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  "Title": "Cross Validation Based on Survey Design",
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  "Date": "2022-05-26",
  "Authors@R": "c(\nperson(\"Cole\", \"Guerin\", email = \"cole@guerincreative.com\", role = \"aut\"),\nperson(\"Thomas\", \"McMahon\", email = \"thomasmcmahon9@gmail.com\", role = \"aut\"),\nperson(\"Jerzy\", \"Wieczorek\", email = \"jawieczo@colby.edu\", role = c(\"cre\", \"aut\"),\ncomment = c(ORCID = \"0000-0002-2859-6534\")),\nperson(\"Ben\", \"Schneider\", email = \"benjamin.julius.schneider@gmail.com\", role = \"ctb\"),\nperson(\"Hunter\", \"Ratliff\", role = \"ctb\"))",
  "Description": "Functions to generate K-fold cross validation (CV) folds\nand CV test error estimates that take into account how a survey\ndataset's sampling design was constructed (SRS, clustering,\nstratification, and/or unequal sampling weights). You can input\nlinear and logistic regression models, along with data and a\ntype of survey design in order to get an output that can help\nyou determine which model best fits the data using K-fold cross\nvalidation. Our paper on \"K-Fold Cross-Validation for Complex\nSample Surveys\" by Wieczorek, Guerin, and McMahon (2022)\n<doi:10.1002/sta4.454> explains why differing how we take folds\nbased on survey design is useful.",
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  "Encoding": "UTF-8",
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  "BugReports": "https://github.com/ColbyStatSvyRsch/surveyCV/issues",
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  "Repository": "https://colbystatsvyrsch.r-universe.dev",
  "Date/Publication": "2022-05-26 20:20:09 UTC",
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    "message": "Fix variable extraction for `DBIsvydesign`s. Add warnings, errors, documentation notes, and test cases.\n",
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      "title": "Subset of the 2015-2017 National Survey of Family Growth (NSFG): all live births per respondent.",
      "object": "NSFG_data_everypreg",
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        "income",
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        "race",
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        "PregNum",
        "eduCat",
        "GA",
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      "title": "Creating CV folds based on the survey design",
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