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

`install.packages("HydeNet")`

Patched versions from GitHub may be installed using

```
setRepositories(ind=1:2)
devtools::install_github("nutterb/HydeNet")
```

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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