bigstep: Stepwise Selection for Large Data Sets

Selecting linear and generalized linear models for large data sets using modified stepwise procedure and modern selection criteria (like modifications of Bayesian Information Criterion). Selection can be performed on data which exceed RAM capacity. Bogdan et al., (2004) <doi:10.1534/genetics.103.021683>.

Version:	1.1.2
Depends:	R (≥ 3.5.0)
Imports:	bigmemory, magrittr, matrixStats, R.utils, RcppEigen, speedglm, stats, utils
Suggests:	devtools, knitr, rmarkdown, testthat
Published:	2025-03-10
DOI:	10.32614/CRAN.package.bigstep
Author:	Piotr Szulc [aut, cre]
Maintainer:	Piotr Szulc <piotr.michal.szulc at gmail.com>
BugReports:	https://github.com/pmszulc/bigstep/issues
License:	GPL-3
URL:	https://github.com/pmszulc/bigstep
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	bigstep results

Documentation:

Reference manual:	bigstep.html , bigstep.pdf
Vignettes:	The stepwise procedure for big data (source, R code)

Downloads:

Package source:	bigstep_1.1.2.tar.gz
Windows binaries:	r-devel: bigstep_1.1.2.zip, r-release: bigstep_1.1.2.zip, r-oldrel: bigstep_1.1.2.zip
macOS binaries:	r-release (arm64): bigstep_1.1.2.tgz, r-oldrel (arm64): bigstep_1.1.2.tgz, r-release (x86_64): bigstep_1.1.2.tgz, r-oldrel (x86_64): bigstep_1.1.2.tgz
Old sources:	bigstep archive

Reverse dependencies:

Reverse imports:

stabiliser

Linking:

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