{
  "_id": "6ad627665a02066177b9af32",
  "Type": "Package",
  "Package": "RobinCar2",
  "Title": "ROBust INference for Covariate Adjustment in Randomized Clinical\nTrials",
  "Version": "0.2.4.9000",
  "Date": "2026-10-12",
  "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",
  "VignetteBuilder": "knitr",
  "Config/testthat/edition": "3",
  "Encoding": "UTF-8",
  "Language": "en-US",
  "LazyData": "true",
  "Roxygen": "list(markdown = TRUE)",
  "Config/roxygen2/version": "8.1.0",
  "Repository": "https://openpharma.r-universe.dev",
  "Date/Publication": "2026-10-19 12:53:36 UTC",
  "RemoteUrl": "https://github.com/openpharma/RobinCar2",
  "RemoteRef": "HEAD",
  "RemoteSha": "da87b40f1bbbb54b360a52cf586187323668c626",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-19 13:54:38 UTC",
    "User": "root"
  },
  "Author": "Liming Li [aut, cre] (ORCID: <https://orcid.org/0009-0008-6870-0878>),\nMarlena Bannick [aut] (ORCID: <https://orcid.org/0000-0001-6279-5978>),\nDaniel Sabanes Bove [aut] (ORCID:\n<https://orcid.org/0000-0002-0176-9239>),\nDong Xi [aut],\nTing Ye [aut],\nYanyao Yi [aut],\nGregory Chen [ctb],\nGilead Sciences, Inc. [cph, fnd],\nF. Hoffmann-La Roche AG [cph, fnd],\nMerck Sharp & Dohme, Inc. [cph, fnd],\nAstraZeneca plc [cph, fnd],\nEli Lilly and Company [cph, fnd],\nNovartis Pharma AG [cph, fnd],\nThe University of Washington [cph, fnd]",
  "Maintainer": "Liming Li <liming.li1@astrazeneca.com>",
  "_user": "openpharma",
  "_type": "src",
  "_file": "RobinCar2_0.2.4.9000.tar.gz",
  "_fileid": "https://r2.ropensci.org/0aa201b58011c25db64b6579870a7f83312b0abe9419a0bd8319bef689ac1ca8",
  "_filesize": 222602,
  "_sha256": "0aa201b58011c25db64b6579870a7f83312b0abe9419a0bd8319bef689ac1ca8",
  "_expires": "2027-01-27T14:21:23.000Z",
  "_created": "2026-10-19T13:54:38.000Z",
  "_published": "2026-10-19T14:21:26.081Z",
  "_buildurl": "https://github.com/r-universe/openpharma/actions/runs/39263530508",
  "_status": "success",
  "_host": "GitHub-Actions",
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  "_commit": {
    "id": "da87b40f1bbbb54b360a52cf586187323668c626",
    "author": "Liming <36079400+clarkliming@users.noreply.github.com>",
    "committer": "GitHub <noreply@github.com>",
    "message": "117 reject offsets (#122)\n\n* 117: reject models carrying an offset\n\nModels with an offset previously returned estimates silently, and those\nestimates were wrong: the offset was dropped from the counterfactual\npredictions but retained in the residuals used for the bias correction,\nso the two were on different scales and the result did not target the\nmarginal mean or any other interpretable quantity.\n\nThe mismatch is not repaired, it is rejected. Offsets are used almost\nexclusively to express a rate per unit exposure time, and there is no\nestablished covariate-adjusted rate estimand for RobinCar2 to target:\nthe theory the package implements develops marginal means of the\nresponse and does not treat exposure time. Repairing the algebra alone\nwould ship a rate method we authored, presented as covariate adjustment\ntheory. Users who want exposure time in the model should include it as\nan ordinary covariate with a free coefficient.\n\nThe guard sits in predict_counterfactual.lm, the single implementation\nthat robin_glm, robin_lm and both treatment_effect routes pass through;\npredict_counterfactual.glm delegates to it and glm.nb inherits from glm.\nDetection is !is.null(fit$offset), which covers both supply routes,\noffset() in the formula and the offset argument, across lm/glm/glm.nb.\nThe guard is family-agnostic because both defects live in the shared\nprediction method, so lm and Poisson are affected identically.\n\nAnalysis and the full decision record, including the open questions a\nfuture rate estimand depends on, are in\ndesign/offset_rate/offset_estimand_note.md.\n\nCo-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>\n\n* 117: branch the offset rejection message on the link\n\nThe single message was wrong in two ways. It recommended giving the\noffset variable a free coefficient, which is a different model -- the\noffset's content is that the coefficient is fixed at 1, so freeing it\ndrops the proportionality to exposure and estimates a marginal mean of\nthe count, not",
    "time": 1792414416
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    "name": "Liming Li",
    "email": "liming.li1@astrazeneca.com",
    "login": "clarkliming",
    "orcid": "0009-0008-6870-0878",
    "description": "",
    "uuid": 36079400
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      "name": "v0.2.2-rc1",
      "date": "2026-01-08"
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    "followers": 267,
    "description": "Further precompetitive collaboration in life sciences"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/RobinCar2"
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  "_realowner": "openpharma",
  "_cranurl": true,
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  "_exports": [
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    "custom_contrast",
    "eff_jacob",
    "find_data",
    "h_diff",
    "h_jac_diff",
    "h_jac_log_odds_ratio",
    "h_jac_log_risk_ratio",
    "h_jac_odds_ratio",
    "h_jac_risk_ratio",
    "h_log_odds_ratio",
    "h_log_risk_ratio",
    "h_odds_ratio",
    "h_risk_ratio",
    "pairwise",
    "predict_counterfactual",
    "robin_glm",
    "robin_lm",
    "robin_surv",
    "surv_control",
    "table",
    "treatment_effect",
    "vcovG",
    "vcovHC"
  ],
  "_datasets": [
    {
      "name": "glm_data",
      "title": "Example Trial Data for GLMs with Permute-Block Randomization",
      "object": "glm_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
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        "treatment",
        "s1",
        "s2",
        "covar",
        "y",
        "y_b"
      ],
      "rows": 600,
      "table": true,
      "tojson": true
    },
    {
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      "title": "Survival Example Data",
      "object": "surv_data",
      "class": [
        "data.frame"
      ],
      "fields": [
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        "time",
        "status",
        "age",
        "sex",
        "ph.ecog",
        "ph.karno",
        "pat.karno",
        "meal.cal",
        "wt.loss",
        "strata",
        "ecog"
      ],
      "rows": 228,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "RobinCar2-package",
      "title": "'RobinCar2' Package",
      "topics": [
        "RobinCar2-package",
        "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": [
        "against_ref",
        "custom_contrast",
        "eff_jacob",
        "h_diff",
        "h_jac_diff",
        "h_jac_log_odds_ratio",
        "h_jac_log_risk_ratio",
        "h_jac_odds_ratio",
        "h_jac_risk_ratio",
        "h_log_odds_ratio",
        "h_log_risk_ratio",
        "h_odds_ratio",
        "h_risk_ratio",
        "pairwise"
      ]
    },
    {
      "page": "predict_counterfactual",
      "title": "Counterfactual Prediction",
      "topics": [
        "predict_counterfactual"
      ]
    },
    {
      "page": "robin_glm",
      "title": "Covariate adjusted glm model",
      "topics": [
        "robin_glm"
      ]
    },
    {
      "page": "robin_lm",
      "title": "Covariate adjusted lm model",
      "topics": [
        "robin_lm"
      ]
    },
    {
      "page": "robin_surv",
      "title": "Covariate Adjusted and Stratified Survival Analysis",
      "topics": [
        "robin_surv"
      ]
    },
    {
      "page": "surv_control",
      "title": "Control Survival Analysis Root Finding",
      "topics": [
        "surv_control"
      ]
    },
    {
      "page": "surv_data",
      "title": "Survival Example Data",
      "topics": [
        "surv_data"
      ]
    },
    {
      "page": "surv_effect_methods",
      "title": "S3 Methods for 'surv_effect'",
      "topics": [
        "print.surv_effect",
        "surv_effect_methods",
        "table",
        "table.default",
        "table.surv_effect"
      ]
    },
    {
      "page": "treatment_effect",
      "title": "Treatment Effect",
      "topics": [
        "difference",
        "log_odds_ratio",
        "log_risk_ratio",
        "odds_ratio",
        "risk_ratio",
        "treatment_effect"
      ]
    },
    {
      "page": "vcovG",
      "title": "Generalized Covariance (ANHECOVA)",
      "topics": [
        "vcovG"
      ]
    },
    {
      "page": "vcovHC",
      "title": "Heteroskedasticity-consistent covariance matrix for predictions",
      "topics": [
        "vcovHC"
      ]
    }
  ],
  "_readme": "https://github.com/openpharma/RobinCar2/raw/HEAD/README.md",
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    "survival",
    "zoo"
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      "filename": "intro.html",
      "title": "Package Introduction",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Common Usage",
        "Data Introduction",
        "Obtain Treatment Effect for Continuous Outcomes Using the General Variance",
        "Obtain Treatment Effect for Binary Outcomes",
        "Obtain Treatment Effect for Counts",
        "Models with an Offset",
        "Using Different Covariate-Adaptive Randomization Schema",
        "Obtain the Confidence Intervals for the Marginal Means and contrast"
      ],
      "created": "2024-05-14 13:46:07",
      "modified": "2026-10-19 12:53:36",
      "commits": 7
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  "_indexed": true,
  "_nocasepkg": "robincar2",
  "_universes": [
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    "clarkliming"
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