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 [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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