{
  "_id": "6a114a9bacfb0bcc41ce86d2",
  "Package": "dfr",
  "Title": "Dual Feature Reduction for SGL",
  "Version": "0.1.5",
  "Date": "2025-03-06",
  "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)\napproach for the Sparse Group Lasso (SGL) and the Adaptive\nSparse Group Lasso (aSGL) (Feser and Evangelou (2024)\n<doi:10.48550/arXiv.2405.17094>). The DFR approach is a feature\nreduction approach that applies strong screening to reduce the\nfeature space before optimisation, leading to speed-up\nimprovements for fitting SGL (Simon et al. (2013)\n<doi:10.1080/10618600.2012.681250>) and aSGL (Mendez-Civieta et\nal. (2020) <doi:10.1007/s11634-020-00413-8> and Poignard (2020)\n<doi:10.1007/s10463-018-0692-7>) models. DFR is implemented\nusing the Adaptive Three Operator Splitting (ATOS) (Pedregosa\nand Gidel (2018) <doi:10.48550/arXiv.1804.02339>) algorithm,\nwith linear and logistic SGL models supported, both of which\ncan be fit using k-fold cross-validation. Dense and sparse\ninput matrices are supported.",
  "Roxygen": "list(markdown = TRUE)",
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  "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": "2025-03-09 08:50:40 UTC",
  "RemoteUrl": "https://github.com/ff1201/dfr",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-23 06:30:33 UTC",
    "User": "root"
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  "Author": "Fabio Feser [aut, cre] (ORCID: <https://orcid.org/0009-0007-3088-9727>)",
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  "_published": "2026-05-23T06:35:07.083Z",
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      "page": "dfr_adap_sgl",
      "title": "Fit a DFR-aSGL model.",
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