Cats

Shamelessly adapted from Field (2017).

datasets

Derived from the work of Kruschke (2015,
<ISBN:9780124058880>), the present package aims to provide a framework
for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC)
sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer,
2003, <https://mcmc-jags.sourceforge.io>). The initial version
includes several modules for conducting Bayesian equivalents of
chi-squared tests, analysis of variance (ANOVA), multiple
(hierarchical) regression, softmax regression, and for fitting data
(e.g., structural equation modeling).

<c3><98>ystein Olav Skaar

Bayesian Framework for Computational Modeling

Cats function

A data frame with 2000 rows and 4 variables:<dl class="dl-horizontal">
 <dt><code>Reward</code></dt><dd>integer Food or Affection</dd>
 <dt><code>Dance</code></dt><dd>integer Yes or No</dd>
 <dt><code>Alignment</code></dt><dd>integer Good or Evil</dd>
 <dt><code>Ratings</code></dt><dd>double Cats rate their owners (average of multiple seven-point Likert-type scale (1 = Hate ... 7 = Love)</dd> 
</dl>

Format

Dataset with Cats — Cats

A data frame with 2000 rows and 4 variables:<dl class='dl-horizontal'>
 <dt><code>Reward</code></dt><dd>integer Food or Affection</dd>
 <dt><code>Dance</code></dt><dd>integer Yes or No</dd>
 <dt><code>Alignment</code></dt><dd>integer Good or Evil</dd>
 <dt><code>Ratings</code></dt><dd>double Cats rate their owners (average of multiple seven-point Likert-type scale (1 = Hate ... 7 = Love)</dd> 
</dl>

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Cats: Dataset with Cats

Description

Usage

Arguments

Format

Details