regnet

Network-Based Regularization for Generalized Linear Models

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.

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Description file content

Package
regnet
Type
Package
Title
Network-Based Regularization for Generalized Linear Models
Version
0.4.0
Author
Jie Ren, Luann C. Jung, Yinhao Du, Cen Wu, Yu Jiang, Junhao Liu
Maintainer
Jie Ren
Description
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) and Ren et al. (2019) ). 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.
Depends
R (>= 3.5.0)
License
GPL-2
Encoding
UTF-8
LazyData
true
Imports
glmnet, stats, Rcpp
URL
BugReports
https://github.com/jrhub/regnet/issues
RoxygenNote
6.1.1
LinkingTo
Rcpp, RcppArmadillo
NeedsCompilation
yes
Packaged
2019-06-07 22:03:33 UTC; JieRen
Repository
CRAN
Date/Publication
2019-06-08 14:30:02 UTC

install.packages('regnet')

0.4.0

2 months ago

https://github.com/jrhub/regnet

Jie Ren

GPL-2

Depends on

R (>= 3.5.0)

Imports

glmnet, stats, Rcpp

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