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  "Title": "Independent Hypothesis Weighting",
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  "Description": "Independent hypothesis weighting (IHW) is a multiple\ntesting procedure that increases power compared to the method\nof Benjamini and Hochberg by assigning data-driven weights to\neach hypothesis. The input to IHW is a two-column table of\np-values and covariates. The covariate can be any\ncontinuous-valued or categorical variable that is thought to be\ninformative on the statistical properties of each hypothesis\ntest, while it is independent of the p-value under the null\nhypothesis.",
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  "VignetteBuilder": "knitr",
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  "Repository": "https://bioc-release.r-universe.dev",
  "Date/Publication": "2026-04-28 12:42:33 UTC",
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  "Maintainer": "Nikos Ignatiadis <nikos.ignatiadis01@gmail.com>",
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    "covariate_type",
    "covariates",
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    "groups_by_filter",
    "groups_factor",
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    "nbins",
    "nfolds",
    "nrow",
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    "show",
    "thresholds",
    "weighted_pvalues",
    "weights"
  ],
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    {
      "page": "get_bh_threshold",
      "title": "Data-driven threshold of Benjamini Hochberg Procedure",
      "topics": [
        "get_bh_threshold"
      ]
    },
    {
      "page": "groups_by_filter",
      "title": "Stratify hypotheses based on increasing value of the covariate",
      "topics": [
        "groups_by_filter"
      ]
    },
    {
      "page": "ihw.default",
      "title": "ihw: Main function for Independent Hypothesis Weighting",
      "topics": [
        "ihw",
        "ihw.default",
        "ihw.formula"
      ]
    },
    {
      "page": "ihw.DESeqResults",
      "title": "ihw.DESeqResults: IHW method dispatching on DESeqResults objects",
      "topics": [
        "ihw.DESeqResults"
      ]
    },
    {
      "page": "ihwResult-class",
      "title": "An S4 class to represent the ihw output.",
      "topics": [
        "adj_pvalues",
        "adj_pvalues,ihwResult-method",
        "alpha",
        "alpha,ihwResult-method",
        "as.data.frame,ihwResult-method",
        "as.data.frame_ihwResult",
        "covariates",
        "covariates,ihwResult-method",
        "covariate_type",
        "covariate_type,ihwResult-method",
        "groups_factor",
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        "nfolds",
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        "thresholds",
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        "weighted_pvalues",
        "weighted_pvalues,ihwResult-method",
        "weights,ihwResult-method"
      ]
    },
    {
      "page": "plot-ihwResult-method",
      "title": "Plot functions for IHW",
      "topics": [
        "plot,ihwResult-method"
      ]
    }
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      "source": "introduction_to_ihw.Rmd",
      "filename": "introduction_to_ihw.html",
      "title": "Introduction to Independent Hypothesis Weighting with the IHW Package",
      "author": "Nikos Ignatiadis, Wolfgang Huber",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "An example: RNA-Seq differential expression",
        "FDR control",
        "Diagnostic plots",
        "Estimated weights",
        "Decision boundary",
        "Raw versus adjusted p-values",
        "FWER control with IHW",
        "Other data types, and how to choose the covariate",
        "Criteria for choosing a covariate",
        "Examples",
        "Why are the different covariate criteria necessary?",
        "Diagnostic plots for the covariate",
        "Scatter plots",
        "Stratified p-value histograms",
        "Further reading about appropriate covariates",
        "Advanced usage: Working with incomplete p-value lists",
        "References"
      ],
      "created": "2016-01-31 16:28:33",
      "modified": "2016-10-03 22:01:49",
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