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Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.
| Version: | 0.1 |
| Imports: | MASS, cvTools, changepoint |
| Published: | 2022-05-25 |
| DOI: | 10.32614/CRAN.package.OPTS |
| Author: | Mihai Giurcanu [aut, cre], Marinela Capanu [aut, ctb], Colin Begg [aut], Mithat Gonen [aut] |
| Maintainer: | Mihai Giurcanu <giurcanu at uchicago.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | OPTS results |
| Reference manual: | OPTS.pdf |
| Package source: | OPTS_0.1.tar.gz |
| Windows binaries: | r-devel: OPTS_0.1.zip, r-release: OPTS_0.1.zip, r-oldrel: OPTS_0.1.zip |
| macOS binaries: | r-release (arm64): OPTS_0.1.tgz, r-oldrel (arm64): OPTS_0.1.tgz, r-release (x86_64): OPTS_0.1.tgz, r-oldrel (x86_64): OPTS_0.1.tgz |
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