# Estimate Directed and Undirected Graphical Models and Construct Networks

## Documentation for package ‘equSA’ version 1.1.5

- DESCRIPTION file.

## Help Pages

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.