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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) <doi:10.48550/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-oldrel (arm64): beast_1.1.tgz, r-release (x86_64): beast_1.1.tgz, r-oldrel (x86_64): beast_1.1.tgz |
| Old sources: | beast archive |
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