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: 0.1.5 Imports: sgs, caret, MASS, methods, stats, grDevices, graphics, Matrix Suggests: SGL, gglasso, glmnet, testthat Published: 2025-03-06 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 form https://CRAN.R-project.org/package=dfr to link to this page.
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