GGMM: Mixture Gaussian Graphical Models

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 <arXiv:1805.02547> for detail.

Version: 1.0.0
Depends: R (≥ 3.0.2)
Imports: mvtnorm, equSA, huge
Published: 2018-05-09
Author: Bochao Jia [aut, ctb, cre, cph], Faming Liang [ctb]
Maintainer: Bochao Jia <jbc409 at ufl.edu>
License: GPL-2
NeedsCompilation: yes
CRAN checks: GGMM results

Downloads:

Reference manual: GGMM.pdf
Package source: GGMM_1.0.0.tar.gz
Windows binaries: r-devel: GGMM_1.0.0.zip, r-release: GGMM_1.0.0.zip, r-oldrel: GGMM_1.0.0.zip
OS X binaries: r-release: GGMM_1.0.0.tgz, r-oldrel: GGMM_1.0.0.tgz

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