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) 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.">

sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control (original) (raw)

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) 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: 0.3.0
Imports: Matrix, MASS, caret, grDevices, graphics, methods, stats, faux, SLOPE, Rlab, Rcpp (≥ 1.0.10)
LinkingTo: Rcpp, RcppArmadillo
Suggests: SGL, gglasso, glmnet, testthat, knitr, grpSLOPE, rmarkdown
Published: 2024-09-18
DOI: 10.32614/CRAN.package.sgs
Author: Fabio Feser ORCID iD [aut, cre]
Maintainer: Fabio Feser
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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