Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control R package sgs version 0.3.9 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. Software 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 Fabio Feser <ff120@ic.ac.uk> Comprehensive R Archive Network (CRAN) Fabio Feser [aut, cre] (ORCID: <https://orcid.org/0009-0007-3088-9727>) GPL (>= 3) 2025-09-30 application/tgz https://CRAN.R-project.org/package=sgs doi:10.32614/CRAN.package.sgs