Estimate Directed and Undirected Graphical Models and Construct Networks

Documentation for package ‘equSA’ version 1.1.5

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
combineR	Combine two networks.
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
count	An example of count dataset for constructing networks
DAGsim	Simulate a directed acyclic graph with mixed data (continuous and binary)
diffR	Detect difference between two networks.
equSAR	An equvalent mearsure of partial correlation coeffients
JGGM	Joint estimation of Multiple Gaussian Graphical Models
mixed3000	One example dataset for p_learning
pcorselR	Multiple hypothesis test
plotGraph	Plot Single Network
plotJGraph	Plot Networks
psical	An calculation of psi scores.
p_learning	Construct Bayesian Network based on p-learning algorithm.
simtoequiv	Transform a directed acyclic graph into an equivalent correct graph.
solcov	Calculate covariance matrix and precision matrix
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.