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: |
0.99.1 |
Depends: |
R (≥ 3.6.0) |
Imports: |
glmnet, survival, ggplot2, POT, parallel, utils, pbapply, methods, SummarizedExperiment |
Suggests: |
rmarkdown, knitr, rmdformats, qpdf |
Published: |
2023-03-24 |
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:
Downloads:
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