Package: dfr
Title: Dual Feature Reduction for SGL
Version: 0.1.6
Date: 2025-09-30
Authors@R: person("Fabio", "Feser", role = c("aut", "cre"), email = "ff120@ic.ac.uk",comment = c(ORCID = "0009-0007-3088-9727"))
Maintainer: Fabio Feser <ff120@ic.ac.uk>
Description: 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.
Imports: sgs, caret, MASS, methods, stats, grDevices, graphics, Matrix
Suggests: SGL, gglasso, glmnet, testthat
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
License: GPL (>= 3)
Encoding: UTF-8
URL: https://github.com/ff1201/dfr
BugReports: https://github.com/ff1201/dfr/issues
Config/pak/sysreqs: libicu-dev
Repository: https://ff1201.r-universe.dev
Date/Publication: 2026-06-28 11:10:14 UTC
RemoteUrl: https://github.com/ff1201/dfr
RemoteRef: HEAD
RemoteSha: e3088c9c8eba4e979834d1c3bd70dfb524ff264f
NeedsCompilation: no
Packaged: 2026-06-28 12:00:35 UTC; root
Author: Fabio Feser [aut, cre] (ORCID: <https://orcid.org/0009-0007-3088-9727>)