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 |
Documentation:
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