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GGMM (version 1.0.1)

Mixture Gaussian Graphical Models

Description

The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. For many real problems, the data are heterogeneous, which may contain some subgroups or come from different resources. This package provide a Gaussian Graphical Mixture Model (GGMM) for the heterogeneous data. You can refer to Jia, B. and Liang, F. (2018) at for detail.

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Version

Install

install.packages('GGMM')

Monthly Downloads

7

Version

1.0.1

License

GPL-2

Maintainer

Bochao Jia

Last Published

March 19th, 2019

Functions in GGMM (1.0.1)

GGMM

Learning high-dimensional Gaussian Graphical Models with Heterogeneous Data.
BRGM

Learning gene regulatory networks for breast cancer.
GGMM-package

Gaussian Graphical Mixture Models
SimHetDat

Simulate Heterogeneous Data for Gaussian Graphical Models
breast

Example dataset for learning gene regulatory network.