The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

RSO: Ridge Selection Operator for Sparse Linear Regression

Implements the Ridge Selection Operator (RSO) for variable selection in linear regression as proposed by Wu (2021) <doi:10.1080/00401706.2020.1791254>. The RSO method extends classical ridge regression by using individually penalized ridge parameters, inducing sparsity through reciprocal penalty parameters. This package provides a fast C++ implementation ('RSOFast') using 'Armadillo' linear algebra routines. The fast implementation precomputes matrix products, uses Cholesky factorization with primal/dual switching, and performs golden-section search for coordinate optimization.

Version: 1.0.0
Imports: Rcpp
LinkingTo: Rcpp, RcppArmadillo
Published: 2026-07-06
DOI: 10.32614/CRAN.package.RSO
Author: Murat Genc [aut, cre], Adewale Lukman [aut]
Maintainer: Murat Genc <mgenc at cu.edu.tr>
License: GPL (≥ 3)
NeedsCompilation: yes
CRAN checks: RSO results

Documentation:

Reference manual: RSO.html , RSO.pdf

Downloads:

Package source: RSO_1.0.0.tar.gz
Windows binaries: r-devel: RSO_1.0.0.zip, r-release: RSO_1.0.0.zip, r-oldrel: RSO_1.0.0.zip
macOS binaries: r-release (arm64): RSO_1.0.0.tgz, r-oldrel (arm64): RSO_1.0.0.tgz, r-release (x86_64): RSO_1.0.0.tgz, r-oldrel (x86_64): RSO_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=RSO to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.