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.

Version:	2.0.0
Imports:	cellWise, robustbase, mvnfast
Suggests:	testthat
Published:	2026-03-02
DOI:	10.32614/CRAN.package.srlars
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:	no
Materials:	README, NEWS
CRAN checks:	srlars results

Documentation:

Reference manual:

srlars.html , srlars.pdf

Downloads:

Package source:	srlars_2.0.0.tar.gz
Windows binaries:	r-devel: srlars_1.0.1.zip, r-release: srlars_1.0.1.zip, r-oldrel: srlars_2.0.0.zip
macOS binaries:	r-release (arm64): srlars_2.0.0.tgz, r-oldrel (arm64): srlars_2.0.0.tgz, r-release (x86_64): srlars_2.0.0.tgz, r-oldrel (x86_64): srlars_2.0.0.tgz
Old sources:	srlars archive

Reverse dependencies:

Reverse imports:

RMSS

Linking:

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