glmaag

Adaptive LASSO and Network Regularized Generalized Linear Models

Efficient procedures for adaptive LASSO and network regularized for Gaussian, logistic, and Cox model. Provides network estimation procedure (combination of methods proposed by Ucar, et. al (2007) <doi:10.1093/bioinformatics/btm423> and Meinshausen and Buhlmann (2006) <doi:10.1214/009053606000000281>), cross validation and stability selection proposed by Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x> and Liu, Roeder and Wasserman (2010) <arXiv:1006.3316> methods. Interactive R app is available.

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Description file content

Package
glmaag
Title
Adaptive LASSO and Network Regularized Generalized Linear Models
Version
0.0.6
Date
2019-05-09
Author
Kaiqiao Li [aut, cre], Pei Fen Kuan [aut], Xuefeng Wang [aut]
Maintainer
Kaiqiao Li
Description
Efficient procedures for adaptive LASSO and network regularized for Gaussian, logistic, and Cox model. Provides network estimation procedure (combination of methods proposed by Ucar, et. al (2007) and Meinshausen and Buhlmann (2006) ), cross validation and stability selection proposed by Meinshausen and Buhlmann (2010) and Liu, Roeder and Wasserman (2010) methods. Interactive R app is available.
License
MIT + file LICENSE
Encoding
UTF-8
LazyData
true
RoxygenNote
6.1.1
LinkingTo
Rcpp, RcppArmadillo
Depends
R (>= 3.6.0), survival, data.table
Imports
Rcpp (>= 1.0.0), methods, stats, Matrix, ggplot2, gridExtra, maxstat, survminer, plotROC, shiny, foreach, pROC, huge, OptimalCutpoints
Suggests
knitr, rmarkdown
VignetteBuilder
knitr
NeedsCompilation
yes
Packaged
2019-05-10 02:34:26 UTC; Prob
Repository
CRAN
Date/Publication
2019-05-10 07:50:16 UTC

install.packages('glmaag')

0.0.6

2 months ago

Kaiqiao Li

MIT + file LICENSE

Depends on

R (>= 3.6.0), survival, data.table

Imports

Rcpp (>= 1.0.0), methods, stats, Matrix, ggplot2, gridExtra, maxstat, survminer, plotROC, shiny, foreach, pROC, huge, OptimalCutpoints

Suggests

knitr, rmarkdown

Discussions