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) <arXiv:1709.06111> for a detailed presentation of the method.

Version: | 1.1 |

Depends: | R (≥ 2.10) |

Imports: | RColorBrewer |

Published: | 2018-03-16 |

Author: | Panagiotis Papastamoulis |

Maintainer: | Panagiotis Papastamoulis <papapast at yahoo.gr> |

License: | GPL-2 |

NeedsCompilation: | no |

Citation: | beast citation info |

CRAN checks: | beast results |

Reference manual: | beast.pdf |

Package source: | beast_1.1.tar.gz |

Windows binaries: | r-devel: beast_1.1.zip, r-release: beast_1.1.zip, r-oldrel: beast_1.1.zip |

macOS binaries: | r-release (arm64): beast_1.1.tgz, r-release (x86_64): beast_1.1.tgz, r-oldrel: beast_1.1.tgz |

Old sources: | beast archive |

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