DESCRIPTION

Package: VSOLassoBag
Title: A variable-selection oriented LASSO bagging algorithm for biomarker discovery in omic-based translational research
Version: 1.0.0
Authors@R: c(
  person('Qi', 'Zhao',  email = 'zhaoqi@sysucc.org.cn', role = c('aut','cre'), comment = c(ORCID = '0000-0002-8683-6145')),
  person('Jiaqi', 'Liang',  email = 'liangjq27@mail2.sysu.edu.cn', role = c('aut')),
  person('Chaoye', 'Wang',  email = 'wangcy1@sysucc.org.cn', role = c('aut'))
  )
Maintainer: Qi Zhao <zhaoqi@sysucc.org.cn>
Description: VSOLassoBag is 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, VSOLassoBag can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates.
License: GPL-3
Encoding: UTF-8
Depends:
    R (>= 3.5.0)
Imports: 
    glmnet,
    survival,
    ggplot2,
    POT,
    parallel
RoxygenNote: 7.2.0
LazyData: true