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dfr

0.1.6

Dual Feature Reduction for SGL

0packages depend
2.3Kdownloads / year
62.6%test coverage
13/13checks pass

Overview

About
Maintained by Fabio FeserFirst published 2024-09-267 releasesCRAN page ↗GitHub ↗

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.

Install

Health

CRAN checks
13OK
Slowest check: 7.7 min · r-devel-windows-x86_64
Code health
Yes
Tests · ratio 0.41
62.6%
Coverage · measured lines
100%
Documentation · exports
8
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-06-09
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-06-08
    12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-05-02
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Show 2 earlier snapshots
  • ERROR2026-04-25
    11 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 273 wordsVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
100%
Return-value docs
100%
References docs
50%

Downloads

2.3K
CRAN downloads in the past year
Rank #20,947 · ~6/day · ~191/mo
Daily download trend is not available in this view yet.
14330 days
54390 days
2.3K1 year
Compare downloads with other packages →
Also on106 r2u19 autocran

Repository

Repository
1Stars
0Forks
2Open issues
0Open PRs
0Releases
9Commits
1Contributors
9 commits · Last activity 2026-06-28

Stars over time

2024-09-24 · 12026-07-07 · 1

Repository practices

Upstream repositoryBeta

2 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/ff1201/dfr on 2026-08-23.

CRAN release process (1)
cran-comments.md
Lint, format, editor (1)
RStudio project
How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)
Depends (0)
none
Imports (8)
sgscaretMASSmethodsstatsgrDevicesgraphicsMatrix
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (1)
Maintainer (1)
Author, Maintainer
Authors (1)
Author, Maintainer
Package Timeline

7 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 0.1.6Latest
    2025-09-30 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • 0.1.5
    2025-03-06 · diff ↗
  • 0.1.4
    2025-02-06 · diff ↗
  • 0.1.3
    2025-02-03 · diff ↗
  • 0.1.2
    2024-11-28 · diff ↗
  • 0.1.1
    2024-11-16 · diff ↗
  • unarchivedReturned to CRAN
    2024-11-16
  • archivedRemoved from CRAN
    2024-11-05
    requires archived package 'faux'
  • 0.1.0
    2024-09-26
  • R
    R 4.4.0 released · 2024-04-24

Package metadata

First published
2024-09-26
Total releases
7 / 2 yrs
License
GPL (>= 3) OSI
Download size
81 KB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("dfr")
Feser, F. (2025). dfr: Dual Feature Reduction for SGL (Version 0.1.6) [Computer software]. https://doi.org/10.32614/CRAN.package.dfr

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for dfr version 0.1.6 [Data set]. HJJB, LLC. Data release v2026-08-23. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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