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

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
DOI: 10.32614/CRAN.package.beast
Author: Panagiotis Papastamoulis
Maintainer: Panagiotis Papastamoulis <papapast at>
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:, r-release:, r-oldrel:
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


Please use the canonical form to link to this page.