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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.
Package details |
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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@gmail.com> |
License | GPL-2 |
Version | 1.0.2 |
URL | https://github.com/jrhub/regnet |
Package repository | View on CRAN |
Installation |
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