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bigstep  

Stepwise Selection for Large Data Sets
View on CRAN: Click here


Download and install bigstep package within the R console
Install from CRAN:
install.packages("bigstep")

Install from Github:
library("remotes")
install_github("cran/bigstep")

Install by package version:
library("remotes")
install_version("bigstep", "1.1.2")



Attach the package and use:
library("bigstep")
Maintained by
Piotr Szulc
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-10-27
Latest Update: 2025-03-10
Description:
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) .
How to cite:
Piotr Szulc (2016). bigstep: Stepwise Selection for Large Data Sets. R package version 1.1.2, https://cran.r-project.org/web/packages/bigstep. Accessed 04 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:21), 0.5.0 (2016-10-27 17:40), 0.7.1 (2016-12-31 01:44), 0.7.4 (2017-04-05 23:57), 1.0.0 (2018-09-12 21:10), 1.0.1 (2019-03-21 18:33), 1.0.2 (2019-07-18 08:35), 1.0.3 (2019-07-26 01:10), 1.1.0 (2023-03-28 15:10), 1.1.1 (2023-05-14 01:12)
Other packages that cited bigstep R package
View bigstep citation profile
Other R packages that bigstep depends, imports, suggests or enhances
Complete documentation for bigstep
Functions, R codes and Examples using the bigstep R package
Some associated functions: aic . backward . bic . bigstep . fast_forward . forward . get_model . maic . maic2 . mbic . mbic2 . multi_backward . prepare_data . reduce_matrix . reexports . stepwise . summary.big . 
Some associated R codes: backward.R . bigstep.R . criteria.R . fast_forward.R . fit_model.R . forward.R . multi_backward.R . prep_data.R . reduce.R . reexport.R . stepwise.R .  Full bigstep package functions and examples
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