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


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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