| parglms-package {parglms} | R Documentation |
support for parallelized estimation of GLMs/GEEs, catering for dispersed data
The DESCRIPTION file:
| Package: | parglms |
| Title: | support for parallelized estimation of GLMs/GEEs |
| Version: | 1.12.0 |
| Author: | VJ Carey <stvjc@channing.harvard.edu> |
| Description: | support for parallelized estimation of GLMs/GEEs, catering for dispersed data |
| Suggests: | RUnit, sandwich, MASS |
| 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 |
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.
VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
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.
showMethods("parGLM")