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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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      "title": "ihw: Main function for Independent Hypothesis Weighting",
      "topics": [
        "ihw",
        "ihw.default",
        "ihw.formula"
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      "title": "An S4 class to represent the ihw output.",
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        "adj_pvalues,ihwResult-method",
        "alpha",
        "alpha,ihwResult-method",
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        "as.data.frame_ihwResult",
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      "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?",
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        "Stratified p-value histograms",
        "Further reading about appropriate covariates",
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