High-Dimensional Mixed Graphical Models Estimation

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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("hmgm")

1.0.3 by Mingyu Qi, 14 days ago


< https://arxiv.org/pdf/1304.2810.pdf>


Browse source code at https://github.com/cran/hmgm


Authors: Mingyu Qi , Tianxi Li


Documentation:   PDF Manual  


GPL (>= 2) license


Imports rgl, Matrix, glmnet, MASS, nat, binaryLogic, Rcpp, stats, methods


See at CRAN