ssgraph: Bayesian Graphical Estimation using Spike-and-Slab Priors

Bayesian estimation of graph structure learning in undirected graphical models using spike-and-slab priors. The package handles continuous, discrete, and mixed data. To speed up the computations, the computationally intensive tasks of the package are implemented in C++ in parallel using OpenMP.

Version: 1.6
Depends: BDgraph (≥ 2.52)
Imports: Matrix, igraph
Published: 2018-11-08
Author: Reza Mohammadi ORCID iD
Maintainer: Reza Mohammadi <a.mohammadi at uva.nl>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://www.uva.nl/profile/a.mohammadi
NeedsCompilation: yes
Citation: ssgraph citation info
Materials: NEWS
In views: Bayesian, HighPerformanceComputing
CRAN checks: ssgraph results

Downloads:

Reference manual: ssgraph.pdf
Package source: ssgraph_1.6.tar.gz
Windows binaries: r-devel: ssgraph_1.6.zip, r-release: ssgraph_1.6.zip, r-oldrel: ssgraph_1.6.zip
OS X binaries: r-release: ssgraph_1.6.tgz, r-oldrel: ssgraph_1.6.tgz
Old sources: ssgraph archive

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