Package: srlars 3.0.1

srlars: Fast and Scalable Cellwise-Robust Ensemble

Functions to perform robust variable selection and regression using the Fast and Scalable Cellwise-Robust Ensemble (FSCRE) algorithm. The approach establishes a robust foundation using the Detect Deviating Cells (DDC) algorithm and robust correlation estimates. It then employs a competitive ensemble architecture where a robust Least Angle Regression (LARS) engine proposes candidate variables and cross-validation arbitrates their assignment. A final robust MM-estimator is applied to the selected predictors.

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

srlars_3.0.1.tar.gz
srlars_3.0.1.zip(r-4.7)srlars_3.0.1.zip(r-4.6)srlars_3.0.1.zip(r-4.5)
srlars_3.0.1.tgz(r-4.6-any)srlars_3.0.1.tgz(r-4.5-any)
srlars_3.0.1.tar.gz(r-4.7-any)srlars_3.0.1.tar.gz(r-4.6-any)
srlars_3.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
srlars/json (API)

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

Bug tracker:https://github.com/anthonychristidis/srlars/issues

On CRAN:

Conda:

3.48 score 1 stars 1 packages 8 scripts 494 downloads 1 exports 37 dependencies

Last updated from:aeac518675. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK150
source / vignettesOK238
linux-release-x86_64OK148
macos-release-arm64OK114
macos-oldrel-arm64OK79
windows-develOK110
windows-releaseOK95
windows-oldrelOK104
wasm-releaseOK119

Exports:srlars

Dependencies:BHcellWiseclicpp11DEoptimRfarverggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrmatrixStatsmvnfastmvtnormpcaPPplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangrobustbaserrcovS7scalesshapestringistringrsvdvctrsviridisLitewithr