Package: RMSS 1.2.4

Anthony Christidis

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.

Authors:Anthony Christidis [aut, cre], Gabriela Cohen-Freue [aut]

RMSS_1.2.4.tar.gz
RMSS_1.2.4.tar.gz(r-4.7-arm64)RMSS_1.2.4.tar.gz(r-4.7-x86_64)RMSS_1.2.4.tar.gz(r-4.6-arm64)RMSS_1.2.4.tar.gz(r-4.6-x86_64)
RMSS_1.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
RMSS/json (API)
NEWS

# Install 'RMSS' in R:
install.packages('RMSS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

2.40 score 4 scripts 157 downloads 3 exports 46 dependencies

Last updated from:86f78319d9. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK185
linux-devel-x86_64OK154
source / vignettesOK237
linux-release-arm64OK172
linux-release-x86_64OK184
wasm-releaseOK232

Exports:cv.RMSSRMSStrimmed_samples

Dependencies:BHcellWiseclicodetoolscpp11DEoptimRfarverforeachggplot2glmnetgluegridExtragtableisobanditeratorslabelinglatticelifecyclemagrittrMatrixmatrixStatsmvnfastmvtnormpcaPPplyrR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2rlangrobStepSplitRegrobustbaserrcovS7scalesshapesrlarsstringistringrsurvivalsvdvctrsviridisLitewithr