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 [aut, cre], Marina Evangelou [aut]
Maintainer:	Fabio Feser <ff120 at ic.ac.uk>
BugReports:	https://github.com/ff1201/sgs/issues
License:	GPL (≥ 3)
URL:	https://github.com/ff1201/sgs
NeedsCompilation:	yes
Citation:	sgs citation info
Materials:	README
CRAN checks:	sgs results

Documentation:

Reference manual:	sgs.pdf
Vignettes:	sgs reproducible example

Downloads:

Package source:	sgs_0.2.0.tar.gz
Windows binaries:	r-devel: sgs_0.2.0.zip, r-release: sgs_0.2.0.zip, r-oldrel: sgs_0.2.0.zip
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

Linking:

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