DirichletProcessNode

Represents a Dirichlet process as a single 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

DirichletProcessNode 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(theta, s, alpha, base, ...)</code></dt><dd>Create a DirichletProcessNode with value vector
 <code>theta</code> (length &gt; 1), initial grouping vector <code>s</code>, concentration parameter <code>alpha</code>, and
 base distribution <code>base</code>. Base should be a distribution function (dnorm, dgamma, etc) whose parameters
 are specified in <code>...</code>.</dd>
 </dl>

Methods

DirichletProcessNode — DirichletProcessNode

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

DirichletProcessNode: DirichletProcessNode

Description

Arguments

Value

Format

Methods

Details