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:

Reference manual:	VSOLassoBag.pdf
Vignettes:	VSOLassoBag

Downloads:

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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