Network-based regularization has achieved success in variable selections for high-dimensional biological data, due to its ability to incorporate the correlations among genomic features. This package provides procedures for fitting network-based regularization, minimax concave penalty (MCP) and lasso penalty for generalized linear models. This first version, regnet0.1.1, focuses on binary outcomes. Functions for continuous, survival outcomes and other regularization methods will be included in the forthcoming upgraded version.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.1.0) |
| Imports: | glmnet, stats |
| Published: | 2017-05-23 |
| 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: | no |
| Materials: | README |
| CRAN checks: | regnet results |
| Reference manual: | regnet.pdf |
| Package source: | regnet_0.1.1.tar.gz |
| Windows binaries: | r-devel: regnet_0.1.1.zip, r-release: regnet_0.1.1.zip, r-oldrel: regnet_0.1.1.zip |
| OS X El Capitan binaries: | r-release: regnet_0.1.1.tgz |
| OS X Mavericks binaries: | r-oldrel: regnet_0.1.1.tgz |
| Old sources: | regnet archive |
Please use the canonical form https://CRAN.R-project.org/package=regnet to link to this page.