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  "Title": "Inference Based on Non-Probability Samples",
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  "Description": "Statistical inference with non-probability samples when\nauxiliary information from external sources such as probability\nsamples or population totals or means is available. The package\nimplements various methods such as inverse probability\n(propensity score) weighting, mass imputation and doubly robust\napproach. Details can be found in: Chen et al. (2020)\n<doi:10.1080/01621459.2019.1677241>, Yang et al. (2020)\n<doi:10.1111/rssb.12354>, Kim et al. (2021)\n<doi:10.1111/rssa.12696>, Yang et al. (2021)\n<https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm>\nand Wu (2022)\n<https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>.\nFor details on the package and its functionalities see\n<doi:10.48550/arXiv.2504.04255>.",
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  "Author": "Łukasz Chrostowski [aut, ctb],\nMaciej Beręsewicz [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-8281-4301>),\nPiotr Chlebicki [aut, ctb] (ORCID:\n<https://orcid.org/0009-0006-4867-7434>)",
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      "title": "Admin Data (Non-Probability Survey)",
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        "size",
        "nace",
        "region",
        "single_shift"
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      "class": [
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      ],
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        "private",
        "size",
        "nace",
        "region",
        "weight"
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      "table": true,
      "tojson": true
    }
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      "page": "admin",
      "title": "Admin Data (Non-Probability Survey)",
      "topics": [
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      ]
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    {
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      "title": "Checks Variable Balance Between the Probability and Non-Probability Samples",
      "topics": [
        "check_balance"
      ]
    },
    {
      "page": "coef.nonprob",
      "title": "Returns Coefficients of the Underlying Models",
      "topics": [
        "coef.nonprob"
      ]
    },
    {
      "page": "confint.nonprob",
      "title": "Returns Confidence Intervals for Estimated Mean",
      "topics": [
        "confint.nonprob"
      ]
    },
    {
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      "title": "Control Parameters for Inference",
      "topics": [
        "control_inf"
      ]
    },
    {
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      "title": "Control Parameters for Outcome Model",
      "topics": [
        "control_out"
      ]
    },
    {
      "page": "control_sel",
      "title": "Control Parameters for the Selection Model",
      "topics": [
        "control_sel"
      ]
    },
    {
      "page": "extract",
      "title": "Extracts Estimates from the Nonprob Class Object",
      "topics": [
        "extract"
      ]
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      "title": "Job Vacancy Survey",
      "topics": [
        "jvs"
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    },
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      "title": "Mass Imputation Using the Generalized Linear Model Method",
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      ]
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      "title": "Mass Imputation Using Nearest Neighbours Matching Method",
      "topics": [
        "method_nn"
      ]
    },
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      "page": "method_npar",
      "title": "Mass Imputation Using Non-Parametric Model Method",
      "topics": [
        "method_npar"
      ]
    },
    {
      "page": "method_pmm",
      "title": "Mass Imputation Using Predictive Mean Matching Method",
      "topics": [
        "method_pmm"
      ]
    },
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      "page": "method_ps",
      "title": "Propensity Score Model Functions",
      "topics": [
        "method_ps"
      ]
    },
    {
      "page": "nobs.nonprob",
      "title": "Returns the Number of Rows in Samples",
      "topics": [
        "nobs.nonprob"
      ]
    },
    {
      "page": "nonprob",
      "title": "Inference with Non-Probability Survey Samples",
      "topics": [
        "nonprob"
      ]
    },
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      "page": "plot.nonprob",
      "title": "Plots the Estimated Mean(s) and Their Confidence Interval(s)",
      "topics": [
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      ]
    },
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      "title": "Returns Population Size (Estimated or Fixed)",
      "topics": [
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    },
    {
      "page": "print.nonprob_summary",
      "title": "Print Method for the Nonprob_summary Object",
      "topics": [
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    },
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      "title": "Summary Statistics for Models of the Nonprob Class",
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      ]
    },
    {
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      "title": "Update Method for the Nonprob Object with Changed Arguments or Parameters",
      "topics": [
        "update.nonprob"
      ]
    },
    {
      "page": "weights.nonprob",
      "title": "Extracts the Inverse Probability (Propensity Score) Weights",
      "topics": [
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      "source": "nonprobsvy.Rmd",
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      "title": "nonprobsvy -- An R Package for Modern Methods for Non-Probability Surveys",
      "author": "Łukasz Chrostowski (Analytic Partners), Piotr Chlebicki (Stockholm University), Maciej Beręsewicz (Poznań University of Economics and Business; Statistical Office in Poznań)",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Software for non-probability samples",
        "Methods for non-probability samples",
        "Basic setup",
        "Prediction-based approach",
        "Prediction estimators",
        "Mass imputation estimators",
        "Variance estimators for the prediction approach",
        "Inverse probability weighting",
        "Variance estimators for the inverse probability weighting approach",
        "Doubly robust approach",
        "Parameters estimated separately",
        "Minimization of the bias for doubly robust methods",
        "Variance estimators for the doubly robust approach",
        "Variable selection algorithms",
        "The main function and the package functionalities",
        "The nonprob function",
        "Controlling the type of estimators",
        "Controlling variance estimation",
        "Data analysis example",
        "Description of the data",
        "Estimation",
        "Propensity score approach",
        "The doubly robust approach",
        "Comparison of estimates",
        "Advanced usage",
        "Bootstrap approach for variance estimation",
        "Classes and S3 methods",
        "Summary and future work",
        "Acknowledgements",
        "Appendix: Algorithms for the MI-NN and MI-PMM estimators",
        "References"
      ],
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      "modified": "2026-05-23 07:23:03",
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