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>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.

Version: 1.0.1
Depends: R (≥ 4.0.0)
Imports: glmnet, stats, Rcpp, igraph, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, covr
Published: 2024-02-22
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
In views: Omics
CRAN checks: regnet results

Documentation:

Reference manual: regnet.pdf

Downloads:

Package source: regnet_1.0.1.tar.gz
Windows binaries: r-devel: regnet_1.0.0.zip, r-release: regnet_1.0.1.zip, r-oldrel: regnet_1.0.1.zip
macOS binaries: r-release (arm64): regnet_1.0.1.tgz, r-oldrel (arm64): regnet_1.0.1.tgz, r-release (x86_64): regnet_1.0.1.tgz
Old sources: regnet archive

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