SE.cpt

<p>This is a conditional probability table used in the emesis example of the JSS article.</p>

datasets

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.

Benjamin Nutter

HydeNet

Hybrid Bayesian Networks Using R and JAGS

SE.cpt function

<p>An object of class <code>cpt</code> (inherits from <code>array</code>) of dimension 2 x 2 x 2.</p>

SE.cpt: Conditional Probability Table for side effects as a function of emesis and drug.

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