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: |
1.0.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
glmnet, stats, Rcpp, igraph, utils |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
testthat, covr |
| Published: |
2022-08-18 |
| 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 |
Documentation:
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