Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noisecorrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of changepoints. The package infers the joint posterior distribution of the number and position of changepoints as well as the unknown mean parameters per timeseries by MCMC sampling. Apriori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of changepoints, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of changepoints gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2017)
Package details 


Author  Panagiotis Papastamoulis 
Date of publication  20180316 07:42:36 UTC 
Maintainer  Panagiotis Papastamoulis <[email protected]> 
License  GPL2 
Version  1.1 
Package repository  View on CRAN 
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