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.

Hybrid Bayesian Networks Using R and JAGS

package-ubuntu-20.04-r-4.0.5#cran#HydeNet-0.10.11

<p>Just a wrapper to strip the attributes off, change the class, and print the array.
</p>

## S3 method for class 'cpt'
print(x, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>Object of class <code>cpt</code></p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>Additional arguments to be passed to other methods.</p>
</td>
</tr>
</table>

<p>Jarrod Dalton and Benjamin Nutter
</p>

package-ubuntu-20.04-r-4.0.5#cran#HydeNet-0.10.11#docs#print.cpt

`x`	Object of class `cpt`
`...`	Additional arguments to be passed to other methods.

print.cpt

Description

Usage

Arguments

Author(s)

HydeNet

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