Package: bigstep 1.1.2

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>.

Authors:Piotr Szulc [aut, cre]

bigstep_1.1.2.tar.gz
bigstep_1.1.2.zip(r-4.7-any)bigstep_1.1.2.zip(r-4.6-any)bigstep_1.1.2.zip(r-4.5-any)
bigstep_1.1.2.tgz(r-4.6-any)bigstep_1.1.2.tgz(r-4.5-any)
bigstep_1.1.2.tar.gz(r-4.7-any)bigstep_1.1.2.tar.gz(r-4.6-any)
bigstep_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bigstep/json (API)

# Install 'bigstep' in R:

install.packages('bigstep', repos = c('https://pmszulc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pmszulc/bigstep/issues

On CRAN:

5.27 score 2 stars 1 packages 62 scripts 370 downloads 17 exports 17 dependencies

Last updated from:37ab6d4f9c. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	180
source / vignettes	OK	209
linux-release	OK	209
macos-release	OK	138
macos-oldrel	OK	187
windows-devel	OK	111
windows-release	OK	106
windows-oldrel	OK	117
wasm-release	OK	139

Exports:%>%aic attach.big.matrix backward bic fast_forward forward get_model maic maic2 mbic mbic2 multi_backward prepare_data read.big.matrix reduce_matrix stepwise

Dependencies:BH biglm bigmemory bigmemory.sri DBI lattice magrittr MASS Matrix matrixStats R.methodsS3 R.oo R.utils Rcpp RcppEigen speedglm uuid

The stepwise procedure for big data

Small data | Bigger data | Big data | Own criteria | General linear models

Rendered frombigstep.Rmdusingknitr::rmarkdown

Last update: 2023-05-11
Started: 2018-09-12

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
AIC	aic
Backward step	backward
BIC	bic
Model selection	bigstep
Fast-forward step	fast_forward
Forward step	forward
Get the model	get_model
mAIC	maic
mAIC2	maic2
mBIC	mbic
mBIC2	mbic2
Multi-backward step	multi_backward
Data preparation	prepare_data
Reducing number of variables	reduce_matrix
Stepwise	stepwise
Summarizing model fit	summary.big