Variable Selection Oriented LASSO Bagging Algorithm
c(
person('Jiaqi', 'Liang',  email = 'liangjq27@mail2.sysu.edu.cn', role = c('aut')),
person('Chaoye', 'Wang',  email = 'wangcy1@sysucc.org.cn', role = c('aut', 'cre'))
)
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.
glmnet, survival, ggplot2, POT, parallel, utils, pbapply,
methods, SummarizedExperiment
Jiaqi Liang [aut],
Chaoye Wang [aut, cre]

Robin Genuer, Jean-Michel Poggi and Christine Tuleau-Malot
Three steps variable selection procedure based on random
forests. Initially developed to handle high dimensional data
(for which number of variables largely exceeds number of
observations), the package is very versatile and can treat most
dimensions of data, for regression and supervised
classification problems.  First step is dedicated to eliminate
irrelevant variables from the dataset.  Second step aims to
select all variables related to the response for interpretation
purpose.  Third step refines the selection by eliminating
redundancy in the set of variables selected by the second step,
for prediction purpose.

Three steps variable selection procedure based on random forests. Initially developed to
handle high dimensional data (for which number of variables largely exceeds number of observations),
the package is very versatile and can treat most dimensions of data, for regression and	supervised
classification problems.
First step is dedicated to eliminate irrelevant variables from the dataset.
Second step aims to select all variables related to the response for interpretation purpose.
Third step refines the selection by eliminating redundancy in the set of variables selected by the
second step, for prediction purpose.