sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control
Implementation of Sparse-group SLOPE: Adaptive bi-level with FDR-control (Feser et al. (2023) <arXiv:2305.09467>). 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.
Version: |
0.1.0 |
Imports: |
Matrix, MASS, caret, grDevices, graphics, methods, stats, faux, SLOPE, Rlab, Rcpp (≥ 1.0.10) |
LinkingTo: |
Rcpp, RcppArmadillo |
Suggests: |
SGL, gglasso, glmnet, seagull, testthat, knitr, rmarkdown |
Published: |
2023-05-19 |
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 |
Materials: |
README |
CRAN checks: |
sgs results |
Documentation:
Downloads:
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