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 [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 |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=sgsto link to this page.