Functions to perform stepwise split regularized regression. The approach first uses a stepwise algorithm to split the variables into the models with a goodness of fit criterion, and then regularization is applied to each model. The weights of the models in the ensemble are determined based on a criterion selected by the user.
| Version: | 1.0.3 |
| Imports: | Rcpp (≥ 1.0.7), SplitGLM, nnls |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat, mvnfast |
| Published: | 2022-11-22 |
| DOI: | 10.32614/CRAN.package.stepSplitReg |
| Author: | Anthony Christidis [aut, cre], Stefan Van Aelst [aut], Ruben Zamar [aut] |
| Maintainer: | Anthony Christidis <anthony.christidis at stat.ubc.ca> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | stepSplitReg results [issues need fixing before 2024-12-12] |
| Reference manual: | stepSplitReg.pdf |
| Package source: | stepSplitReg_1.0.3.tar.gz |
| Windows binaries: | r-devel: stepSplitReg_1.0.3.zip, r-release: stepSplitReg_1.0.3.zip, r-oldrel: stepSplitReg_1.0.3.zip |
| macOS binaries: | r-release (arm64): stepSplitReg_1.0.3.tgz, r-oldrel (arm64): stepSplitReg_1.0.3.tgz, r-release (x86_64): stepSplitReg_1.0.3.tgz, r-oldrel (x86_64): stepSplitReg_1.0.3.tgz |
| Old sources: | stepSplitReg archive |
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