{
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  "Package": "nrba",
  "Title": "Methods for Conducting Nonresponse Bias Analysis (NRBA)",
  "Version": "0.3.1",
  "Authors@R": "c(\nperson(given = \"Ben\",\nfamily = \"Schneider\",\nrole = c(\"aut\", \"cre\"),\nemail = \"BenjaminSchneider@westat.com\",\ncomment = c(ORCID = \"0000-0002-0406-8470\")),\nperson(given = \"Jim\",\nfamily = \"Green\",\nrole = c(\"aut\"),\nemail = \"JimGreen@westat.com\"),\nperson(given = \"Shelley\",\nfamily = \"Brock\",\nrole = \"aut\",\nemail = \"ShelleyBrock@westat.com\",\ncomment = \"Author of original SAS macro, WesNRBA\"),\nperson(given = \"Tom\",\nfamily = \"Krenzke\",\nrole = \"aut\",\nemail = \"TomKrenzke@westat.com\",\ncomment = \"Author of original SAS macro, WesNRBA\"),\nperson(given = \"Michael\",\nfamily = \"Jones\",\nrole = \"aut\",\nemail = \"MichaelJones@westat.com\",\ncomment = \"Author of original SAS macro, WesNRBA\"),\nperson(given = \"Wendy\",\nfamily = \"Van de Kerckhove\",\nrole = \"aut\",\ncomment = \"Author of original SAS macro, WesNRBA\"),\nperson(given = \"David\",\nfamily = \"Ferraro\",\nrole = \"aut\",\ncomment = \"Author of original SAS macro, WesNRBA\"),\nperson(given = \"Laura\",\nfamily = \"Alvarez-Rojas\",\nrole = \"aut\",\ncomment = \"Author of original SAS macro, WesNRBA\"),\nperson(given = \"Katie\",\nfamily = \"Hubbell\",\nrole = \"aut\",\nemail = \"KatieHubbell@westat.com\",\ncomment = \"Author of original SAS macro, WesNRBA\"),\nperson(given = \"Westat\", role = c(\"cph\")))",
  "Description": "Facilitates nonresponse bias analysis (NRBA) for survey\ndata.  Such data may arise from a complex sampling design with\nfeatures such as stratification, clustering, or unequal\nprobabilities of selection. Multiple types of analyses may be\nconducted: comparisons of response rates across subgroups;\ncomparisons of estimates before and after weighting\nadjustments; comparisons of sample-based estimates to external\npopulation totals; tests of systematic differences in covariate\nmeans between respondents and full samples; tests of\nindependence between response status and covariates; and\nmodeling of outcomes and response status as a function of\ncovariates. Extensive documentation and references are provided\nfor each type of analysis. Krenzke, Van de Kerckhove, and\nMohadjer (2005)\n<http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf>\nand Lohr and Riddles (2016)\n<https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR>\nprovide an overview of the methods implemented in this package.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "RoxygenNote": "7.2.3",
  "Config/testthat/edition": "3",
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  "Packaged": {
    "Date": "2026-06-01 08:05:37 UTC",
    "User": "root"
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  "Author": "Ben Schneider [aut, cre]\n(<https://orcid.org/0000-0002-0406-8470>), Jim Green [aut],\nShelley Brock [aut] (Author of original SAS macro, WesNRBA),\nTom Krenzke [aut] (Author of original SAS macro, WesNRBA),\nMichael Jones [aut] (Author of original SAS macro, WesNRBA),\nWendy Van de Kerckhove [aut] (Author of original SAS macro,\nWesNRBA), David Ferraro [aut] (Author of original SAS macro,\nWesNRBA), Laura Alvarez-Rojas [aut] (Author of original SAS\nmacro, WesNRBA), Katie Hubbell [aut] (Author of original SAS\nmacro, WesNRBA), Westat [cph]",
  "Maintainer": "Ben Schneider <BenjaminSchneider@westat.com>",
  "Config/pak/sysreqs": "make libicu-dev",
  "Repository": "https://westat-schneider.r-universe.dev",
  "Date/Publication": "2023-11-22 02:42:05 UTC",
  "RemoteUrl": "https://github.com/cran/nrba",
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  "_created": "2026-06-01T08:05:37.000Z",
  "_published": "2026-06-01T08:09:50.464Z",
  "_distro": "noble",
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    "email": "benjaminschneider@westat.com",
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    "description": "Statistician at Westat",
    "uuid": 186607058,
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    "extra/citation.json",
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    "extra/NEWS.txt",
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      "date": "2023-07-17"
    },
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      "date": "2023-11-21"
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    "%>%",
    "assess_range_of_bias",
    "calculate_response_rates",
    "chisq_test_ind_response",
    "chisq_test_vs_external_estimate",
    "get_cumulative_estimates",
    "predict_outcome_via_glm",
    "predict_response_status_via_glm",
    "rake_to_benchmarks",
    "stepwise_model_selection",
    "t_test_of_weight_adjustment",
    "t_test_resp_vs_elig",
    "t_test_resp_vs_full",
    "t_test_vs_external_estimate",
    "wt_class_adjust"
  ],
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      "name": "involvement_survey_pop",
      "title": "Parent involvement survey: population data",
      "object": "involvement_survey_pop",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "UNIQUE_ID",
        "SCHOOL_DISTRICT",
        "SCHOOL_ID",
        "STUDENT_GRADE",
        "STUDENT_AGE",
        "STUDENT_DISABILITY_CODE",
        "STUDENT_DISABILITY_CATEGORY",
        "STUDENT_SEX",
        "STUDENT_RACE",
        "PARENT_HAS_EMAIL"
      ],
      "rows": 20000,
      "table": true,
      "tojson": true
    },
    {
      "name": "involvement_survey_srs",
      "title": "Parent involvement survey: simple random sample",
      "object": "involvement_survey_srs",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "UNIQUE_ID",
        "RESPONSE_STATUS",
        "SCHOOL_DISTRICT",
        "SCHOOL_ID",
        "STUDENT_GRADE",
        "STUDENT_AGE",
        "STUDENT_DISABILITY_CODE",
        "STUDENT_DISABILITY_CATEGORY",
        "STUDENT_SEX",
        "STUDENT_RACE",
        "WHETHER_PARENT_AGREES",
        "PARENT_HAS_EMAIL",
        "PARENT_HAS_EMAIL_BENCHMARK",
        "STUDENT_RACE_BENCHMARK",
        "BASE_WEIGHT",
        "N_STUDENTS",
        "CONTACT_ATTEMPTS"
      ],
      "rows": 5000,
      "table": true,
      "tojson": true
    },
    {
      "name": "involvement_survey_str2s",
      "title": "Parent involvement survey: stratified, two-stage sample",
      "object": "involvement_survey_str2s",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "UNIQUE_ID",
        "RESPONSE_STATUS",
        "SCHOOL_DISTRICT",
        "SCHOOL_ID",
        "STUDENT_GRADE",
        "STUDENT_AGE",
        "STUDENT_DISABILITY_CODE",
        "STUDENT_DISABILITY_CATEGORY",
        "STUDENT_SEX",
        "STUDENT_RACE",
        "WHETHER_PARENT_AGREES",
        "PARENT_HAS_EMAIL",
        "PARENT_HAS_EMAIL_BENCHMARK",
        "STUDENT_RACE_BENCHMARK",
        "N_SCHOOLS_IN_DISTRICT",
        "N_STUDENTS_IN_SCHOOL",
        "BASE_WEIGHT",
        "CONTACT_ATTEMPTS"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "assess_range_of_bias",
      "title": "Assess the range of possible bias based on specified assumptions about how nonrespondents differ from respondents",
      "topics": [
        "assess_range_of_bias"
      ]
    },
    {
      "page": "calculate_response_rates",
      "title": "Calculate Response Rates",
      "topics": [
        "calculate_response_rates"
      ]
    },
    {
      "page": "chisq_test_ind_response",
      "title": "Test the independence of survey response and auxiliary variables",
      "topics": [
        "chisq_test_ind_response"
      ]
    },
    {
      "page": "chisq_test_vs_external_estimate",
      "title": "Test of differences in survey percentages relative to external estimates",
      "topics": [
        "chisq_test_vs_external_estimate"
      ]
    },
    {
      "page": "get_cumulative_estimates",
      "title": "Calculate cumulative estimates of a mean/proportion",
      "topics": [
        "get_cumulative_estimates"
      ]
    },
    {
      "page": "involvement_survey_pop",
      "title": "Parent involvement survey: population data",
      "topics": [
        "involvement_survey_pop"
      ]
    },
    {
      "page": "involvement_survey_srs",
      "title": "Parent involvement survey: simple random sample",
      "topics": [
        "involvement_survey_srs"
      ]
    },
    {
      "page": "involvement_survey_str2s",
      "title": "Parent involvement survey: stratified, two-stage sample",
      "topics": [
        "involvement_survey_str2s"
      ]
    },
    {
      "page": "predict_outcome_via_glm",
      "title": "Fit a regression model to predict survey outcomes",
      "topics": [
        "predict_outcome_via_glm"
      ]
    },
    {
      "page": "predict_response_status_via_glm",
      "title": "Fit a logistic regression model to predict response to the survey.",
      "topics": [
        "predict_response_status_via_glm"
      ]
    },
    {
      "page": "rake_to_benchmarks",
      "title": "Re-weight data to match population benchmarks, using raking or post-stratification",
      "topics": [
        "rake_to_benchmarks"
      ]
    },
    {
      "page": "stepwise_model_selection",
      "title": "Select and fit a model using stepwise regression",
      "topics": [
        "stepwise_model_selection"
      ]
    },
    {
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      "title": "t-test of differences in means/percentages between responding sample and full sample, or between responding sample and eligible sample",
      "topics": [
        "t_test_by_response_status",
        "t_test_resp_vs_elig",
        "t_test_resp_vs_full"
      ]
    },
    {
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      "topics": [
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      ]
    },
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      "topics": [
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      ]
    },
    {
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      "title": "Adjust weights in a replicate design for nonresponse or unknown eligibility status, using weighting classes",
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      ]
    }
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      "title": "Analysis of Response Rates and Response Propensity ",
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