{
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  "Type": "Package",
  "Package": "tipr",
  "Title": "Tipping Point Analyses",
  "Version": "1.0.2.9000",
  "Authors@R": "c(\nperson(\"Lucy\", \"D'Agostino McGowan\", , \"lucydagostino@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-6983-2759\")),\nperson(\"Malcolm\", \"Barrett\", , \"malcolmbarrett@gmail.com\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-0299-5825\"))\n)",
  "Description": "The strength of evidence provided by epidemiological and\nobservational studies is inherently limited by the potential\nfor unmeasured confounding.  We focus on three key quantities:\nthe observed bound of the confidence interval closest to the\nnull, the relationship between an unmeasured confounder and the\noutcome, for example a plausible residual effect size for an\nunmeasured continuous or binary confounder, and the\nrelationship between an unmeasured confounder and the exposure,\nfor example a realistic mean difference or prevalence\ndifference for this hypothetical confounder between exposure\ngroups. Building on the methods put forth by Cornfield et al.\n(1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin\n(1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum\n(1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017),\nand Ding & VanderWeele (2016), we can use these quantities to\nassess how an unmeasured confounder may tip our result to\ninsignificance.",
  "License": "MIT + file LICENSE",
  "URL": "https://r-causal.github.io/tipr/, https://github.com/r-causal/tipr",
  "BugReports": "https://github.com/r-causal/tipr/issues",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.1",
  "Repository": "https://r-causal.r-universe.dev",
  "Date/Publication": "2026-10-04 16:40:54 UTC",
  "RemoteUrl": "https://github.com/r-causal/tipr",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
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    "User": "root"
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  "Author": "Lucy D'Agostino McGowan [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6983-2759>),\nMalcolm Barrett [aut] (ORCID: <https://orcid.org/0000-0003-0299-5825>)",
  "Maintainer": "Lucy D'Agostino McGowan <lucydagostino@gmail.com>",
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  "_expires": "2027-01-12T18:11:41.000Z",
  "_created": "2026-10-04T18:08:55.000Z",
  "_published": "2026-10-04T18:11:43.367Z",
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    "login": "lucymcgowan",
    "description": "Associate Professor in Statistics • Co-founder @rladies-nashville",
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  "_selfowned": true,
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  "_updates": [
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    "name": "Causal Inference in R",
    "followers": 176,
    "description": "Tools and educational material for causal inference in R"
  },
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/tipr"
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  "_pkgdown": "https://r-causal.github.io/tipr/",
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    "extra/citation.html",
    "extra/citation.json",
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    "extra/contents.json",
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    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "extra/tipr.html",
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  ],
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  "_realowner": "r-causal",
  "_cranurl": true,
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      "date": "2017-11-28"
    },
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      "date": "2020-11-16"
    },
    {
      "version": "0.3.0",
      "date": "2021-09-10"
    },
    {
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      "date": "2022-04-17"
    },
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      "date": "2022-05-06"
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      "date": "2022-08-06"
    },
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      "date": "2022-09-05"
    },
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      "date": "2024-02-06"
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  ],
  "_exports": [
    "%>%",
    "adjust_coef",
    "adjust_coef_with_binary",
    "adjust_coef_with_continuous",
    "adjust_coef_with_r2",
    "adjust_hr",
    "adjust_hr_with_binary",
    "adjust_hr_with_continuous",
    "adjust_or",
    "adjust_or_with_binary",
    "adjust_or_with_continuous",
    "adjust_rr",
    "adjust_rr_with_binary",
    "adjust_rr_with_continuous",
    "e_value",
    "observed_bias_order",
    "observed_bias_tbl",
    "observed_bias_tip",
    "observed_covariate_e_value",
    "r_value",
    "tip",
    "tip_b",
    "tip_c",
    "tip_coef",
    "tip_coef_with_continuous",
    "tip_coef_with_r2",
    "tip_hr",
    "tip_hr_with_binary",
    "tip_hr_with_continuous",
    "tip_or",
    "tip_or_with_binary",
    "tip_or_with_continuous",
    "tip_rr",
    "tip_rr_with_continuous",
    "tip_with_binary",
    "tip_with_continuous"
  ],
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      "name": "exdata_continuous",
      "title": "Example Data (Continuous Outcome)",
      "object": "exdata_continuous",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        ".unmeasured_confounder",
        "measured_confounder",
        "exposure",
        "outcome"
      ],
      "rows": 2000,
      "table": true,
      "tojson": true
    },
    {
      "name": "exdata_rr",
      "title": "Example Data (Risk Ratio)",
      "object": "exdata_rr",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        ".unmeasured_confounder",
        "measured_confounder",
        "exposure",
        "outcome"
      ],
      "rows": 2000,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "adjust_coef",
      "title": "Adjust an observed regression coefficient for a normally distributed confounder",
      "topics": [
        "adjust_coef",
        "adjust_coef_with_continuous"
      ]
    },
    {
      "page": "adjust_coef_with_binary",
      "title": "Adjust an observed coefficient from a regression model with a binary confounder",
      "topics": [
        "adjust_coef_with_binary"
      ]
    },
    {
      "page": "adjust_coef_with_r2",
      "title": "Adjust a regression coefficient using the partial R2 for an unmeasured confounder-exposure relationship and unmeasured confounder- outcome relationship",
      "topics": [
        "adjust_coef_with_r2"
      ]
    },
    {
      "page": "adjust_hr",
      "title": "Adjust an observed hazard ratio for a normally distributed confounder",
      "topics": [
        "adjust_hr",
        "adjust_hr_with_continuous"
      ]
    },
    {
      "page": "adjust_hr_with_binary",
      "title": "Adjust an observed hazard ratio with a binary confounder",
      "topics": [
        "adjust_hr_with_binary"
      ]
    },
    {
      "page": "adjust_or",
      "title": "Adjust an observed odds ratio for a normally distributed confounder",
      "topics": [
        "adjust_or",
        "adjust_or_with_continuous"
      ]
    },
    {
      "page": "adjust_or_with_binary",
      "title": "Adjust an observed odds ratio with a binary confounder",
      "topics": [
        "adjust_or_with_binary"
      ]
    },
    {
      "page": "adjust_rr",
      "title": "Adjust an observed risk ratio for a normally distributed confounder",
      "topics": [
        "adjust_rr",
        "adjust_rr_with_continuous"
      ]
    },
    {
      "page": "adjust_rr_with_binary",
      "title": "Adjust an observed risk ratio with a binary confounder",
      "topics": [
        "adjust_rr_with_binary"
      ]
    },
    {
      "page": "e_value",
      "title": "Calculate an E-value",
      "topics": [
        "e_value"
      ]
    },
    {
      "page": "exdata_continuous",
      "title": "Example Data (Continuous Outcome)",
      "topics": [
        "exdata_continuous"
      ]
    },
    {
      "page": "exdata_rr",
      "title": "Example Data (Risk Ratio)",
      "topics": [
        "exdata_rr"
      ]
    },
    {
      "page": "observed_bias_order",
      "title": "Order observed bias data frame for plotting",
      "topics": [
        "observed_bias_order"
      ]
    },
    {
      "page": "observed_bias_tbl",
      "title": "Create a data frame to assist with creating an observed bias plot",
      "topics": [
        "observed_bias_tbl"
      ]
    },
    {
      "page": "observed_bias_tip",
      "title": "Create a data frame to combine with an observed bias data frame demonstrating a hypothetical unmeasured confounder",
      "topics": [
        "observed_bias_tip"
      ]
    },
    {
      "page": "observed_covariate_e_value",
      "title": "Calculate the Observed Covariate E-value",
      "topics": [
        "observed_covariate_e_value"
      ]
    },
    {
      "page": "r_value",
      "title": "Robustness value",
      "topics": [
        "r_value"
      ]
    },
    {
      "page": "tip",
      "title": "Tip a result with a normally distributed confounder.",
      "topics": [
        "tip",
        "tip_c",
        "tip_with_continuous"
      ]
    },
    {
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        "tip_coef_with_continuous"
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        "tip_hr_with_continuous"
      ]
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      ]
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      "title": "Tip a result with a binary confounder.",
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