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
Author: Fabio Feser ORCID iD [aut, cre], Marina Evangelou ORCID iD [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
Materials: README
CRAN checks: sgs results

Documentation:

Reference manual: sgs.pdf
Vignettes: SGS reproducible example

Downloads:

Package source: sgs_0.1.1.tar.gz
Windows binaries: r-devel: sgs_0.1.1.zip, r-release: sgs_0.1.1.zip, r-oldrel: sgs_0.1.1.zip
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
Old sources: sgs archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=sgs to link to this page.