The R package ssgraph is designed for Bayesian structure learning in graphical models using spike-and-slab priors. To speed up the computations, the computationally intensive tasks of the package are implemented in C++ in parallel using OpenMP.


You can install the latest version from CRAN using:

install.packages( "ssgraph" )
require( "ssgraph" )


This is a simple example to see the preformance of the

Frist, by using the function bdgraph.sim we simulate 60 observations (n = 60) from a multivariate Gaussian distribution with 8 variables (p = 8) and “scale-free” graph structure, as follows:

data.sim = bdgraph.sim( n = 100, p = 8, graph = "scale-free", vis = TRUE )
round( head( data.sim $ data, 4 ), 2 )

Since the generated data are Gaussian, we run ssgraph function by choosing method = "ggm", as follows:

ssgraph.obj <- ssgraph( data = data.sim, method = "ggm", iter = 5000, save = TRUE )

summary( ssgraph.obj )

To compare the result with true graph

compare( data.sim, ssgraph.obj, main = c( "Target", "ssgraph" ), vis = TRUE )