SGPR: Sparse Group Penalized Regression for Bi-Level Variable Selection

Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) <doi:10.1002/bimj.202200334>.

Version:	0.1.2
Imports:	Rcpp
LinkingTo:	Rcpp
Published:	2024-05-16
DOI:	10.32614/CRAN.package.SGPR
Author:	Gregor Buch [aut, cre, cph], Andreas Schulz [ths], Irene Schmidtmann [ths], Konstantin Strauch [ths], Philipp Wild [ths]
Maintainer:	Gregor Buch <buchgregor at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	SGPR results

Documentation:

Reference manual:

SGPR.pdf

Downloads:

Package source:	SGPR_0.1.2.tar.gz
Windows binaries:	r-devel: SGPR_0.1.2.zip, r-release: SGPR_0.1.2.zip, r-oldrel: SGPR_0.1.2.zip
macOS binaries:	r-release (arm64): SGPR_0.1.2.tgz, r-oldrel (arm64): SGPR_0.1.2.tgz, r-release (x86_64): SGPR_0.1.2.tgz, r-oldrel (x86_64): SGPR_0.1.2.tgz

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