  Bayesian network (BN) modelling is extensively used in systems epidemiology.
Usually it consists in selecting and reporting the best-fitting structure
conditional to the data. A major practical concern is avoiding overfitting, on
account of its extreme flexibility and its modelling richness. Many approaches
have been proposed to control for overfitting. Unfortunately, they essentially
all rely on very crude decisions that result in too simplistic approaches for
such complex systems. In practice, with limited data sampled from complex
system, this approach seems too simplistic. An alternative would be to use the
Monte Carlo Markov chain model choice (MC3) over the network to learn the
landscape of reasonably supported networks, and then to present all possible
arcs with their MCMC support. This paper presents an R implementation, called
mcmcabn, of a flexible structural MC3 that is accessible to non-specialists.
