dfr: Dual Feature Reduction for SGL
Implementation of the Dual Feature Reduction (DFR) approach for the Sparse Group Lasso (SGL) and the Adaptive Sparse Group Lasso (aSGL) (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.17094>). The DFR approach is a feature reduction approach that applies strong screening to reduce the feature space before optimisation, leading to speed-up improvements for fitting SGL (Simon et al. (2013) <doi:10.1080/10618600.2012.681250>) and aSGL (Mendez-Civieta et al. (2020) <doi:10.1007/s11634-020-00413-8> and Poignard (2020) <doi:10.1007/s10463-018-0692-7>) models. DFR is implemented using the Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) algorithm, with linear and logistic SGL models supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported.
| Version: | 0.1.6 |
| Imports: | sgs,caret,MASS, methods, stats, grDevices, graphics,Matrix |
| Suggests: | SGL,gglasso,glmnet,testthat |
| Published: | 2025-09-30 |
| DOI: | 10.32614/CRAN.package.dfr |
| Author: | Fabio Feser [aut, cre] |
| Maintainer: | Fabio Feser <ff120 at ic.ac.uk> |
| BugReports: | https://github.com/ff1201/dfr/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/ff1201/dfr |
| NeedsCompilation: | no |
| Citation: | dfr citation info |
| Materials: | README |
| CRAN checks: | dfr results |
Documentation:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=dfrto link to this page.