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  "Title": "Hierarchical Inference",
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  "Authors@R": "c(person(\"Claude\", \"Renaux\", role = c(\"aut\", \"cre\"), \nemail = \"renaux@stat.math.ethz.ch\"),\nperson(\"Laura\", \"Buzdugan\", role = c(\"aut\")),\nperson(\"Markus\", \"Kalisch\", role = c(\"aut\")),\nperson(\"Peter\", \"Bühlmann\", role = c(\"aut\")))",
  "Description": "Tools to perform hierarchical inference for one or\nmultiple studies / data sets based on high-dimensional\nmultivariate (generalised) linear models. A possible\napplication is to perform hierarchical inference for GWA\nstudies to find significant groups or single SNPs (if the\nsignal is strong) in a data-driven and automated procedure. The\nmethod is based on an efficient hierarchical multiple testing\ncorrection and controls the FWER. The functions can easily be\nrun in parallel.",
  "License": "GPL-3 | file LICENSE",
  "Encoding": "UTF-8",
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  "biocViews": "Clustering, GenomeWideAssociation, LinkageDisequilibrium,\nRegression, SNP",
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  "Date/Publication": "2026-04-28 12:48:26 UTC",
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    "User": "root"
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  "Author": "Claude Renaux [aut, cre],\nLaura Buzdugan [aut],\nMarkus Kalisch [aut],\nPeter Bühlmann [aut]",
  "Maintainer": "Claude Renaux <renaux@stat.math.ethz.ch>",
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    "name": "Bioconductor",
    "description": "Software for the analysis and comprehension of high-throughput genomic data"
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      "page": "cluster_position",
      "title": "Build Hierarchical Tree based on Position",
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    },
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      "title": "Build Hierarchical Tree based on Hierarchical Clustering",
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      "page": "hierinf",
      "title": "hierinf: Hierarchical Inference",
      "topics": [
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        "hierinf"
      ]
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      ]
    },
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      "page": "print.hierT",
      "title": "Print Object of Class 'hierT'",
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      ]
    },
    {
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      "title": "Simulated GWAS data set",
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      ]
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