sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control

Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general adaptive three operator splitting (ATOS) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) models are also implemented. Both gSLOPE and SGS are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.

Version: 0.2.0
Imports: Matrix, MASS, caret, grDevices, graphics, methods, stats, faux, SLOPE, Rlab, Rcpp (≥ 1.0.10)
LinkingTo: Rcpp, RcppArmadillo
Suggests: SGL, gglasso, glmnet, testthat, knitr, grpSLOPE, rmarkdown
Published: 2024-07-14
DOI: 10.32614/CRAN.package.sgs
Author: Fabio Feser ORCID iD [aut, cre], Marina Evangelou ORCID iD [aut]
Maintainer: Fabio Feser <ff120 at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: sgs citation info
Materials: README
CRAN checks: sgs results


Reference manual: sgs.pdf
Vignettes: sgs reproducible example


Package source: sgs_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sgs_0.2.0.tgz, r-oldrel (arm64): sgs_0.2.0.tgz, r-release (x86_64): sgs_0.2.0.tgz, r-oldrel (x86_64): sgs_0.2.0.tgz
Old sources: sgs archive


Please use the canonical form to link to this page.