Learning High-Dimensional Graphical Models

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Documentation for package ‘equSA’ version 1.2.1

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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).
alarm One example dataset for p-plearning algorithm.
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 network
DAGsim Simulate a directed acyclic graph with mixed data (gaussian and binary)
diffR Detect difference between two networks.
equSAR An equvalent mearsure of partial correlation coeffients
GauSim Simulate centered Gaussian data from multiple types of structures.
GGMM Learning high-dimensional Gaussian Graphical Models with Heterogeneous Data.
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
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.
SimGraDat Simulate Incomplete Data for Gaussian Graphical Models
SimHetDat Simulate Heterogeneous Data for Gaussian Graphical Models
SimMNR Simulate Data for high-dimensional inference
solcov Calculate covariance matrix and precision matrix
SR0 One example dataset for psi-learning alogorithm
TR0 One example dataset for psi-learning alogorithm