For Bayesian inference in undirected graphical models using spike-and-slab priors, continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models' literature, including Wang (2015) <doi:10.1214/14-BA916>. To speed up the computations, the computationally intensive tasks of the package are implemented in C++ in parallel using OpenMP.
| Version: | 1.4 |
| Depends: | BDgraph (≥ 2.49) |
| Imports: | Matrix, igraph |
| Published: | 2018-07-04 |
| Author: | Reza Mohammadi |
| 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 |
| Reference manual: | ssgraph.pdf |
| Package source: | ssgraph_1.4.tar.gz |
| Windows binaries: | r-devel: ssgraph_1.4.zip, r-release: ssgraph_1.4.zip, r-oldrel: ssgraph_1.4.zip |
| OS X binaries: | r-release: ssgraph_1.4.tgz, r-oldrel: ssgraph_1.4.tgz |
| Old sources: | ssgraph archive |
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