Update bnlearn to version 3.4. Major changes:

  * move the test counter into bnlearn's namespace.
  * include Tsamardinos' optimizations in mmpc(..., optimized = FALSE),
     but not backtracking, to make it comparable with other learning
     algorithms.
  * check whether the residuals and the fitted values are present before
     trying to plot a bn.fit{,.gnode} object.
  * fixed two integer overflows in factors' levels and degrees of freedom
     in large networks.
  * added {compelled,reversible}.arcs().
  * added the MSE and predictive correlation loss functions to bn.cv().
  * use the unbiased estimate of residual variance to compute the standard
     error in bn.fit(..., method = "mle") (thanks Jean-Baptiste Denis).
  * revised optimizations in constraint-based algorithms, removing most
     false positives by sacrificing speed.
  * fixed warning in cp{dist,query}().
  * added support for ordered factors.
  * implemented the Jonckheere-Terpstra test to support ordered factors
     in constraint-based structure learning.
  * added a plot() method for bn.strength objects containing bootstrapped
     confidence estimates; it prints their ECDF and the estimated
     significance threshold.
  * fixed dimension reduction in cpdist().
  * reimplemented Gaussian rbn() in C, it's now twice as fast.
  * improve precision and robustness of (partial) correlations.
  * remove the old network scripts for network that are now available from
     www.bnlearn.com/bnrepository.
  * implemented likelihood weighting in cp{dist,query}().