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: | 1.0.1 |
| Depends: | R (≥ 3.0.2) |
| Imports: | mvtnorm, equSA, huge |
| Published: | 2019-03-19 |
| Author: | Bochao Jia [aut, ctb, cre, cph], Faming Liang [ctb] |
| Maintainer: | Bochao Jia <jbc409 at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | GGMM results |
| Reference manual: | GGMM.pdf |
| Package source: | GGMM_1.0.1.tar.gz |
| Windows binaries: | r-devel: GGMM_1.0.1.zip, r-release: GGMM_1.0.1.zip, r-oldrel: GGMM_1.0.1.zip |
| macOS binaries: | r-release: GGMM_1.0.1.tgz, r-oldrel: GGMM_1.0.1.tgz |
| Old sources: | GGMM archive |
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