VSOLassoBag: Variable Selection Oriented LASSO Bagging Algorithm
A wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient, stable, and high confidential variables from omics-based data. Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) <doi:10.1126/scitranslmed.aax7533> for more details.
| Version: | 1.0 |
| Depends: | R (≥ 3.6.0) |
| Imports: | glmnet,survival,ggplot2,POT, parallel, utils,pbapply, methods,SummarizedExperiment |
| Suggests: | rmarkdown,knitr,rmdformats,qpdf |
| Published: | 2025-09-01 |
| DOI: | 10.32614/CRAN.package.VSOLassoBag |
| Author: | Jiaqi Liang [aut], Chaoye Wang [aut, cre] |
| Maintainer: | Chaoye Wang <wangcy1 at sysucc.org.cn> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | VSOLassoBag results |
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