Flexible and informed regression with Multiple Change Points (MCP). 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.

Regression with Multiple Change Points

package-ubuntu-20.04-r-4.0.5#cran#mcp-0.3.0

<p>The mcpprior is just a list, but it can be displayed in a more condensed
way using cbind.
</p>

## S3 method for class 'mcpprior'
print(x, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>An <code><a href="mcpfit-class.html">mcpfit</a></code> object.</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>Currently ignored</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#mcp-0.3.0#docs#print.mcpprior

`x`	An `mcpfit` object.
`...`	Currently ignored

print.mcpprior

Description

Usage

Arguments

mcp

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