A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

Functions for Learning Bayesian Inference

package-ubuntu-20.04-r-4.0.5#cran#LearnBayes-2.15.1

<p>Collapses a matrix by summing over a specific number of rows 
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>data</code></td>
<td>
<p>a matrix</p>
</td>
</tr>
<tr valign="top">
<td><code>g</code></td>
<td>
<p>a positive integer beween 1 and the number of rows of data</p>
</td>
</tr>
</table>

<p>reduced matrix found by summing over rows
</p>

data=matrix(c(1:20),nrow=4,ncol=5)
g=2
regroup(data,2)

package-ubuntu-20.04-r-4.0.5#cran#LearnBayes-2.15.1#docs#regroup

regroup

Description

Usage

Arguments

Value

Author(s)

Examples

LearnBayes

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`data`	a matrix
`g`	a positive integer beween 1 and the number of rows of data