{
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  "Package": "causaldrf",
  "Type": "Package",
  "Title": "Estimating Causal Dose Response Functions",
  "Version": "0.4.2",
  "Date": "2022-09-29",
  "Authors@R": "c(\nperson(\"Douglas\", \"Galagate\", email = \"galagated@gmail.com\", role = \"cre\"),\nperson(\"Joseph\", \"Schafer\", email = \"Joseph.L.Schafer@census.gov\", role = \"aut\"))",
  "Description": "Functions and data to estimate causal dose response\nfunctions given continuous, ordinal, or binary treatments.  A\ndescription of the methods is given in Galagate (2016)\n<https://drum.lib.umd.edu/handle/1903/18170>.",
  "License": "MIT + file LICENSE",
  "LazyData": "TRUE",
  "VignetteBuilder": "knitr",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-04 10:14:41 UTC",
    "User": "root"
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  "Author": "Douglas Galagate [cre], Joseph Schafer [aut]",
  "Maintainer": "Douglas Galagate <galagated@gmail.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2022-09-29 19:40:10 UTC",
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    "extra/citation.html",
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    "gam_est",
    "get_ci",
    "hi_est",
    "iptw_est",
    "ismw_est",
    "iw_est",
    "nw_est",
    "overlap_fun",
    "prop_spline_est",
    "reg_est",
    "scalar_wts",
    "t_mod",
    "wtrg_est"
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      "title": "Simulated data from Hirano and Imbens (2004)",
      "object": "hi_sim_data",
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        "data.frame"
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        "X2",
        "T",
        "gps",
        "Y"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "nmes_data",
      "title": "Data set containing data from the National Medical Expenditure Survey (NMES)",
      "object": "nmes_data",
      "class": [
        "data.frame"
      ],
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        "packyears",
        "AGESMOKE",
        "LASTAGE",
        "MALE",
        "RACE3",
        "beltuse",
        "educate",
        "marital",
        "SREGION",
        "POVSTALB",
        "HSQACCWT",
        "TOTALEXP"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "sim_data",
      "title": "Simulated data from Schafer and Galagate (2015)",
      "object": "sim_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "A.1",
        "A.2",
        "A.3",
        "A.4",
        "A.5",
        "A.6",
        "A.7",
        "A.8",
        "B.1",
        "B.2",
        "B.3",
        "B.4",
        "B.5",
        "B.6",
        "B.7",
        "B.8",
        "T",
        "Y",
        "Theta.1",
        "Theta.2"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    }
  ],
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    {
      "page": "add_spl_est",
      "title": "The additive spline estimator",
      "topics": [
        "add_spl_est"
      ]
    },
    {
      "page": "aipwee_est",
      "title": "Prediction with a residual bias correction estimator",
      "topics": [
        "aipwee_est"
      ]
    },
    {
      "page": "bart_est",
      "title": "The BART estimator",
      "topics": [
        "bart_est"
      ]
    },
    {
      "page": "gam_est",
      "title": "The GAM estimator",
      "topics": [
        "gam_est"
      ]
    },
    {
      "page": "get_ci",
      "title": "This calculates an upper and lower bound from bootstrap matrix",
      "topics": [
        "get_ci"
      ]
    },
    {
      "page": "hi_est",
      "title": "The Hirano and Imbens estimator",
      "topics": [
        "hi_est"
      ]
    },
    {
      "page": "hi_sim_data",
      "title": "Simulated data from Hirano and Imbens (2004)",
      "topics": [
        "hi_sim_data"
      ]
    },
    {
      "page": "iptw_est",
      "title": "The inverse probability of treatment weighting (iptw) estimator",
      "topics": [
        "iptw_est"
      ]
    },
    {
      "page": "ismw_est",
      "title": "The inverse second moment weighting (ismw) estimator",
      "topics": [
        "ismw_est"
      ]
    },
    {
      "page": "iw_est",
      "title": "The inverse weighting estimator (nonparametric method)",
      "topics": [
        "iw_est"
      ]
    },
    {
      "page": "nmes_data",
      "title": "Data set containing data from the National Medical Expenditure Survey (NMES)",
      "topics": [
        "nmes_data"
      ]
    },
    {
      "page": "nw_est",
      "title": "The Nadaraya-Watson modified estimator",
      "topics": [
        "nw_est"
      ]
    },
    {
      "page": "overlap_fun",
      "title": "This function creates an overlapping dataset",
      "topics": [
        "overlap_fun"
      ]
    },
    {
      "page": "prop_spline_est",
      "title": "The propensity-spline prediction estimator",
      "topics": [
        "prop_spline_est"
      ]
    },
    {
      "page": "reg_est",
      "title": "The regression prediction estimator",
      "topics": [
        "reg_est"
      ]
    },
    {
      "page": "scalar_wts",
      "title": "This function calculates scalar weights for use in other models",
      "topics": [
        "scalar_wts"
      ]
    },
    {
      "page": "sim_data",
      "title": "Simulated data from Schafer and Galagate (2015)",
      "topics": [
        "sim_data"
      ]
    },
    {
      "page": "t_mod",
      "title": "A function to estimate conditional expected values and higher order moments",
      "topics": [
        "t_mod"
      ]
    },
    {
      "page": "wtrg_est",
      "title": "The weighted regression estimator",
      "topics": [
        "wtrg_est"
      ]
    }
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    "mgcv",
    "minqa",
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    "nlme",
    "numDeriv",
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    "survival"
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      "title": "Using causaldrf",
      "engine": "knitr::knitr",
      "headings": [],
      "created": "2015-11-30 17:18:48",
      "modified": "2022-09-29 19:40:10",
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  "_indexed": true,
  "_nocasepkg": "causaldrf",
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