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  "Title": "Bayesian Multilevel Single Case Models using 'Stan'",
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  "Authors@R": "person(\"Michele\", \"Scandola\", email = \"michele.scandola@univr.it\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-0853-8975\"))",
  "Description": "Analyse single case analyses against a control group. Its\npurpose is to provide a flexible, with good power and low first\ntype error approach that can manage at the same time controls'\nand patient's data. The use of Bayesian statistics allows to\ntest both the alternative and null hypothesis. Scandola, M., &\nRomano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq>\nScandola, M., & Romano, D. (2021).\n<doi:10.1016/j.neuropsychologia.2021.107834>.",
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  "Date/Publication": "2022-09-04 12:39:37 UTC",
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  "Author": "Michele Scandola [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-0853-8975>)",
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        "Body.District",
        "Congruency",
        "Side",
        "ID"
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    },
    {
      "name": "data.pt",
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      "object": "BSE",
      "class": [
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      ],
      "fields": [
        "RT",
        "Body.District",
        "Congruency",
        "Side"
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      "page": "BMSC",
      "title": "Fit Bayesian Multilevel Single Case models",
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        "BMSC"
      ]
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        "print.loo_BMSC",
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      "title": "bmscstan wrapper for model comparison.",
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        "print.pareto_k_ids_BMSC",
        "print.pareto_k_influence_values_BMSC",
        "print.pareto_k_table_BMSC",
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        "print.psis_n_eff_values_BMSC"
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      "title": "Bayesian Multilevel Single Case models using 'Stan'",
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      "title": "Fitting Bayesian Multilevel Single Case models using bmscstan",
      "author": "Michele Scandola",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Example on real data",
        "Explore the data",
        "Deciding the contrasts and the random effects part",
        "Fitting the BMSC model",
        "The summary.BMSC output",
        "Understanding the summary.BMSC output",
        "Checking the diagnostic indexes",
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