{
  "_id": "6a1f07a5b401979e7341bf2a",
  "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": "2024-02-06 20:55:06 UTC",
  "RemoteUrl": "https://github.com/r-causal/tipr",
  "RemoteRef": "HEAD",
  "RemoteSha": "ba218310fe20e23976e63d20608b791a81ea913d",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-18 06:41:25 UTC",
    "User": "root"
  },
  "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>",
  "MD5sum": "1385db001ae2df33212efda65228bafd",
  "_user": "r-causal",
  "_type": "src",
  "_file": "tipr_1.0.2.9000.tar.gz",
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  "_created": "2026-05-18T06:41:25.000Z",
  "_published": "2026-06-02T16:41:09.358Z",
  "_distro": "noble",
  "_jobs": [
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  "_upstream": "https://github.com/r-causal/tipr",
  "_commit": {
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    "author": "Lucy D'Agostino McGowan <mcgowald@wfu.edu>",
    "committer": "Lucy D'Agostino McGowan <mcgowald@wfu.edu>",
    "message": "Increment version number to 1.0.2.9000\n",
    "time": 1707252906
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  "_maintainer": {
    "name": "Lucy DAgostino McGowan",
    "email": "lucydagostino@gmail.com",
    "login": "lucymcgowan",
    "description": "Assistant Professor in Statistics • Co-founder @rladies-nashville",
    "uuid": 8431897,
    "orcid": "0000-0002-6983-2759"
  },
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  "_dependencies": [
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      "version": ">= 2.10",
      "role": "Depends"
    },
    {
      "package": "cli",
      "version": ">= 3.4.1",
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    },
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      "package": "glue",
      "role": "Imports"
    },
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      "package": "purrr",
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    },
    {
      "package": "rlang",
      "version": ">= 1.0.6",
      "role": "Imports"
    },
    {
      "package": "sensemakr",
      "role": "Imports"
    },
    {
      "package": "tibble",
      "role": "Imports"
    },
    {
      "package": "broom",
      "role": "Suggests"
    },
    {
      "package": "dplyr",
      "role": "Suggests"
    },
    {
      "package": "MASS",
      "role": "Suggests"
    },
    {
      "package": "testthat",
      "role": "Suggests"
    }
  ],
  "_owner": "r-causal",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [],
  "_tags": [],
  "_stars": 42,
  "_contributors": [
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      "count": 130,
      "uuid": 8431897
    },
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  "_userbio": {
    "uuid": 129535100,
    "type": "organization",
    "name": "Causal Inference in R",
    "description": "Tools and educational material for causal inference in R"
  },
  "_downloads": {
    "count": 343,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/tipr"
  },
  "_devurl": "https://github.com/r-causal/tipr",
  "_pkgdown": "https://r-causal.github.io/tipr/",
  "_searchresults": 110,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "extra/tipr.html",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/r-causal/tipr",
  "_realowner": "r-causal",
  "_cranurl": true,
  "_releases": [
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      "version": "0.1.1",
      "date": "2017-11-28"
    },
    {
      "version": "0.2.0",
      "date": "2020-11-16"
    },
    {
      "version": "0.3.0",
      "date": "2021-09-10"
    },
    {
      "version": "0.4.0",
      "date": "2022-04-17"
    },
    {
      "version": "0.4.1",
      "date": "2022-05-06"
    },
    {
      "version": "1.0.0",
      "date": "2022-08-06"
    },
    {
      "version": "1.0.1",
      "date": "2022-09-05"
    },
    {
      "version": "1.0.2",
      "date": "2024-02-06"
    }
  ],
  "_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"
  ],
  "_datasets": [
    {
      "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"
      ]
    },
    {
      "page": "tip_coef",
      "title": "Tip a linear model coefficient with a continuous confounder.",
      "topics": [
        "tip_coef",
        "tip_coef_with_continuous"
      ]
    },
    {
      "page": "tip_coef_with_r2",
      "title": "Tip a regression coefficient using the partial R2 for an unmeasured confounder-exposure relationship and unmeasured confounder- outcome relationship",
      "topics": [
        "tip_coef_with_r2"
      ]
    },
    {
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      "title": "Tip an observed hazard ratio with a normally distributed confounder.",
      "topics": [
        "tip_hr",
        "tip_hr_with_continuous"
      ]
    },
    {
      "page": "tip_hr_with_binary",
      "title": "Tip an observed hazard ratio with a binary confounder.",
      "topics": [
        "tip_hr_with_binary"
      ]
    },
    {
      "page": "tip_or",
      "title": "Tip an observed odds ratio with a normally distributed confounder.",
      "topics": [
        "tip_or",
        "tip_or_with_continuous"
      ]
    },
    {
      "page": "tip_or_with_binary",
      "title": "Tip an observed odds ratio with a binary confounder.",
      "topics": [
        "tip_or_with_binary"
      ]
    },
    {
      "page": "tip_rr",
      "title": "Tip an observed risk ratio with a normally distributed confounder.",
      "topics": [
        "tip_rr",
        "tip_rr_with_continuous"
      ]
    },
    {
      "page": "tip_rr_with_binary",
      "title": "Tip an observed risk ratio with a binary confounder.",
      "topics": [
        "tip_rr_with_binary"
      ]
    },
    {
      "page": "tip_with_binary",
      "title": "Tip a result with a binary confounder.",
      "topics": [
        "tip_b",
        "tip_with_binary"
      ]
    }
  ],
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