Package: srlars 3.0.0

Anthony Christidis

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_3.0.0.tar.gz
srlars_3.0.0.tar.gz(r-4.7-any)srlars_3.0.0.tar.gz(r-4.6-any)
srlars_3.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
srlars/json (API)
NEWS

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

On CRAN:

Conda:

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

2.65 score 1 packages 8 scripts 482 downloads 1 exports 37 dependencies

Last updated from:3780d7b160. Checks:4 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK159
source / vignettesOK184
linux-release-x86_64OK159
wasm-releaseOK147

Exports:srlars

Dependencies:BHcellWiseclicpp11DEoptimRfarverggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrmatrixStatsmvnfastmvtnormpcaPPplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangrobustbaserrcovS7scalesshapestringistringrsvdvctrsviridisLitewithr