Bayesian Package for Network Changepoint Analysis
Detect a break number using different metrics
Compute the Average Loss of Hidden State Changes from Expected Break P...
Plot of latent node cluster
Plot of network by hidden regime
K-mean clustering of latent node positions
Build a synthetic block-structured temporal data with breaks
Compare Log Marginal Likelihood
Printing multiple ggplots in oone file
Changepoint analysis of a degree-corrected multilinear tensor model
Changepoint analysis of a degree-corrected multilinear tensor model wi...
Degree-corrected multilinear tensor model
Contour plot of latent node positions
Plot of network array data
Plot of latent node positions
Plot of layer-specific network generation rules.
Sample a starting value of hidden states
Starting values of U and V
Hidden State Sampler with precision
Hidden State Sampler
Update time-constant regression parameters
Update regime-changing regression parameters
Update transition matrix
Update latent states
Update regime-specific variance
Update time-constant latent node positions
Regime-specific latent node positions
Update layer specific network generation rules
Update V from a change-point network process
Compare WAIC
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.