parglms-package {parglms}R Documentation

support for parallelized estimation of GLMs/GEEs

Description

This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data.

Details

The DESCRIPTION file:

Package: parglms
Title: support for parallelized estimation of GLMs/GEEs
Version: 1.26.0
Author: VJ Carey <stvjc@channing.harvard.edu>
Description: This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data.
Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown
VignetteBuilder: knitr
Depends: methods
Imports: BiocGenerics, BatchJobs, foreach, doParallel
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
License: Artistic-2.0
LazyLoad: yes
BiocViews: statistics, genetics
ByteCompile: TRUE
git_url: https://git.bioconductor.org/packages/parglms
git_branch: RELEASE_3_14
git_last_commit: d69d65f
git_last_commit_date: 2021-10-26
Date/Publication: 2021-10-26

Index of help topics:

parGLM-methods          fit GLM-like models with parallelized
                        contributions to sufficient statistics
parglms-package         support for parallelized estimation of
                        GLMs/GEEs

In version 0.0.0 we established an approach to fitting GLM from data that have been persistently dispersed and managed by a Registry.

Author(s)

VJ Carey <stvjc@channing.harvard.edu>

Maintainer: VJ Carey <stvjc@channing.harvard.edu>

References

This package shares an objective with the bigglm methods of biglm. In bigglm, a small-RAM-footprint algorithm is employed, with sequential chunking to update statistics in each iteration. In parGLM the footprint is likewise controllable, but statistics in each iteration are evaluated in parallel over chunks.

Examples

showMethods("parGLM")

[Package parglms version 1.26.0 Index]