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 (gaussian and binary)
GGMM                    Learning high-dimensional Gaussian Graphical
                        Models with Heterogeneous Data.
GauSim                  Simulate centered Gaussian data from multiple
                        types of structures.
GraphIRO                Learning high-dimensional Gaussian Graphical
                        Models with Missing Observations.
JGGM                    Joint estimation of Multiple Gaussian Graphical
                        Models
JMGM                    Joint Mixed Graphical Models
MNR                     Markov Neighborhood Regression for
                        High-Dimensional Inference.
Mulpval                 Multiple hypothesis tests for p values
SR0                     One example dataset for psi-learning alogorithm
SimGraDat               Simulate Incomplete Data for Gaussian Graphical
                        Models
SimHetDat               Simulate Heterogeneous Data for Gaussian
                        Graphical Models
SimMNR                  Simulate Data for high-dimensional inference
TR0                     One example dataset for psi-learning alogorithm
alarm                   One example dataset for p-plearning algorithm.
combineR                Combine two networks.
count                   An example of count dataset for constructing
                        network
diffR                   Detect difference between two networks.
equSA-package           Graphical model has been widely used in many
                        scientific fileds to describe the conditional
                        independent relationships for a large set of
                        random variables. Through this package, we
                        provide tools to learn structure for undirected
                        graph (Markov Random Field) and moral graph for
                        directed acyclic graph (Bayesian Network).
equSAR                  An equvalent mearsure of partial correlation
                        coeffients
pcorselR                Multiple hypothesis test
plearn.moral            Learning Moral graph based on p-learning
                        algorithm.
plearn.struct           Infer network structure for mixed types of
                        random variables.
plotGraph               Plot Single Network
plotJGraph              Plot Networks
psical                  A calculation of psi scores.
solcov                  Calculate covariance matrix and precision
                        matrix
