sgs-package: sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR...

In sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control

sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control

Description

Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) \Sexpr[results=rd]{tools:::Rd_expr_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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.1804.02339")}) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2017.1411269")}) and group-based OSCAR models (Feser and Evangelou (2024) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2405.15357")}) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2405.15357")}) for computational speed-up.

Author(s)

Maintainer: Fabio Feser [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/ff1201/sgs

• Report bugs at https://github.com/ff1201/sgs/issues

sgs documentation built on June 12, 2025, 5:09 p.m.