count

<p>A simulated dataset for illustrating our proposed method for inferening networks from next generation sequencing data.</p>

datasets

Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to Liang, F., Song, Q. and Qiu, P. (2015) <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data, jointly estimating multiple Gaussian Graphical Models, constructing single graphical model for heterogeneous dataset, inferring graphical models from high-dimensional missing data and estimating moral graph for Bayesian network.

Bochao Jia

equSA

Learning High-Dimensional Graphical Models

count function

<p>count dataset is a 100x200 matrix. Each row represents a observation and each column represents a variable. It is generated from an overdispersion and zero-inflated Possion distribution.</p>

count: An example of count dataset for constructing network

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