beast: Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series
Version 1.1

Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted 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 change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points 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) for a detailed presentation of the method.

Package details

AuthorPanagiotis Papastamoulis
Date of publication2018-03-16 07:42:36 UTC
MaintainerPanagiotis Papastamoulis <[email protected]>
Package repositoryView on CRAN
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beast documentation built on March 18, 2018, 2:23 p.m.