DESCRIPTION

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
Roxygen: list(markdown = 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