Package: RMSS 1.1.2

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_1.1.2.tar.gz
RMSS_1.1.2.tar.gz(r-4.5-noble)RMSS_1.1.2.tar.gz(r-4.4-noble)
RMSS_1.1.2.tgz(r-4.4-emscripten)RMSS_1.1.2.tgz(r-4.3-emscripten)
RMSS.pdf |RMSS.html
RMSS/json (API)
NEWS

# Install 'RMSS' in R:
install.packages('RMSS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

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

openblascppopenmp

1.70 score 4 scripts 149 downloads 3 exports 52 dependencies

Last updated 11 days agofrom:c2cf6da14e. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 20 2024
R-4.5-linux-x86_64OKDec 20 2024

Exports:cv.RMSSRMSStrimmed_samples

Dependencies:cellWiseclicodetoolscolorspaceDEoptimRfansifarverforeachggplot2glmnetgluegridExtragtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnlmepcaPPpillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2rlangrobStepSplitRegrobustbaserrcovscalesshapesrlarsstringistringrsurvivalsvdtibbleutf8vctrsviridisLitewithr