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dfr  

Dual Feature Reduction for SGL
View on CRAN: Click here


Download and install dfr package within the R console
Install from CRAN:
install.packages("dfr")

Install from Github:
library("remotes")
install_github("cran/dfr")

Install by package version:
library("remotes")
install_version("dfr", "0.1.6")



Attach the package and use:
library("dfr")
Maintained by
Fabio Feser
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-26
Latest Update: 2025-03-06
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.
How to cite:
Fabio Feser (2024). dfr: Dual Feature Reduction for SGL. R package version 0.1.6, https://cran.r-project.org/web/packages/dfr. Accessed 12 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:31), 0.1.0 (2024-09-26 13:10), 0.1.1 (2024-11-16 17:20), 0.1.2 (2024-11-28 13:50), 0.1.3 (2025-02-03 17:20), 0.1.4 (2025-02-06 11:30), 0.1.5 (2025-03-06 19:00)
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Complete documentation for dfr
Functions, R codes and Examples using the dfr R package
Full dfr package functions and examples
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