Facilities for easy implementation of hybrid Bayesian networks using R. Bayesian networks are directed acyclic graphs representing joint probability distributions, where each node represents a random variable and each edge represents conditionality. The full joint distribution is therefore factorized as a product of conditional densities, where each node is assumed to be independent of its non-descendants given information on its parent nodes. Since exact, closed-form algorithms are computationally burdensome for inference within hybrid networks that contain a combination of continuous and discrete nodes, particle-based approximation techniques like Markov Chain Monte Carlo are popular. We provide a user-friendly interface to constructing these networks and running inference using rjags. Econometric analyses (maximum expected utility under competing policies, value of information) involving decision and utility nodes are also supported.
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