Package: sgs
Title: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control
Version: 0.3.8
Date: 2025-06-12
Authors@R: person("Fabio", "Feser", role = c("aut", "cre"), email = "ff120@ic.ac.uk",comment = c(ORCID = "0009-0007-3088-9727"))
Maintainer: Fabio Feser <ff120@ic.ac.uk>
Description: 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.
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
RoxygenNote: 7.3.1
License: GPL (>= 3)
Encoding: UTF-8
URL: https://github.com/ff1201/sgs
BugReports: https://github.com/ff1201/sgs/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2025-06-12 15:39:44 UTC; ff120
Author: Fabio Feser [aut, cre] (ORCID: <https://orcid.org/0009-0007-3088-9727>)
Repository: CRAN
Date/Publication: 2025-06-12 16:20:02 UTC
Built: R 4.3.3; x86_64-apple-darwin20; 2025-06-19 06:33:27 UTC; unix
Archs: sgs.so.dSYM
