RMSS: Robust Multi-Model Subset Selection

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

Documentation:

Reference manual: RMSS.pdf

Downloads:

Package source: RMSS_1.1.1.tar.gz
Windows binaries: r-prerel: RMSS_1.1.1.zip, r-release: RMSS_1.1.1.zip, r-oldrel: RMSS_1.1.1.zip
macOS binaries: r-prerel (arm64): RMSS_1.1.1.tgz, r-release (arm64): RMSS_1.1.1.tgz, r-oldrel (arm64): RMSS_1.1.1.tgz, r-prerel (x86_64): RMSS_1.1.1.tgz, r-release (x86_64): RMSS_1.1.1.tgz
Old sources: RMSS archive

Linking:

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