Jie Ren, Luann C. Jung, Yinhao Du, Cen Wu, Yu Jiang, Junhao Liu
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.

Network-based regularization has achieved success in variable selection for
high-dimensional biological data due to its ability to incorporate correlations
between genomic features. This package provides procedures for fitting network-based
regularization, minimax concave penalty (MCP) and lasso penalty for generalized linear
models. This current version, regnet0.2.0, focuses on binary outcomes. Functions for
continuous, survival outcomes and other regularization methods will be included in the
forthcoming upgraded versions.

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.

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>). 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.