{
  "abstract": "Stein's method constructs computable discrepancies between a target distribution and a candidate distribution without requiring the target distribution's normalizing constant. These discrepancies support goodness-of-fit tests for model assessment as well as sampling tools for empirical approximation. The R package steinsampling provides the first unified R workflow for applying score-based Stein methods to kernel goodness-of-fit testing of independent or serially dependent observations, point transport, greedy point construction, and sample compression. High-level functions carry out each task in a single call, while the kernel, calibration, optimization, and transition components are provided separately so that users can replace any one of them. A single score and kernel setup can therefore be reused across sampling and testing, making these methods easier to reproduce, compare, and extend.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.26450",
  "author_total": 2,
  "authors": [
    {
      "name": "Junhao Gao"
    },
    {
      "name": "Ery Arias-Castro"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a7nqj18s7r9ft.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.26450",
  "fulltext_url": null,
  "id": "a7nqj18s7r9ft",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.26450",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.CO"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Markov Chains and Monte Carlo Methods"
    ]
  ],
  "openalex": "W7204578287",
  "pdf_url": "https://arxiv.org/pdf/2608.26450",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a7nqj18s7r9ft.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:26Z",
      "native_id": "2608.26450",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.26450"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R",
  "title_text": "Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:26Z",
  "url": "https://research.draconitic.com/paper/a7nqj18s7r9ft",
  "venue": "arXiv",
  "versions": []
}