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. Two recent additions are the robust network regularization for the survival response and the network regularization for continuous response. Functions for other regularization methods will be included in the forthcoming upgraded versions.
| Version: | 0.3.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | glmnet, stats, Rcpp |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2018-05-22 |
| 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 |
| CRAN checks: | regnet results |
| Reference manual: | regnet.pdf |
| Package source: | regnet_0.3.0.tar.gz |
| Windows binaries: | r-devel: regnet_0.3.0.zip, r-release: regnet_0.3.0.zip, r-oldrel: regnet_0.3.0.zip |
| OS X binaries: | r-release: regnet_0.3.0.tgz, r-oldrel: regnet_0.3.0.tgz |
| Old sources: | regnet archive |
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