startS

Sample a starting value of hidden states

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

startS function

<dl><dt>Z</dt>
<dd>Degree-corrected network array data</dd>
<dt>Time</dt>
<dd>The length of time.</dd>
<dt>m</dt>
<dd>The number of breaks</dd>
<dt>initial.U</dt>
<dd>Initialized U matrix.</dd>
<dt>V</dt>
<dd>Initialized V matrix.</dd>
<dt>s2</dt>
<dd>Initialized error variance</dd>
<dt>R</dt>
<dd>The dimensionality of latent space</dd></dl>

Arguments

Sample a starting value of hidden states — startS

<dl>

<dt>Z</dt>
<dd>Degree-corrected network array data</dd>


<dt>Time</dt>
<dd>The length of time.</dd>


<dt>m</dt>
<dd>The number of breaks</dd>


<dt>initial.U</dt>
<dd>Initialized U matrix.</dd>


<dt>V</dt>
<dd>Initialized V matrix.</dd>


<dt>s2</dt>
<dd>Initialized error variance</dd>


<dt>R</dt>
<dd>The dimensionality of latent space</dd>

</dl>

startS: Sample a starting value of hidden states

Description

Usage

Value

Arguments