Package: HydeNet
Type: Package
Title: Hybrid Bayesian Networks Using R and JAGS
Version: 0.10.11
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
Imports: checkmate, DiagrammeR (>= 0.9.0), plyr, dplyr, graph,
        magrittr, pixiedust (>= 0.6.1), rjags, stats, stringr, utils
Suggests: knitr, RCurl, rmarkdown, 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
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-07-06 12:40:35 UTC; benjamin
Repository: CRAN
Date/Publication: 2020-07-06 15:20:13 UTC
Built: R 4.0.5; ; 2022-04-21 14:32:54 UTC; windows
