{
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  "Type": "Package",
  "Package": "RobinCar2",
  "Title": "ROBust INference for Covariate Adjustment in Randomized Clinical\nTrials",
  "Version": "0.2.3",
  "Date": "2026-07-02",
  "Authors@R": "c(\nperson(\"Liming\", \"Li\", , \"liming.li1@astrazeneca.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0009-0008-6870-0878\")),\nperson(\"Marlena\", \"Bannick\", , \"mnorwood@uw.edu\", role=\"aut\", comment=c(ORCID=\"0000-0001-6279-5978\")),\nperson(\"Daniel\", \"Sabanes Bove\", , \"daniel@rconis.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-0176-9239\")),\nperson(\"Dong\", \"Xi\", , \"Dong.Xi1@gilead.com\", role = \"aut\"),\nperson(\"Ting\", \"Ye\", , \"tingye1@uw.edu\", role=\"aut\"),\nperson(\"Yanyao\", \"Yi\", , \"yi_yanyao@lilly.com\", role=\"aut\"),\nperson(\"Gregory\", \"Chen\", , \"xiangyi.gregory.chen@msd.com\", role = \"ctb\"),\nperson(\"Gilead Sciences, Inc.\", role = c(\"cph\", \"fnd\")),\nperson(\"F. Hoffmann-La Roche AG\", role = c(\"cph\", \"fnd\")),\nperson(\"Merck Sharp & Dohme, Inc.\", role = c(\"cph\", \"fnd\")),\nperson(\"AstraZeneca plc\", role = c(\"cph\", \"fnd\")),\nperson(\"Eli Lilly and Company\", role = c(\"cph\", \"fnd\")),\nperson(\"Novartis Pharma AG\", role = c(\"cph\", \"fnd\")),\nperson(\"The University of Washington\", role = c(\"cph\", \"fnd\"))\n)",
  "Description": "Performs robust estimation and inference when using\ncovariate adjustment and/or covariate-adaptive randomization in\nrandomized controlled trials. This package is trimmed to reduce\nthe dependencies and validated to be used across industry. See\n\"FDA's final guidance on covariate\nadjustment\"<https://www.regulations.gov/docket/FDA-2019-D-0934>,\nTsiatis (2008) <doi:10.1002/sim.3113>, Bugni et al. (2018)\n<doi:10.1080/01621459.2017.1375934>, Ye, Shao, Yi, and Zhao\n(2023)<doi:10.1080/01621459.2022.2049278>, Ye, Shao, and Yi\n(2022)<doi:10.1093/biomet/asab015>, Rosenblum and van der Laan\n(2010)<doi:10.2202/1557-4679.1138>, Wang et al.\n(2021)<doi:10.1080/01621459.2021.1981338>, Ye, Bannick, Yi, and\nShao (2023)<doi:10.1080/24754269.2023.2205802>, and Bannick,\nShao, Liu, Du, Yi, and Ye\n(2024)<doi:10.48550/arXiv.2306.10213>.",
  "License": "Apache License 2.0",
  "URL": "https://github.com/openpharma/RobinCar2/",
  "BugReports": "https://github.com/openpharma/RobinCar2/issues",
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  "Packaged": {
    "Date": "2026-07-02 21:17:11 UTC",
    "User": "root"
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  "Author": "Liming Li [aut, cre] (ORCID:\n<https://orcid.org/0009-0008-6870-0878>), Marlena Bannick [aut]\n(ORCID: <https://orcid.org/0000-0001-6279-5978>), Daniel\nSabanes Bove [aut] (ORCID:\n<https://orcid.org/0000-0002-0176-9239>), Dong Xi [aut], Ting\nYe [aut], Yanyao Yi [aut], Gregory Chen [ctb], Gilead Sciences,\nInc. [cph, fnd], F. Hoffmann-La Roche AG [cph, fnd], Merck\nSharp & Dohme, Inc. [cph, fnd], AstraZeneca plc [cph, fnd], Eli\nLilly and Company [cph, fnd], Novartis Pharma AG [cph, fnd],\nThe University of Washington [cph, fnd]",
  "Maintainer": "Liming Li <liming.li1@astrazeneca.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-07-02 06:10:09 UTC",
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    "robin_surv",
    "table",
    "treatment_effect",
    "vcovG",
    "vcovHC"
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      "title": "Example Trial Data for GLMs with Permute-Block Randomization",
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        "tbl",
        "data.frame"
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        "treatment",
        "s1",
        "s2",
        "covar",
        "y",
        "y_b"
      ],
      "rows": 600,
      "table": true,
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      "title": "Survival Example Data",
      "object": "surv_data",
      "class": [
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        "time",
        "status",
        "age",
        "sex",
        "ph.ecog",
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        "wt.loss",
        "strata",
        "ecog"
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      "title": "'RobinCar2' Package",
      "topics": [
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        "RobinCar2"
      ]
    },
    {
      "page": "confint",
      "title": "Confidence Interval",
      "topics": [
        "confint",
        "confint.prediction_cf",
        "confint.surv_effect",
        "confint.treatment_effect"
      ]
    },
    {
      "page": "find_data",
      "title": "Find Data in a Fit",
      "topics": [
        "find_data"
      ]
    },
    {
      "page": "glm_data",
      "title": "Example Trial Data for GLMs with Permute-Block Randomization",
      "topics": [
        "glm_data"
      ]
    },
    {
      "page": "contrast",
      "title": "Contrast Functions and Jacobians",
      "topics": [
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        "custom_contrast",
        "eff_jacob",
        "h_diff",
        "h_jac_diff",
        "h_jac_log_odds_ratio",
        "h_jac_log_risk_ratio",
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      "title": "Counterfactual Prediction",
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    },
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      "page": "robin_glm",
      "title": "Covariate adjusted glm model",
      "topics": [
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      "title": "Covariate adjusted lm model",
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        "robin_lm"
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      "title": "Covariate Adjusted and Stratified Survival Analysis",
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      "title": "Survival Example Data",
      "topics": [
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        "table.surv_effect"
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        "log_odds_ratio",
        "log_risk_ratio",
        "odds_ratio",
        "risk_ratio",
        "treatment_effect"
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      "title": "Heteroskedasticity-consistent covariance matrix for predictions",
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        "Data Introduction",
        "Obtain Treatment Effect for Continuous Outcomes Using the General Variance",
        "Obtain Treatment Effect for Binary Outcomes",
        "Obtain Treatment Effect for Counts",
        "Using Different Covariate-Adaptive Randomization Schema",
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