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:
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