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  "Title": "Bayesian Estimation of Change-Points in the Slope of\nMultivariate Time-Series",
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  "Date": "2026-02-04",
  "Authors@R": "c(person(given = \"Panagiotis\",\nfamily = \"Papastamoulis\",\nemail = \"papapast@yahoo.gr\",\nrole = c( \"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9468-7613\")))",
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  "Description": "Assume that a temporal process is composed of contiguous\nsegments with differing slopes and replicated noise-corrupted\ntime series measurements are observed. The unknown mean of the\ndata generating process is modelled as a piecewise linear\nfunction of time with an unknown number of change-points. The\npackage infers the joint posterior distribution of the number\nand position of change-points as well as the unknown mean\nparameters per time-series by MCMC sampling. A-priori, the\nproposed model uses an overfitting number of mean parameters\nbut, conditionally on a set of change-points, only a subset of\nthem influences the likelihood. An exponentially decreasing\nprior distribution on the number of change-points gives rise to\na posterior distribution concentrating on sparse\nrepresentations of the underlying sequence, but also available\nis the Poisson distribution. See Papastamoulis et al (2019)\n<doi:10.1515/ijb-2018-0052> for a detailed presentation of the\nmethod.",
  "License": "GPL-2",
  "NeedsCompilation": "no",
  "Packaged": {
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    "User": "root"
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  "Author": "Panagiotis Papastamoulis [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-9468-7613>)",
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      "title": "Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series",
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      "topics": [
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    },
    {
      "page": "computeEmpiricalPriorParameters",
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      "topics": [
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    },
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      "page": "computePosteriorParameters",
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      "page": "computePosteriorParametersFree",
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      "topics": [
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      "title": "Move 2",
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