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.