rats

Rat tumour data from Tarone (1982). Data from Table 5.1 of Bayesian Data Analysis

datasets

Perform nonparametric Bayesian analysis using Dirichlet
processes without the need to program the inference algorithms.
Utilise included pre-built models or specify custom
models and allow the 'dirichletprocess' package to handle the
Markov chain Monte Carlo sampling.
Our Dirichlet process objects can act as building blocks for a variety
of statistical models including and not limited to: density estimation,
clustering and prior distributions in hierarchical models.
See Teh, Y. W. (2011)
<https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>,
among many other sources.

Dean Markwick

dirichletprocess

Build Dirichlet Process Objects for Bayesian Modelling

Gordon J. Ross

Kees Mulder

Giovanni Sighinolfi

Filippo Fiocchi 

rats function

<dl>
<dt>y</dt>
<dd>number of rats with a tumour</dd><dt>N</dt>
<dd>total number of rats in the experiment</dd>
</dl>

Format

Tumour incidences in rats — rats

<dl>
<dt>y</dt>
<dd>number of rats with a tumour</dd>

<dt>N</dt>
<dd>total number of rats in the experiment</dd>


</dl>

rats: Tumour incidences in rats

Description

Usage

Arguments

Format