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: |
0.1.2 |
| Imports: |
sgs, caret, MASS, methods, stats, grDevices, graphics, Matrix |
| Suggests: |
SGL, gglasso, glmnet, testthat |
| Published: |
2024-11-28 |
| 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 |
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