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.


News

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>>>>> ssgraph NEWS
  • CHANGES IN VERSION 1.1

    • The sampling algorithms are implemented in C++ with parallel implementation using OpenMP.
    • Option "method" is added to function "ssgraph()" which is for both GGMs and GCGMs mehtods.
    • Functions "plot.ssgraph()", "summary.ssgraph()", and "print.ssgraph()" are added.
  • CHANGES IN VERSION 1.2

    • Option "save.all" is added to function "ssgraph()".
  • CHANGES IN VERSION 1.4

    • Option "is.discrete" is added to function "ssgraph()".

Reference manual

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install.packages("ssgraph")

1.6 by Reza Mohammadi, a month ago


https://www.uva.nl/profile/a.mohammadi


Browse source code at https://github.com/cran/ssgraph


Authors: Reza Mohammadi


Documentation:   PDF Manual  


Task views: High-Performance and Parallel Computing with R, Bayesian Inference


GPL (>= 2) license


Imports Matrix, igraph

Depends on BDgraph


See at CRAN