equSA: Estimate Directed and Undirected Graphical Models and Construct
Networks
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, for jointly estimating multiple Gaussian Graphical Models and constructing directed acyclic graph (Bayesian Network).
| Version: |
1.1.5 |
| Depends: |
R (≥ 3.0.2) |
| Imports: |
igraph, huge, XMRF, ZIM, mvtnorm, speedglm |
| Published: |
2018-01-20 |
| Author: |
Bochao Jia, Faming Liang, Runmin Shi, Suwa Xu |
| Maintainer: |
Bochao Jia <jbc409 at ufl.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| CRAN checks: |
equSA results |
Downloads:
Reverse dependencies:
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