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Hybrid Bayesian Networks Using R and JAGS

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-desendents 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.

HydeNet may be installed using


Patched versions from GitHub may be installed using


Please note that you may need to use the ref argument in install_github to get the latest updates. Please visit the GitHub repository to explore branches of the project.

The package includes a colletion of vignettes to help you get started. Use vignette(package = "HydeNet") to see the complete listing of vignettes.

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HydeNet documentation built on July 8, 2020, 5:15 p.m.