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  "Title": "Solve the Empirical Bayes Normal Means Problem",
  "Authors@R": "c(person(\"Jason\", \"Willwerscheid\", role = \"aut\"),\nperson(\"Matthew\", \"Stephens\", role = \"aut\"),\nperson(\"Peter\", \"Carbonetto\", role = c(\"aut\", \"cre\"),\nemail = \"peter.carbonetto@gmail.com\"),\nperson(\"Andrew\", \"Goldstein\", role = \"ctb\"),\nperson(\"Yusha\", \"Liu\", role = \"ctb\"))",
  "URL": "https://github.com/stephenslab/ebnm",
  "BugReports": "https://github.com/stephenslab/ebnm/issues",
  "Description": "Provides simple, fast, and stable functions to fit the\nnormal means model using empirical Bayes. For available models\nand details, see function ebnm(). Our JSS article,\nWillwerscheid, Carbonetto, and Stephens (2025)\n<doi:10.18637/jss.v114.i03>, provides a detailed introduction\nto the package.",
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    "ebnm_generalized_binary",
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    "ebnm_normal_scale_mixture",
    "ebnm_npmle",
    "ebnm_output_all",
    "ebnm_output_default",
    "ebnm_point_exponential",
    "ebnm_point_laplace",
    "ebnm_point_mass",
    "ebnm_point_normal",
    "ebnm_scale_normalmix",
    "ebnm_scale_npmle",
    "ebnm_scale_unimix",
    "ebnm_unimodal",
    "ebnm_unimodal_nonnegative",
    "ebnm_unimodal_nonpositive",
    "ebnm_unimodal_symmetric",
    "gammamix",
    "horseshoe",
    "laplacemix"
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        "Name",
        "Team",
        "PA",
        "x",
        "s"
      ],
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      "table": true,
      "tojson": true
    }
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      "title": "Extract posterior means from a fitted EBNM model",
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      ]
    },
    {
      "page": "confint.ebnm",
      "title": "Obtain credible intervals using a fitted EBNM model",
      "topics": [
        "confint.ebnm"
      ]
    },
    {
      "page": "ebnm",
      "title": "Solve the EBNM problem",
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        "ebnm",
        "ebnm_output_all",
        "ebnm_output_default"
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    },
    {
      "page": "ebnm_add_sampler",
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    },
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      "topics": [
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      ]
    },
    {
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    },
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      ]
    },
    {
      "page": "ebnm_flat",
      "title": "Solve the EBNM problem using a flat prior",
      "topics": [
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      ]
    },
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      "page": "ebnm_group",
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      ]
    },
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      "title": "Solve the EBNM problem using scale mixtures of normals",
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      ]
    },
    {
      "page": "ebnm_npmle",
      "title": "Solve the EBNM problem using the family of all distributions",
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        "ebnm_npmle"
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    },
    {
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      "topics": [
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    },
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      "title": "Solve the EBNM problem using a point mass prior",
      "topics": [
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    },
    {
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      "title": "Solve the EBNM problem using point-normal priors",
      "topics": [
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    },
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      "page": "ebnm_scale_normalmix",
      "title": "Set scale parameter for scale mixtures of normals",
      "topics": [
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    },
    {
      "page": "ebnm_scale_npmle",
      "title": "Set scale parameter for NPMLE and deconvolveR prior family",
      "topics": [
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    },
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    },
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      "topics": [
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