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1.2

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

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Maintained by Panagiotis PapastamoulisFirst published 2017-11-293 releasesCRAN page ↗

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 (2019) doi:10.1515/ijb-2018-0052 for a detailed presentation of the method.

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  • OK2026-03-10
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1 external dependency (excludes base and recommended)
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R >= 2.10
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Package Timeline

3 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.2Latest
    2026-02-04 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • R
    R 3.6.0 released · 2019-04-26
  • R
    R 3.5.0 released · 2018-04-23
  • 1.1
    2018-03-16 · diff ↗
  • 1.0
    2017-11-29
  • R
    R 3.4.0 released · 2017-04-21

Package metadata

First published
2017-11-29
Total releases
3 / 9 yrs
License
GPL-2 OSI
Minimum R
≥ 2.10
Bundled data
192 KB / 1 file
Download size
217 KB
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not tracked yet
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