Cont2Gaus               A transfomation from count data into Gaussian
                        data
ContSim                 A simulation method for generating count data
                        from multivariate Zero-Inflated Negative
                        Binomial distributions
ContTran                A data continuized transformation
DAGsim                  Simulate a directed acyclic graph with mixed
                        data (continuous and binary)
JGGM                    Joint estimation of Multiple Gaussian Graphical
                        Models
SR0                     One example dataset for equSA
SR0_mat                 The adjacency matrix for SR0 dataset.
TR0                     One example dataset for equSA
TR0_mat                 The adjacency matrix for TR0 dataset.
combineR                Combine two networks.
count                   An example of count dataset for constructing
                        networks
diffR                   Detect difference between two networks.
equSA-package           Graphical model has been widely used in may
                        scientific fileds to describe the conditional
                        independent relationships for a large set of
                        random variables. Through this package, we
                        provide tools to learn both undirected graph
                        (Markov Random Field) and directed acyclic
                        graph (Bayesian Network). p
equSAR                  An equvalent mearsure of partial correlation
                        coeffients
mixed3000               One example dataset for p_learning
p_learning              Construct Bayesian Network based on p-learning
                        algorithm.
pcorselR                Multiple hypothesis test
plotGraph               Plot Single Network
plotJGraph              Plot Networks
psical                  An calculation of psi scores.
simtoequiv              Transform a directed acyclic graph into an
                        equivalent correct graph.
solcov                  Calculate covariance matrix and precision
                        matrix
