  The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for
structure learning and sampling of Bayesian networks. The package includes
tools to search for a maximum a posteriori (MAP) graph and to sample graphs
from the posterior distribution given the data. A new hybrid approach to
structure learning enables inference in large graphs. In the first step, we
define a reduced search space by means of the PC algorithm or based on prior
knowledge. In the second step, an iterative order MCMC scheme proceeds to
optimize within the restricted search space and estimate the MAP graph.
Sampling from the posterior distribution is implemented using either order or
partition MCMC. The models and algorithms can handle both discrete and
continuous data. The BiDAG package also provides an implementation of MCMC
schemes for structure learning and sampling of dynamic Bayesian networks.
