{
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  "Title": "Bayes Factor Functions",
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  "Authors@R": "c(person(given = \"Rachael\",\nfamily = \"Shudde\",\nrole = c(\"aut\", \"cre\"),\nemail = \"rachael.shudde@gmail.com\"),\nperson(given = \"František\",\nfamily = \"Bartoš\",\nrole = c(\"aut\"),\nemail = \"f.bartos96@gmail.com\"),\nperson(given = \"Saptati\",\nfamily = \"Datta\",\nrole = c(\"aut\"),\nemail = \"saptati@tamu.edu\"),\nperson(given = \"Riana\",\nfamily = \"Guha\",\nrole = c(\"aut\"),\nemail = \"riana.guha@tamu.edu\"),\nperson(given = \"Sandy\", family = \"Pramanik\",\nrole = c(\"aut\"),\nemail = \"sandy.pramanik@gmail.com\"),\nperson(given = \"Valen\",\nfamily = \"Johnson\",\nrole = c(\"aut\"),\nemail = \"vejohnson@exchange.tamu.edu\"))",
  "Author": "Rachael Shudde [aut, cre], František Bartoš [aut], Saptati\nDatta [aut], Riana Guha [aut], Sandy Pramanik [aut], Valen\nJohnson [aut]",
  "Maintainer": "Rachael Shudde <rachael.shudde@gmail.com>",
  "Description": "Bayes factors represent the ratio of probabilities\nassigned to data by competing scientific hypotheses. However,\none drawback of Bayes factors is their dependence on prior\nspecifications that define null and alternative hypotheses.\nAdditionally, there are challenges in their computation. To\naddress these issues, we define Bayes factor functions (BFFs)\ndirectly from common test statistics. BFFs express Bayes\nfactors as a function of the prior densities used to define the\nalternative hypotheses. These prior densities are centered on\nstandardized effects, which serve as indices for the BFF.\nTherefore, BFFs offer a summary of evidence in favor of\nalternative hypotheses that correspond to a range of\nscientifically interesting effect sizes. Such summaries remove\nthe need for arbitrary thresholds to determine \"statistical\nsignificance.\" BFFs are available in closed form and can be\neasily computed from z, t, chi-squared, and F statistics. They\ndepend on hyperparameters \"r\" and \"tau^2\", which determine the\nshape and scale of the prior distributions defining the\nalternative hypotheses. Plots of BFFs versus effect size\nprovide informative summaries of hypothesis tests that can be\neasily aggregated across studies.",
  "License": "GPL (>= 2)",
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  "Date/Publication": "2025-05-20 01:27:11 UTC",
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  "NeedsCompilation": "no",
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    "User": "root"
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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    "chi2_test_BFF",
    "dnlnm",
    "f_test_BFF",
    "is.BFF",
    "posterior_plot",
    "regression_test_BFF",
    "t_test_BFF",
    "z_test_BFF"
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    {
      "page": "BFF.object",
      "title": "BFF Object",
      "topics": [
        "BFF.object"
      ]
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      "title": "chi2_test_BFF",
      "topics": [
        "chi2_test_BFF"
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        "f_test_BFF"
      ]
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    {
      "page": "is.BFF",
      "title": "Check whether x is a BFF object",
      "topics": [
        "is.BFF"
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      "page": "nlnm",
      "title": "Non-local Normal Moment Distribution",
      "topics": [
        "dnlnm",
        "nlnm"
      ]
    },
    {
      "page": "plot.BFF",
      "title": "Plot Bayes Factor Function",
      "topics": [
        "plot.BFF"
      ]
    },
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      "title": "Plot Prior and Posterior Distribution",
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      ]
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      "title": "Summarize BFF object",
      "topics": [
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