{
  "_id": "6a1696b8acfb0bcc41d7bb8e",
  "Package": "NonProbEst",
  "Type": "Package",
  "Title": "Estimation in Nonprobability Sampling",
  "Version": "0.2.4",
  "Author": "Luis Castro Martín <luiscastro193@gmail.com>, Ramón Ferri\nGarcía <rferri@ugr.es> and María del Mar Rueda <mrueda@ugr.es>",
  "Maintainer": "Luis Castro Martín <luiscastro193@gmail.com>",
  "Description": "Different inference procedures are proposed in the\nliterature to correct for selection bias that might be\nintroduced with non-random selection mechanisms. A class of\nmethods to correct for selection bias is to apply a statistical\nmodel to predict the units not in the sample (super-population\nmodeling). Other studies use calibration or Statistical\nMatching (statistically match nonprobability and probability\nsamples). To date, the more relevant methods are weighting by\nPropensity Score Adjustment (PSA). The Propensity Score\nAdjustment method was originally developed to construct weights\nby estimating response probabilities and using them in\nHorvitz–Thompson type estimators. This method is usually used\nby combining a non-probability sample with a reference sample\nto construct propensity models for the non-probability sample.\nCalibration can be used in a posterior way to adding\ninformation of auxiliary variables. Propensity scores in PSA\nare usually estimated using logistic regression models. Machine\nlearning classification algorithms can be used as alternatives\nfor logistic regression as a technique to estimate\npropensities. The package 'NonProbEst' implements some of these\nmethods and thus provides a wide options to work with data\ncoming from a non-probabilistic sample.",
  "License": "GPL (>= 2)",
  "Encoding": "UTF-8",
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  "Packaged": {
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  "Repository": "https://luiscastro193.r-universe.dev",
  "Date/Publication": "2020-06-03 11:10:03 UTC",
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    "calib_weights",
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    "fast_jackknife_variance",
    "generic_jackknife_variance",
    "jackknife_variance",
    "lee_weights",
    "matching",
    "mean_estimation",
    "model_assisted",
    "model_based",
    "model_calibrated",
    "prop_estimation",
    "propensities",
    "sc_weights",
    "total_estimation",
    "valliant_weights",
    "vd_weights"
  ],
  "_datasets": [
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      "name": "population",
      "title": "A full population",
      "object": "population",
      "class": [
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      ],
      "fields": [
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        "education_secundaria",
        "education_terciaria",
        "age",
        "sex",
        "language"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "sampleNP",
      "title": "A non-probabilistic sample",
      "object": "sampleNP",
      "class": [
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      ],
      "fields": [
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        "vote_pens",
        "vote_pir",
        "education_primaria",
        "education_secundaria",
        "education_terciaria",
        "age",
        "sex",
        "language"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "sampleP",
      "title": "A probabilistic sample",
      "object": "sampleP",
      "class": [
        "data.frame"
      ],
      "fields": [
        "education_primaria",
        "education_secundaria",
        "education_terciaria",
        "age",
        "sex"
      ],
      "rows": 500,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "calib_weights",
      "title": "Weights of the calibration estimator",
      "topics": [
        "calib_weights"
      ]
    },
    {
      "page": "confidence_interval",
      "title": "Confidence interval",
      "topics": [
        "confidence_interval"
      ]
    },
    {
      "page": "fast_jackknife_variance",
      "title": "Calculates Jackknife variance without reweighting",
      "topics": [
        "fast_jackknife_variance"
      ]
    },
    {
      "page": "generic_jackknife_variance",
      "title": "Calculates Jackknife variance with reweighting for an arbitrary estimator",
      "topics": [
        "generic_jackknife_variance"
      ]
    },
    {
      "page": "jackknife_variance",
      "title": "Calculates Jackknife variance with reweighting for PSA",
      "topics": [
        "jackknife_variance"
      ]
    },
    {
      "page": "lee_weights",
      "title": "Calculates Lee weights",
      "topics": [
        "lee_weights"
      ]
    },
    {
      "page": "matching",
      "title": "Predicts unknown responses by matching",
      "topics": [
        "matching"
      ]
    },
    {
      "page": "mean_estimation",
      "title": "Estimates the population means",
      "topics": [
        "mean_estimation"
      ]
    },
    {
      "page": "model_assisted",
      "title": "Calculates a model assisted estimation",
      "topics": [
        "model_assisted"
      ]
    },
    {
      "page": "model_based",
      "title": "Calculates a model based estimation",
      "topics": [
        "model_based"
      ]
    },
    {
      "page": "model_calibrated",
      "title": "Calculates a model calibrated estimation",
      "topics": [
        "model_calibrated"
      ]
    },
    {
      "page": "population",
      "title": "A full population",
      "topics": [
        "population"
      ]
    },
    {
      "page": "prop_estimation",
      "title": "Estimates the population proportion",
      "topics": [
        "prop_estimation"
      ]
    },
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      "title": "Calculates sample propensities",
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      ]
    },
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      "topics": [
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      ]
    },
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      "topics": [
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      ]
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      ]
    },
    {
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      "title": "Estimates the population totals",
      "topics": [
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      ]
    },
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      ]
    },
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