{
  "_id": "6a1fce68b401979e734337bf",
  "Package": "qgcomp",
  "Title": "Quantile G-Computation",
  "Version": "2.19.0",
  "Date": "2026-05-04",
  "Authors@R": "person(\"Alexander\", \"Keil\", , \"alex.keil@nih.gov\", role = c(\"aut\", \"cre\"))",
  "Description": "G-computation for a set of time-fixed exposures with\nquantile-based basis functions, possibly under linearity and\nhomogeneity assumptions. This approach estimates a regression\nline corresponding to the expected change in the outcome (on\nthe link basis) given a simultaneous increase in the\nquantile-based category for all exposures. Works with\ncontinuous, binary, and right-censored time-to-event outcomes.\nReference: Alexander P. Keil, Jessie P. Buckley, Katie M.\nOBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J.\nWhite (2019) A quantile-based g-computation approach to\naddressing the effects of exposure mixtures;\n<doi:10.1289/EHP5838>.",
  "License": "GPL (>= 2)",
  "URL": "https://github.com/alexpkeil1/qgcomp/",
  "BugReports": "https://github.com/alexpkeil1/qgcomp/issues",
  "VignetteBuilder": "knitr",
  "Encoding": "UTF-8",
  "Language": "en-US",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "Config/pak/sysreqs": "cmake make libicu-dev",
  "Repository": "https://alexpkeil1.r-universe.dev",
  "Date/Publication": "2026-05-04 20:06:21 UTC",
  "RemoteUrl": "https://github.com/alexpkeil1/qgcomp",
  "RemoteRef": "HEAD",
  "RemoteSha": "f6c3c66721c4110011931849d88ee029304f479d",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-03 06:43:54 UTC",
    "User": "root"
  },
  "Author": "Alexander Keil [aut, cre]",
  "Maintainer": "Alexander Keil <alex.keil@nih.gov>",
  "MD5sum": "025d36e9e02bfbbeb00557839e9b6d03",
  "_user": "alexpkeil1",
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  "_created": "2026-06-03T06:43:54.000Z",
  "_published": "2026-06-03T06:49:12.826Z",
  "_distro": "noble",
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  "_commit": {
    "id": "f6c3c66721c4110011931849d88ee029304f479d",
    "author": "Alex Keil <alexpkeil1@users.noreply.github.com>",
    "committer": "Alex Keil <alexpkeil1@users.noreply.github.com>",
    "message": "Add partial-average methods and printers\n\nIntroduce experimental partial-average estimation: add .qgcomp.partials_avg and .qgcomp.partials_avg_bootci implementations (with future-based resampling, Rubin-style variance pooling and bootstrap CIs). Add S3 print methods (print.qgcomppartialavg, print.qgcomppartialavg_boot) and corresponding Rd help files, and register the S3 methods in NAMESPACE. Bump package version/date to 2.19.0 and update README.\n",
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    ".qgcomp_object",
    ".qgcomp_object_add",
    "coxmsm_fit",
    "homogeneity_test",
    "hurdlemsm_fit.control",
    "joint_test",
    "mice.impute.leftcenslognorm",
    "mice.impute.tobit",
    "modelbound.boot",
    "modelbound.ee",
    "msm_fit",
    "msm_multinomial_fit",
    "msm.predict",
    "pointwisebound.boot",
    "pointwisebound.noboot",
    "qgcomp",
    "qgcomp.boot",
    "qgcomp.cch.noboot",
    "qgcomp.cox.boot",
    "qgcomp.cox.noboot",
    "qgcomp.ee",
    "qgcomp.glm.boot",
    "qgcomp.glm.ee",
    "qgcomp.glm.noboot",
    "qgcomp.hurdle.boot",
    "qgcomp.hurdle.noboot",
    "qgcomp.multinomial.boot",
    "qgcomp.multinomial.noboot",
    "qgcomp.noboot",
    "qgcomp.partials",
    "qgcomp.survcurve.boot",
    "qgcomp.tobit.noboot",
    "qgcomp.zi.boot",
    "qgcomp.zi.noboot",
    "quantize",
    "se_comb",
    "simdata_quantized",
    "split_data",
    "vc_comb",
    "zimsm_fit.control"
  ],
  "_datasets": [
    {
      "name": "metals",
      "title": "Well water data",
      "object": "metals",
      "file": "metals.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "arsenic",
        "barium",
        "cadmium",
        "calcium",
        "chloride",
        "chromium",
        "copper",
        "iron",
        "lead",
        "magnesium",
        "manganese",
        "mercury",
        "nitrate",
        "nitrite",
        "ph",
        "selenium",
        "silver",
        "sodium",
        "sulfate",
        "total_alkalinity",
        "total_hardness",
        "zinc",
        "mage35",
        "y",
        "disease_time",
        "disease_state"
      ],
      "rows": 452,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "dot-qgcomp_object",
      "title": "Creating a 'qgcompfit' object",
      "topics": [
        ".qgcomp_object"
      ]
    },
    {
      "page": "dot-qgcomp_object_add",
      "title": "Adding objects to a 'qgcompfit' object",
      "topics": [
        ".qgcomp_object_add"
      ]
    },
    {
      "page": "checknames",
      "title": "Check for valid model terms in a qgcomp fit",
      "topics": [
        "checknames"
      ]
    },
    {
      "page": "coxmsm_fit",
      "title": "Marginal structural Cox model (MSM) fitting within quantile g-computation",
      "concept": [
        "variance mixtures"
      ],
      "topics": [
        "coxmsm_fit"
      ]
    },
    {
      "page": "glance.qgcompfit",
      "title": "Glance at a qgcompfit object",
      "topics": [
        "glance.qgcompfit"
      ]
    },
    {
      "page": "homogeneity_test",
      "title": "Hypothesis testing about a joint effect of exposures on a multinomial outcome",
      "topics": [
        "homogeneity_test",
        "homogeneity_test.qgcompmultfit"
      ]
    },
    {
      "page": "hurdlemsm_fit",
      "title": "Secondary prediction method for the (hurdle) qgcomp MSM.",
      "concept": [
        "variance mixtures"
      ],
      "topics": [
        "hurdlemsm_fit"
      ]
    },
    {
      "page": "hurdlemsm_fit.control",
      "title": "Control of fitting parameters for zero inflated MSMs",
      "topics": [
        "hurdlemsm_fit.control"
      ]
    },
    {
      "page": "joint_test",
      "title": "Hypothesis testing about a joint effect of exposures on a multinomial outcome",
      "topics": [
        "joint_test",
        "joint_test.qgcompmultfit"
      ]
    },
    {
      "page": "metals",
      "title": "Well water data",
      "topics": [
        "metals"
      ]
    },
    {
      "page": "mice.impute.leftcenslognorm",
      "title": "Imputation for limits of detection problems",
      "topics": [
        "mice.impute.leftcenslognorm",
        "mice.impute.tobit"
      ]
    },
    {
      "page": "modelbound.boot",
      "title": "Estimating qgcomp regression line confidence bounds",
      "topics": [
        "modelbound.boot"
      ]
    },
    {
      "page": "modelbound.ee",
      "title": "Estimating qgcomp regression line confidence bounds",
      "topics": [
        "modelbound.ee"
      ]
    },
    {
      "page": "msm_fit",
      "title": "Fitting marginal structural model (MSM) within quantile g-computation",
      "concept": [
        "variance mixtures"
      ],
      "topics": [
        "msm_fit"
      ]
    },
    {
      "page": "msm_multinomial_fit",
      "title": "Fitting marginal structural model (MSM) within quantile g-computation",
      "concept": [
        "variance mixtures"
      ],
      "topics": [
        "msm_multinomial_fit"
      ]
    },
    {
      "page": "msm.predict",
      "title": "Secondary prediction method for the (non-survival) qgcomp MSM.",
      "topics": [
        "msm.predict"
      ]
    },
    {
      "page": "plot.qgcompfit",
      "title": "Default plotting method for a qgcompfit object",
      "topics": [
        "plot.qgcompfit",
        "plot.qgcompmultfit"
      ]
    },
    {
      "page": "pointwisebound.boot",
      "title": "Estimating pointwise comparisons for qgcomp.glm.boot objects",
      "topics": [
        "pointwisebound.boot"
      ]
    },
    {
      "page": "pointwisebound.noboot",
      "title": "Estimating pointwise comparisons for qgcomp.glm.noboot objects",
      "topics": [
        "pointwisebound.noboot"
      ]
    },
    {
      "page": "predict.qgcompfit",
      "title": "Default prediction method for a qgcompfit object (non-survival outcomes only)",
      "topics": [
        "predict.qgcompfit"
      ]
    },
    {
      "page": "print.qgcompfit",
      "title": "Default printing method for a qgcompfit object",
      "concept": [
        "variance mixtures"
      ],
      "topics": [
        "print.qgcompfit"
      ]
    },
    {
      "page": "print.qgcomppartialavg",
      "title": "Default printing method for a qgcomppartialavg object",
      "topics": [
        "print.qgcomppartialavg"
      ]
    },
    {
      "page": "print.qgcomppartialavg_boot",
      "title": "Default printing method for a qgcomppartialavg object",
      "topics": [
        "print.qgcomppartialavg_boot"
      ]
    },
    {
      "page": "qgcomp",
      "title": "Quantile g-computation for continuous, binary, count, and censored survival outcomes",
      "concept": [
        "variance mixtures"
      ],
      "topics": [
        "qgcomp"
      ]
    },
    {
      "page": "qgcomp.cch.noboot",
      "title": "Quantile g-computation for survival outcomes in a case-cohort design under linearity/additivity",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.cch.noboot"
      ]
    },
    {
      "page": "qgcomp.cox.boot",
      "title": "Quantile g-computation for survival outcomes",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.cox.boot"
      ]
    },
    {
      "page": "qgcomp.cox.noboot",
      "title": "Quantile g-computation for survival outcomes under linearity/additivity",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.cox.noboot"
      ]
    },
    {
      "page": "qgcomp.glm.boot",
      "title": "Quantile g-computation for continuous and binary outcomes",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.boot",
        "qgcomp.glm.boot"
      ]
    },
    {
      "page": "qgcomp.glm.ee",
      "title": "Quantile g-computation for continuous and binary outcomes",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.ee",
        "qgcomp.glm.ee"
      ]
    },
    {
      "page": "qgcomp.glm.noboot",
      "title": "Quantile g-computation for continuous, binary, and count outcomes under linearity/additivity",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.glm.noboot",
        "qgcomp.noboot"
      ]
    },
    {
      "page": "qgcomp.hurdle.boot",
      "title": "Quantile g-computation for hurdle count outcomes",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.hurdle.boot"
      ]
    },
    {
      "page": "qgcomp.hurdle.noboot",
      "title": "Quantile g-computation for hurdle count outcomes under linearity/additivity",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.hurdle.noboot"
      ]
    },
    {
      "page": "qgcomp.multinomial.boot",
      "title": "Quantile g-computation for multinomial outcomes",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.multinomial.boot"
      ]
    },
    {
      "page": "qgcomp.multinomial.noboot",
      "title": "Quantile g-computation for multinomial outcomes",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.multinomial.noboot"
      ]
    },
    {
      "page": "qgcomp.partials",
      "title": "Partial effect sizes, confidence intervals, hypothesis tests",
      "concept": [
        "qgcomp_methods"
      ],
      "topics": [
        "qgcomp.partials"
      ]
    },
    {
      "page": "qgcomp.survcurve.boot",
      "title": "Survival curve data from a qgcomp survival fit",
      "topics": [
        "qgcomp.survcurve.boot"
      ]
    },
    {
      "page": "qgcomp.tobit.noboot",
      "title": "Quantile g-computation for left-censored outcomes",
      "topics": [
        "qgcomp.tobit.noboot"
      ]
    },
    {
      "page": "qgcomp.zi.boot",
      "title": "Quantile g-computation for zero-inflated count outcomes",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.zi.boot"
      ]
    },
    {
      "page": "qgcomp.zi.noboot",
      "title": "Quantile g-computation for zero-inflated count outcomes under linearity/additivity",
      "concept": [
        "qgcomp_methods",
        "variance mixtures"
      ],
      "topics": [
        "qgcomp.zi.noboot"
      ]
    },
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      "author": "Alexander Keil",
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