{
  "abstract": "dsp provides a unified state-space framework for modeling nonstationary time series using the Dynamic Shrinkage Process (DSP), a class of global-local shrinkage priors. DSP adaptively regularizes time-varying parameters to shrink noise while preserving abrupt changes and other local structure. The package supports Gaussian and count-data smoothing, changepoint detection, time-varying parameter regression, and volatility estimation, with posterior predictive draws available for missing-value imputation and forecasting. Nonstationary time series often contain periods of relative stability punctuated by abrupt changes in mean or variance. dsp offers a common interface for model specification, posterior computation, summarization, and visualization across models that were previously developed separately in the literature. Core functionality is implemented in C++ via Rcpp for computational efficiency. We describe the underlying models, package design, and the main functions for fitting, summarizing, and plotting posterior results.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.07096",
  "author_total": 5,
  "authors": [
    {
      "name": "Jason B. Cho"
    },
    {
      "name": "Daniel R. Kowal"
    },
    {
      "name": "Haoxuan Wu"
    },
    {
      "name": "David S. Matteson"
    },
    {
      "name": "Toryn L. J. Schafer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a14k5c28jgv29.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.07096",
  "fulltext_url": null,
  "id": "a14k5c28jgv29",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.07096",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.CO"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.07096",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a14k5c28jgv29.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:40Z",
      "native_id": "2611.07096",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.07096"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "dsp: An R Package for Bayesian Time Series Modeling with the Dynamic Shrinkage Process",
  "title_text": "dsp: An R Package for Bayesian Time Series Modeling with the Dynamic Shrinkage Process",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:51Z",
  "url": "https://research.draconitic.com/paper/a14k5c28jgv29",
  "venue": "arXiv",
  "versions": []
}