Package: hmgm
Type: Package
Title: High-Dimensional Mixed Graphical Models Estimation
Version: 1.0.3
Author: Mingyu Qi, Tianxi Li
Maintainer: Mingyu Qi <mq3sq@virginia.edu>
Description: Provides weighted lasso framework for high-dimensional mixed data graph estimation.  
    In the graph estimation stage, the graph structure is estimated by maximizing the conditional 
    likelihood of one variable given the rest. We focus on the conditional loglikelihood of each variable 
    and fit separate regressions to estimate the parameters, much in the spirit of the neighborhood 
    selection approach proposed by Meinshausen-Buhlmann for the Gaussian Graphical Model and by Ravikumar
    for the Ising Model. Currently, the discrete variables can only take two values. In the future, method 
    for general discrete data and for visualizing the estimated graph will be added. 
    For more details, see the linked paper.
URL: <https://arxiv.org/pdf/1304.2810.pdf>
License: GPL (>= 2)
Depends: R(>= 3.5.0)
Imports: rgl, Matrix, glmnet, MASS, nat, binaryLogic, Rcpp, stats,
        methods
NeedsCompilation: yes
Encoding: UTF-8
RoxygenNote: 7.0.1
Packaged: 2020-10-06 22:52:48 UTC; mingyuqi
Repository: CRAN
Date/Publication: 2020-10-07 04:40:02 UTC
Built: R 4.0.2; ; 2020-10-07 10:53:44 UTC; unix
