'survey' CRAN package and that utilise 'survey::svyglm'.

Author: Kevin Blighe [aut, cre], Sarega Gurudas [ctb], Jessica Lasky-Su [aut]

Maintainer: Kevin Blighe <kevin at>

Citation (from within R, enter citation("RegParallel")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Standard regression functions in R enabled for parallel processing over large data-frames HTML R Script
Reference Manual PDF


biocViews DiseaseModel, ExperimentData
Version 1.22.0
License GPL-3
Depends doParallel, foreach, parallel, iterators, data.table, stringr, survival, arm, stats, utils, methods
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Suggests RUnit, BiocGenerics, knitr, DESeq2, airway, magrittr, Biobase, GEOquery, biomaRt, survminer, survey, rmarkdown
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Follow Installation instructions to use this package in your R session.

Source Package RegParallel_1.22.0.tar.gz
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