* 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}().
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