\name{print.bdgraph}
\alias{print.bdgraph}

\title{Print function for \code{S3} class \code{"bdgraph"} }

\description{
Prints the information about the selected graph which could be a graph with links for which their estimated posterior probabilities are greater than 0.5
or graph with the highest posterior probability.
It provides adjacency matrix, size and posterior probability of the selected graph.
}

\usage{\method{print}{bdgraph}( x, ... )}

\arguments{
  \item{x}{     An object of \code{S3} class \code{"bdgraph"}, from function \code{\link{bdgraph}}. }
  \item{\dots}{ System reserved (no specific usage). }
}

\references{
Mohammadi, R. and Wit, E. C. (2019). \pkg{BDgraph}: An \code{R} Package for Bayesian Structure Learning in Graphical Models, \emph{Journal of Statistical Software}, 89(3):1-30 

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, \emph{Bayesian Analysis}, 10(1):109-138

Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, \emph{arXiv preprint arXiv:1706.04416v2} 

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, \emph{Annals of Applied Statistics}, 12(2):815-845

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, \emph{Journal of the Royal Statistical Society: Series C}, 66(3):629-645 
}

\author{ Reza Mohammadi \email{a.mohammadi@uva.nl} and Ernst Wit }

\seealso{ \code{\link{bdgraph}}, \code{\link{bdgraph.mpl}} }

\examples{
\dontrun{
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, vis = TRUE )
  
bdgraph.obj <- bdgraph( data = data.sim )
   
print( bdgraph.obj )
}
}

\keyword{print}