SPSP: Selection by Partitioning the Solution Paths

An implementation of the feature Selection procedure by Partitioning the entire Solution Paths (namely SPSP) to identify the relevant features rather than using a single tuning parameter. By utilizing the entire solution paths, this procedure can obtain better selection accuracy than the commonly used approach of selecting only one tuning parameter based on existing criteria, cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) <doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and false negative rates) than other variable selection approaches. In addition, it can be flexibly coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized estimators.

Version: 0.2.0
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.7), glmnet, ncvreg, Matrix, lars
LinkingTo: Rcpp
Suggests: testthat (≥ 3.0.0), MASS
Published: 2023-10-22
DOI: 10.32614/CRAN.package.SPSP
Author: Xiaorui (Jeremy) Zhu [aut, cre], Yang Liu [aut], Peng Wang [aut]
Maintainer: Xiaorui (Jeremy) Zhu <zhuxiaorui1989 at gmail.com>
BugReports: https://github.com/XiaoruiZhu/SPSP/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://xiaorui.site/SPSP/, https://github.com/XiaoruiZhu/SPSP
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: SPSP results


Reference manual: SPSP.pdf


Package source: SPSP_0.2.0.tar.gz
Windows binaries: r-devel: SPSP_0.2.0.zip, r-release: SPSP_0.2.0.zip, r-oldrel: SPSP_0.2.0.zip
macOS binaries: r-release (arm64): SPSP_0.2.0.tgz, r-oldrel (arm64): SPSP_0.2.0.tgz, r-release (x86_64): SPSP_0.2.0.tgz, r-oldrel (x86_64): SPSP_0.2.0.tgz
Old sources: SPSP archive


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