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.
| Version: | 1.0.3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | bigmemory, magrittr, matrixStats, R.utils, RcppEigen, speedglm, stats, utils |
| Suggests: | devtools, knitr, rmarkdown, testthat |
| Published: | 2019-07-25 |
| Author: | Piotr Szulc [aut, cre] |
| Maintainer: | Piotr Szulc <piotr.michal.szulc at gmail.com> |
| BugReports: | http://github.com/pmszulc/bigstep/issues |
| License: | GPL-3 |
| URL: | http://github.com/pmszulc/bigstep |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | bigstep results |
| Reference manual: | bigstep.pdf |
| Vignettes: |
The stepwise procedure for big data |
| Package source: | bigstep_1.0.3.tar.gz |
| Windows binaries: | r-devel: bigstep_1.0.3.zip, r-release: bigstep_1.0.3.zip, r-oldrel: bigstep_1.0.3.zip |
| macOS binaries: | r-release: bigstep_1.0.3.tgz, r-oldrel: bigstep_1.0.3.tgz |
| Old sources: | bigstep archive |
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