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.

Package details

AuthorHan Sun and Xiaofeng Wang
MaintainerHan Sun <han.sunny@gmail.com>
LicenseGPL (>= 2)
URL https://github.com/saintland/RAEN
Package repositoryView on CRAN
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RAEN documentation built on Feb. 22, 2021, 9:07 a.m.