HydeNet (0.10.9)
Hybrid Bayesian Networks Using R and JAGS.
https://github.com/nutterb/HydeNet
http://cran.rproject.org/web/packages/HydeNet
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.
Maintainer:
Benjamin Nutter
Author(s): Jarrod E. Dalton <daltonj@ccf.org> and Benjamin Nutter <benjamin.nutter@gmail.com>
License: MIT + file LICENSE
Uses: checkmate, DiagrammeR, dplyr, magrittr, nnet, pixiedust, plyr, rjags, stringr, survival, RCurl, testthat, knitr
Released about 1 month ago.
8 previous versions
 HydeNet_0.10.8. Released 7 months ago.
 HydeNet_0.10.7. Released about 1 year ago.
 HydeNet_0.10.6. Released about 1 year ago.
 HydeNet_0.10.5. Released about 2 years ago.
 HydeNet_0.10.4. Released over 2 years ago.
 HydeNet_0.10.3. Released about 3 years ago.
 HydeNet_0.10.0. Released over 3 years ago.
 HydeNet_0.9.0. Released over 3 years ago.
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