PoissonNode

Represents a Poisson distribution node in a DAG

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

PoissonNode function

Object of <code><a rd-options="" href="/link/R6Class?package=sourceR&version=1.1.0" data-mini-rdoc="sourceR::R6Class">R6Class</a></code> with methods for constructing a DAG.

Format

<dl class="dl-horizontal">
 <dt><code>new(data, lambda, offset)</code></dt><dd>create a PoissonNode, with mean <a rd-options="" href="/link/Node?package=sourceR&version=1.1.0" data-mini-rdoc="sourceR::Node">Node</a> <code>lambda</code>, and offset <a rd-options="" href="/link/Node?package=sourceR&version=1.1.0" data-mini-rdoc="sourceR::Node">Node</a> <code>offset</code>.</dd>
 </dl>

Methods

PoissonNode — PoissonNode

Object of <code><a rd-options='' href='R6Class'>R6Class</a></code> with methods for constructing a DAG.

PoissonNode: PoissonNode

Description

Arguments

Value

Format

Methods

Details