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  "Title": "Sensitivity Analysis for Observational Studies",
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  "Date": "2022-04-23",
  "Author": "Greg Snow <538280@gmail.com>",
  "Maintainer": "Greg Snow <538280@gmail.com>",
  "Description": "Observational studies are limited in that there could be\nan unmeasured variable related to both the response variable\nand the primary predictor.  If this unmeasured variable were\nincluded in the analysis it would change the relationship\n(possibly changing the conclusions).  Sensitivity analysis is a\nway to see how much of a relationship needs to exist with the\nunmeasured variable before the conclusions change.  This\npackage provides tools for doing a sensitivity analysis for\nregression (linear, logistic, and cox) style models.",
  "License": "GPL-2",
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    "User": "root"
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  "Date/Publication": "2022-04-23 22:50:07 UTC",
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  "_releases": [
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      "date": "2008-02-21"
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    },
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      "date": "2011-12-05"
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      "date": "2013-01-18"
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    "obsSensCCN",
    "obsSensCNN",
    "obsSensNCC",
    "obsSensNCN",
    "obsSensNNN",
    "obsSensSCC",
    "obsSensSCN",
    "obsSensSNN",
    "print.obsSens",
    "print.summary.obsSens",
    "summary.obsSens"
  ],
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    {
      "page": "obsSens-package",
      "title": "Perform sensitivity analysis on observational studies to explore possible effects of unmeasured (lurking) variables.",
      "topics": [
        "obsSens-package",
        "obsSens"
      ]
    },
    {
      "page": "obsSensCCC",
      "title": "Compute Sensitivity analysis for Observational Studies",
      "topics": [
        "obsSensCCC",
        "obsSensCCN",
        "obsSensCNN",
        "obsSensNCC",
        "obsSensNCN",
        "obsSensNNN",
        "obsSensSCC",
        "obsSensSCN",
        "obsSensSNN"
      ]
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    {
      "page": "print.obsSens",
      "title": "Print and Summarize ObsSens objects.",
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        "print.summary.obsSens",
        "summary.obsSens"
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