A network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our recent preprint by Ahn S and Oh EJ (2025) <doi:10.48550/arXiv.2505.22986>.
Version: | 0.0.2 |
Depends: | R (≥ 3.5.0) |
Imports: | huge, glmnet, dplyr, stats, plsgenomics |
Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
Published: | 2025-06-03 |
DOI: | 10.32614/CRAN.package.NetGreg |
Author: | Seungjun Ahn |
Maintainer: | Seungjun Ahn <seungjun.ahn at mountsinai.org> |
License: | GPL-3 |
NeedsCompilation: | no |
CRAN checks: | NetGreg results |
Reference manual: | NetGreg.pdf |
Package source: | NetGreg_0.0.2.tar.gz |
Windows binaries: | r-devel: NetGreg_0.0.2.zip, r-release: NetGreg_0.0.2.zip, r-oldrel: NetGreg_0.0.2.zip |
macOS binaries: | r-release (arm64): NetGreg_0.0.2.tgz, r-oldrel (arm64): NetGreg_0.0.2.tgz, r-release (x86_64): NetGreg_0.0.2.tgz, r-oldrel (x86_64): NetGreg_0.0.2.tgz |
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