r - How to perfrom MAP estimation in a Bayesian Network in R?

Question

I have to perform the Bayesian Network test, using MAP estimation (Maximum A Posteriori). I have a (gaussian) dataframe consisting of 5 variables, varying from 2 till 16 unique values per variable. Does anyone know who to set the arcs using the bnlearn package and how to perform the estimate learning? Right now I receive the following error:

Error in check.fitting.method(method, data) : Bayesian parameter estimation for (conditional) Gaussian Bayesian networks is not implemented.

   #MAP estimate learning
   library(bnlearn)
   pdag = iamb(BayesianDF)
   pdag
   #How to set arcs for the other nodes?
   dag = set.arc(pdag, from = "results.cluster", to = "type_touch")
   fit = bn.fit(dag, BayesianDF, method = "bayes")
   fit

I expect to get parameter estimates which represent the (conditional) probabilities of the Bayesian Network.