RAEN: Random Approximate Elastic Net (RAEN) Variable Selection Method

The Proportional Subdistribution Hazard (PSH) model has been popular for estimating the effects of the covariates on the cause of interest in Competing Risks analysis. The fast accumulation of large scale datasets has posed a challenge to classical statistical methods. Current penalized variable selection methods show unsatisfactory performance in ultra-high dimensional data. We propose a novel method, the Random Approximate Elastic Net (RAEN), with a robust and generalized solution to the variable selection problem for the PSH model. Our method shows improved sensitivity for variable selection compared with current methods.

Version: 0.2
Depends: R (≥ 3.5.0), lars
Imports: boot, foreach, doParallel, glmnet, fastcmprsk
Suggests: testthat, knitr, rmarkdown
Published: 2021-02-21
Author: Han Sun and Xiaofeng Wang
Maintainer: Han Sun <han.sunny at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/saintland/RAEN
NeedsCompilation: no
CRAN checks: RAEN results


Reference manual: RAEN.pdf
Vignettes: RAEN_Tutorial


Package source: RAEN_0.2.tar.gz
Windows binaries: r-devel: RAEN_0.2.zip, r-release: RAEN_0.2.zip, r-oldrel: RAEN_0.2.zip
macOS binaries: r-release (arm64): RAEN_0.2.tgz, r-oldrel (arm64): RAEN_0.2.tgz, r-release (x86_64): RAEN_0.2.tgz, r-oldrel (x86_64): RAEN_0.2.tgz
Old sources: RAEN archive


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