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Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation.
| Version: | 1.1.1 |
| Imports: | Rcpp (≥ 1.0.9), srlars, robStepSplitReg, cellWise, robustbase |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat, mvnfast |
| Published: | 2023-09-14 |
| Author: | Anthony Christidis [aut, cre], Gabriela Cohen-Freue [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: | RMSS results |
| Reference manual: | RMSS.pdf |
| Package source: | RMSS_1.1.1.tar.gz |
| Windows binaries: | r-devel: RMSS_1.1.1.zip, r-release: RMSS_1.1.1.zip, r-oldrel: RMSS_1.1.1.zip |
| macOS binaries: | r-release (arm64): RMSS_1.1.1.tgz, r-oldrel (arm64): RMSS_1.1.1.tgz, r-release (x86_64): RMSS_1.1.1.tgz, r-oldrel (x86_64): RMSS_1.1.1.tgz |
| Old sources: | RMSS archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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