rgn.logi

rgn.surv

<p>Simulated data for demonstrating the features of regnet.</p>

datasets

Network-based regularization has achieved success in variable selection for
high-dimensional biological data due to its ability to incorporate correlations among
genomic features. This package provides procedures of network-based variable selection
for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and
Ren et al. (2019) <doi:10.1002/gepi.22194>). Two recent additions are the robust network
regularization for the survival response and the network regularization for continuous
response. Functions for other regularization methods will be included in the forthcoming
upgraded versions.

Jie Ren

regnet

Network-Based Regularization for Generalized Linear Models

rgn function

<p>Each data consists of three components: X, Y, and beta; beta is a vector of the
true coefficients used to generate Y.</p>

rgn: simulated data for demonstrating the features of regnet

Description

Usage

Arguments

Format

Examples