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 <kaiqiao.li@stonybrook.edu>
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) <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.
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
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 15:53:34 UTC; windows
Archs: i386, x64
