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) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and group-based OSCAR models (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.

Version: 0.3.1
Imports: Matrix, MASS, caret, grDevices, graphics, methods, stats, SLOPE, Rlab, Rcpp (≥ 1.0.10)
LinkingTo: Rcpp, RcppArmadillo
Suggests: SGL, gglasso, glmnet, testthat, knitr, grpSLOPE, rmarkdown
Published: 2024-11-16
DOI: 10.32614/CRAN.package.sgs
Author: Fabio Feser ORCID iD [aut, cre]
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 (source, R code)

Downloads:

Package source: sgs_0.3.1.tar.gz
Windows binaries: r-devel: sgs_0.3.1.zip, r-release: sgs_0.3.1.zip, r-oldrel: sgs_0.3.1.zip
macOS binaries: r-release (arm64): sgs_0.3.1.tgz, r-oldrel (arm64): sgs_0.3.1.tgz, r-release (x86_64): sgs_0.3.1.tgz, r-oldrel (x86_64): sgs_0.3.1.tgz
Old sources: sgs archive

Reverse dependencies:

Reverse imports: dfr

Linking:

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