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 nondescendents given information on its parent nodes. Since exact, closedform algorithms are computationally burdensome for inference within hybrid networks that contain a combination of continuous and discrete nodes, particlebased approximation techniques like Markov Chain Monte Carlo are popular. We provide a userfriendly interface to constructing these networks and running inference using the 'rjags' package. Econometric analyses (maximum expected utility under competing policies, value of information) involving decision and utility nodes are also supported.
Package details 


Author  Jarrod E. Dalton <[email protected]> and Benjamin Nutter <[email protected]> 
Maintainer  Benjamin Nutter <[email protected]> 
License  MIT + file LICENSE 
Version  0.10.5 
URL  https://github.com/nutterb/HydeNet 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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