equSA: Learning High-Dimensional Graphical Models

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.

Version: 1.2.1
Depends: R (≥ 3.0.2)
Imports: igraph, huge, XMRF, ZIM, mvtnorm, speedglm, SIS, ncvreg, survival, bnlearn, doParallel, parallel, foreach
Published: 2019-05-05
Author: Bochao Jia, Faming Liang, Runmin Shi, Suwa Xu
Maintainer: Bochao Jia <jbc409 at gmail.com>
License: GPL-2
NeedsCompilation: yes
CRAN checks: equSA results


Reference manual: equSA.pdf
Package source: equSA_1.2.1.tar.gz
Windows binaries: r-devel: equSA_1.2.1.zip, r-devel-gcc8: equSA_1.2.1.zip, r-release: equSA_1.2.1.zip, r-oldrel: equSA_1.2.1.zip
OS X binaries: r-release: equSA_1.2.1.tgz, r-oldrel: equSA_1.2.1.tgz
Old sources: equSA archive

Reverse dependencies:

Reverse imports: GGMM, IROmiss


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