Package for a Bayesian multiple changepoint detection method to detect mean shifts in AR(1) time series. It accomodates metadta (if available) by letting metadata times have higher prior probabilities to be changepoints. The changepoint configuration with the highest posterior probability is the optimal model. Metropolis-Hastings algorithm is utilized for quick stochastic search of a potentially huge model space. This method is ideal for annual series, since it allows a linear trend component, but not yet monthly cycles.
The most important functions of this package:
find the optimal changepoint configuration,
given a configuration, estimate model parameters.
Maintainer: Yingbo Li <email@example.com>
Li, Y. and Lund, R. (2014) Bayesian Mulitple Changepoint Detection Using Metadata. (submitted)
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