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.
|Author||Jie Ren, Luann C. Jung, Yinhao Du, Cen Wu, Yu Jiang, Junhao Liu|
|Date of publication||2017-10-15 03:08:32 UTC|
|Maintainer||Jie Ren <[email protected]>|
|Package repository||View on CRAN|
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