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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
DOI: 10.32614/CRAN.package.regnet
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_1.0.1.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, r-oldrel (x86_64): regnet_1.0.1.tgz
Old sources: regnet archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=regnet to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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