regnet: Network-Based Regularization for Generalized Linear Models

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.

Version: 0.4.0
Depends: R (≥ 3.5.0)
Imports: glmnet, stats, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Published: 2019-06-08
Author: Jie Ren, Luann C. Jung, Yinhao Du, Cen Wu, Yu Jiang, Junhao Liu
Maintainer: Jie Ren <jieren at ksu.edu>
BugReports: https://github.com/jrhub/regnet/issues
License: GPL-2
URL: https://github.com/jrhub/regnet
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: regnet results

Downloads:

Reference manual: regnet.pdf
Package source: regnet_0.4.0.tar.gz
Windows binaries: r-devel: regnet_0.4.0.zip, r-release: regnet_0.4.0.zip, r-oldrel: regnet_0.4.0.zip
OS X binaries: r-release: regnet_0.4.0.tgz, r-oldrel: regnet_0.4.0.tgz
Old sources: regnet archive

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