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

Implementation of Sparse-group SLOPE: Adaptive bi-level with FDR-control (Feser et al. (2023) <doi:10.48550/arXiv.2305.09467>). 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.

Version: 0.1.1
Imports: Matrix, MASS, caret, grDevices, graphics, methods, stats, faux, SLOPE, Rlab, Rcpp (≥ 1.0.10)
LinkingTo: Rcpp, RcppArmadillo
Suggests: SGL, gglasso, glmnet, testthat, knitr, rmarkdown
Published: 2023-08-22
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
Materials: README
CRAN checks: sgs results


Reference manual: sgs.pdf
Vignettes: SGS reproducible example


Package source: sgs_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sgs_0.1.1.tgz, r-oldrel (arm64): sgs_0.1.1.tgz, r-release (x86_64): sgs_0.1.1.tgz, r-oldrel (x86_64): sgs_0.1.1.tgz
Old sources: sgs archive


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