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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 |
| 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 |
| Reference manual: | VSOLassoBag.pdf |
| Vignettes: |
VSOLassoBag |
| Package source: | VSOLassoBag_0.99.1.tar.gz |
| Windows binaries: | r-devel: VSOLassoBag_0.99.1.zip, r-release: VSOLassoBag_0.99.1.zip, r-oldrel: VSOLassoBag_0.99.1.zip |
| macOS binaries: | r-release (arm64): VSOLassoBag_0.99.1.tgz, r-oldrel (arm64): VSOLassoBag_0.99.1.tgz, r-release (x86_64): VSOLassoBag_0.99.1.tgz, r-oldrel (x86_64): VSOLassoBag_0.99.1.tgz |
| Old sources: | VSOLassoBag archive |
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