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-descendents 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 the 'rjags' package. Econometric analyses (maximum expected utility under competing policies, value of information) involving decision and utility nodes are also supported.

Maintainer: Benjamin Nutter <benjamin.nutter at gmail.com>

Author(s): Jarrod E. Dalton <daltonj at ccf.org> and Benjamin Nutter <benjamin.nutter at gmail.com>

Install package and any missing dependencies by running this line in your R console:

install.packages("HydeNet")

Depends R (>= 3.0.0), nnet
Imports checkmate, DiagrammeR(>=0.9.0), plyr, dplyr, graph, gRbase, magrittr, pixiedust(>=0.6.1), rjags, stats, stringr, utils
Suggests knitr, RCurl, survival, testthat
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Package HydeNet
Materials
URL https://github.com/nutterb/HydeNet
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Version 0.10.5
Published 2017-01-13
License MIT + file LICENSE
BugReports https://github.com/nutterb/HydeNet/issues
SystemRequirements JAGS (http://mcmc-jags.sourceforge.net)
NeedsCompilation no
Citation
CRAN checks HydeNet check results
Package source HydeNet_0.10.5.tar.gz