Package: HydeNet
Type: Package
Title: Hybrid Bayesian Networks Using R and JAGS
Version: 0.10.3
Date: 2015-01-18
Author: Jarrod E. Dalton <daltonj@ccf.org> and Benjamin Nutter <benjamin.nutter@gmail.com>
Maintainer: Benjamin Nutter <benjamin.nutter@gmail.com>
Description: 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.
License: MIT + file LICENSE
Depends: R (>= 3.0.0), nnet, rjags
Imports: ArgumentCheck, DiagrammeR (>= 0.8), plyr, dplyr, graph,
        gRbase, magrittr, pixiedust (>= 0.6.1), stats, stringr, utils
Suggests: knitr, survival, testthat
VignetteBuilder: knitr
SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net)
LazyLoad: yes
LazyData: true
URL: https://github.com/nutterb/HydeNet,
BugReports: https://github.com/nutterb/HydeNet/issues
NeedsCompilation: no
Packaged: 2016-02-05 13:36:55 UTC; Nutter
Repository: CRAN
Date/Publication: 2016-02-05 19:41:00
