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

See how this data was simulated
<a href="https://github.com/lindeloev/mcp/blob/master/data-raw/ex_binomial.R">here</a>
using <code>fit$simulate()</code>, including which parameters were used. See an analysis
<a href="https://lindeloev.github.io/mcp/">here</a>.

A data frame with 100 rows and 3 variables:

<dl>
<dt>x</dt>
<dd>
The x-axis.
</dd>
<dt>y</dt>
<dd>
The y-axis.
</dd>
<dt>N</dt>
<dd>
The number of trials for for <code>y</code>.
</dd>
</dl>

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

ex_binomial

Description

Usage

Format

mcp

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