BreakPointLoss

Compute the Average Loss of Hidden State Changes from Expected Break Points

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. This version includes performance optimizations with vectorized MCMC operations and modern ggplot2-based visualizations with colorblind-friendly palettes.

Jong Hee Park

NetworkChange

Bayesian Package for Network Changepoint Analysis

Yunkyu Sohn

BreakPointLoss function

<dl><dt>model.list</dt>
<dd>MCMC output objects. These have to be of class
<code>mcmc</code> and have a <code>logmarglike</code> attribute. In what
follows, we let <code>M</code> denote the total number of models to be
compared.</dd>
<dt>waic</dt>
<dd>If <code>waic</code> is TRUE, <code>waic</code>(Watanabe information criterion) will be reported.</dd>
<dt>display</dt>
<dd>If <code>display</code> is TRUE, a plot of <code>ave.loss</code> will be produced.
<code>BreakPointLoss</code>. ave.loss, logmarglike, State, Tau, Tau.samp</dd></dl>

Arguments

Compute the Average Loss of Hidden State Changes from Expected Break Points — BreakPointLoss

<dl>

<dt>model.list</dt>
<dd>MCMC output objects. These have to be of class
<code>mcmc</code> and have a <code>logmarglike</code> attribute. In what
follows, we let <code>M</code> denote the total number of models to be
compared.</dd>


<dt>waic</dt>
<dd>If <code>waic</code> is TRUE, <code>waic</code>(Watanabe information criterion) will be reported.</dd>


<dt>display</dt>
<dd>If <code>display</code> is TRUE, a plot of <code>ave.loss</code> will be produced.
<code>BreakPointLoss</code>. ave.loss, logmarglike, State, Tau, Tau.samp</dd>

</dl>

BreakPointLoss: Compute the Average Loss of Hidden State Changes from Expected Break Points

Description

Usage

Value

Arguments

References