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) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) models are also implemented. Both gSLOPE and SGS are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.
| Version: |
0.2.0 |
| 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: |
2024-07-14 |
| DOI: |
10.32614/CRAN.package.sgs |
| Author: |
Fabio Feser  [aut,
cre],
Marina Evangelou
[aut] |
| 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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