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...
Combine regime-specific V matrices efficiently
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 one file (DEPRECATED)
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.
NetworkChange Continuous Color Scale
NetworkChange Discrete Color Scale
NetworkChange Discrete Fill Scale
Sample a starting value of hidden states
Starting values of U and V
NetworkChange ggplot2 Theme
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. This version includes performance optimizations with vectorized MCMC operations and modern ggplot2-based visualizations with colorblind-friendly palettes.