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
DOI: 10.32614/CRAN.package.dfr
Author: Fabio Feser ORCID iD [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:

Reference manual: dfr.pdf

Downloads:

Package source: dfr_0.1.2.tar.gz
Windows binaries: r-devel: dfr_0.1.2.zip, r-release: dfr_0.1.2.zip, r-oldrel: dfr_0.1.2.zip
macOS binaries: r-release (arm64): dfr_0.1.2.tgz, r-oldrel (arm64): dfr_0.1.2.tgz, r-release (x86_64): dfr_0.1.2.tgz, r-oldrel (x86_64): dfr_0.1.2.tgz
Old sources: dfr archive

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