## ----------------------------------------------------------------------------|
# Sampling from G-Wishart AND Wishart distribution
## ----------------------------------------------------------------------------|
# sampling from G-Wishart distribution
rgwish = function( n = 1, adj.g = NULL, b = 3, D = NULL )
{
	if( b <= 2 ) stop( "In G-Wishart distribution parameter 'b' has to be more than 2" )
	if( is.null( adj.g ) ) stop( "Adjacency matrix should be determined" )

	G <- as.matrix( adj.g )
	if( sum( ( G == 1 ) * ( G == 0 ) ) != 0 ) stop( "Elements of matrix G should be zero or one" )	

	if( !isSymmetric( G ) )
	{
		G[ lower.tri( G, diag( TRUE ) ) ] <- 0
		G  = G + t( G )
	}
	
	p <- nrow( G )  
	
	if( is.null(D) ) D <- diag(p)
		
	if( dim( D )[1] != p ) stop( "Dimension of matrix G and D must to be the same." )
		
	Ti      = chol( solve( D ) )
	K       = matrix( 0, p, p )
	
	if( n > 1 )
	{
		samples = array( 0, c( p, p, n ) )
		
		for( i in 1 : n )
		{
			result          = .C( "rgwish_c", as.integer(G), as.double(Ti), K = as.double(K), as.integer(b), as.integer(p), PACKAGE = "BDgraph" )
			samples[ , , i] = matrix( result $ K, p, p ) 		
		}
	}else{
	
		result  = .C( "rgwish_c", as.integer(G), as.double(Ti), K = as.double(K), as.integer(b), as.integer(p), PACKAGE = "BDgraph" )
		samples = matrix( result $ K, p, p ) 		
	}

	return( samples )   
}