# Generate a sample from a multivariate standard normal distribution,
# default is 5-D 100 points
f.gen.norm <- function(n = 100, p = 5) {
  x <- matrix(rnorm(n * p), ncol = p)
  x
}
# Transform the sample through a variance-covariance matrix
f.transf.dat<-function(data, vc)
{
# assuming symmetric vc
        vsvd <- eigen(vc)
        evc <- vsvd$vectors
        evc <- apply(vsvd$vectors, 2, norm.vec)
        evl <- vsvd$values
        trnsfd.dat <- evc %*% diag(sqrt(evl)) %*% t(evc) %*% t(as.matrix(data))
        return(t(trnsfd.dat))
}