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: | 0.3.9 |
| 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: | 2025-09-30 |
| DOI: | 10.32614/CRAN.package.sgs |
| Author: | Fabio Feser [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 |
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