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.

Version: 0.0.6
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
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2019-05-10
Author: Kaiqiao Li [aut, cre], Pei Fen Kuan [aut], Xuefeng Wang [aut]
Maintainer: Kaiqiao Li < at>
License: MIT + file LICENSE
NeedsCompilation: yes
CRAN checks: glmaag results


Reference manual: glmaag.pdf
Vignettes: Vignette Title
Package source: glmaag_0.0.6.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel: not available
OS X binaries: r-release: glmaag_0.0.6.tgz, r-oldrel: not available
Old sources: glmaag archive


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