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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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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.
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