StochasticNode

node

Implements a non-parametric source attribution model to attribute
cases of disease to sources in Bayesian framework with source and type effects.
Type effects are clustered using a Dirichlet Process. Multiple times and
locations are supported.

Poppy Miller

sourceR

Fits a Non-Parametric Bayesian Source Attribution Model

Chris Jewell

Jonathan Marshall

Nigel French

StochasticNode function

Object of <code><a rd-options="" href="/link/R6Class?package=sourceR&version=1.0.1" data-mini-rdoc="sourceR::R6Class">R6Class</a></code> with methods for constructing a DAG.

Format

<dl class="dl-horizontal">
<dt><code>data</code></dt><dd>contains the node&#39;s data</dd>
</dl>

Fields

<dl class="dl-horizontal">
 <dt><code>logPosterior()</code></dt><dd>return the value of the log posterior distribution of the node.</dd>
 <dt><code>getData()</code></dt><dd>returns the node&#39;s data.</dd>
 </dl>

Methods