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  "Type": "Package",
  "Title": "Derive Polygenic Risk Score Based on Emprical Bayes Theory",
  "Version": "2.1.0",
  "Author": "Shuang Song [aut, cre], Wei Jiang [aut], Lin Hou [aut] and\nHongyu Zhao [aut]",
  "Maintainer": "Shuang Song <song-s19@mails.tsinghua.edu.cn>",
  "Description": "EB-PRS is a novel method that leverages information for\neffect sizes across all the markers to improve the prediction\naccuracy.  No parameter tuning is needed in the method, and no\nexternal information is needed. This R-package provides the\ncalculation of polygenic risk scores from the given training\nsummary statistics and testing data. We can use EB-PRS to\nextract main information, estimate Empirical Bayes parameters,\nderive polygenic risk scores for each individual in testing\ndata, and evaluate the PRS according to AUC and predictive r2.\nSee Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a\ndetailed presentation of the method.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-16 08:01:36 UTC",
    "User": "root"
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  "Repository": "https://shuangsong0110.r-universe.dev",
  "Date/Publication": "2020-08-26 04:40:08 UTC",
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  "_stars": 2,
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    "type": "user",
    "name": "Shuang Song",
    "description": "Postdoc@Harvard; Alumni@THU (Ph.D. & B.S.); Alumni@Oxford (joint Ph.D.)"
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    "extra/citation.html",
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    "extra/citation.txt",
    "extra/contents.json",
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    "EBPRSpackage",
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        "data.frame"
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        "A1",
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        "P",
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        "N"
      ],
      "rows": 100,
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    {
      "page": "EBPRS",
      "title": "Main function",
      "topics": [
        "EBPRS"
      ]
    },
    {
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      "title": "Description of the package",
      "topics": [
        "EBPRSpackage"
      ]
    },
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      "title": "Read plink bfiles to R and reformat",
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      ]
    },
    {
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      "title": "Example data for training set",
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    },
    {
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      "title": "Validate the performance of EBPRS",
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      ]
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  ],
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    "bitops",
    "caTools",
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