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  "Package": "LPRelevance",
  "Type": "Package",
  "Title": "Relevance-Integrated Statistical Inference Engine",
  "Version": "3.3",
  "Date": "2022-05-17",
  "Author": "Subhadeep Mukhopadhyay, Kaijun Wang",
  "Maintainer": "Kaijun Wang <kaijunwang.19@gmail.com>",
  "Description": "Provide methods to perform customized inference at\nindividual level by taking contextual covariates into account.\nThree main functions are provided in this package: (i) LASER():\nit generates specially-designed artificial relevant samples for\na given case; (ii) g2l.proc(): computes customized fdr(z|x);\nand (iii) rEB.proc(): performs empirical Bayes inference based\non LASERs. The details can be found in Mukhopadhyay, S., and\nWang, K (2021, <arXiv:2004.09588>).",
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  "NeedsCompilation": "no",
  "Packaged": {
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  "Repository": "https://kaijunwang19.r-universe.dev",
  "Date/Publication": "2022-05-18 05:20:02 UTC",
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    "author": "Kaijun Wang <kaijunwang.19@gmail.com>",
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    "name": "Kaijun Wang",
    "description": "Ph.D. in Statistics, currently working on graph-based high-dimensional nonparametric statistics. "
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    {
      "page": "LPRelevance-package",
      "title": "Relevance-Integrated Statistical Inference Engine",
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        "eLP.poly",
        "eLP.poly.predict",
        "eLP.univar",
        "LP.smooth",
        "LPcden",
        "LPregression",
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