Package ‘regnet’
| dc.contributor.author | Ren, Jie | |
| dc.contributor.author | Jung, Luann C. | |
| dc.contributor.author | Du, Yinhao | |
| dc.contributor.author | Wu, Cen | |
| dc.contributor.author | Jiang, Yu | |
| dc.contributor.author | Liu, Junhao | |
| dc.date.accessioned | 2022-10-10T21:38:29Z | |
| dc.date.available | 2022-10-10T21:38:29Z | |
| dc.date.issued | 2022-08-18 | |
| dc.date.published | 2022 | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.uri | https://hdl.handle.net/2097/42523 | |
| dc.rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | en_US |
| dc.rights.uri | https://rightsstatements.org/vocab/InC/1.0/ | en_US |
| dc.title | Package ‘regnet’ | en_US |
| dc.title.alternative | Network-Based Regularization for Generalized Linear Models | |
| dc.type | Text | en_US |
