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: | 1.0.2 |
| Depends: | R (≥ 4.0.0) |
| Imports: | glmnet, stats,Rcpp,igraph, utils |
| LinkingTo: | Rcpp,RcppArmadillo |
| Suggests: | testthat,covr |
| Published: | 2025-02-10 |
| DOI: | 10.32614/CRAN.package.regnet |
| Author: | Jie Ren [aut, cre], Luann C. Jung [aut], Yinhao Du [aut], Cen Wu [aut], Yu Jiang [aut], Junhao Liu [aut] |
| Maintainer: | Jie Ren <renjie0910 at gmail.com> |
| 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 |
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