{
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  "Package": "BayesSampling",
  "Type": "Package",
  "Title": "Bayes Linear Estimators for Finite Population",
  "Version": "1.1.0",
  "Date": "2021-04-24",
  "Authors@R": "c(person(\"Pedro\", \"Soares Figueiredo\", \nrole = c(\"aut\", \"cre\"),\nemail = \"pedrosfig@hotmail.com\",\ncomment = c(ORCID = \"0000-0003-2279-2881\")),\nperson(\"Kelly C.\", \"M. Gonçalves\",\nrole = c(\"aut\", \"ths\"),\nemail = \"kelly@dme.ufrj.br\",\ncomment = c(ORCID = \"0000-0002-4524-547X\")))",
  "Maintainer": "Pedro Soares Figueiredo <pedrosfig@hotmail.com>",
  "Description": "Allows the user to apply the Bayes Linear approach to\nfinite population with the Simple Random Sampling - BLE_SRS() -\nand the Stratified Simple Random Sampling design - BLE_SSRS() -\n(both without replacement), to the Ratio estimator (using\nauxiliary information) - BLE_Ratio() - and to categorical data\n- BLE_Categorical(). The Bayes linear estimation approach is\napplied to a general linear regression model for finite\npopulation prediction in BLE_Reg() and it is also possible to\nachieve the design based estimators using vague prior\ndistributions. Based on Gonçalves, K.C.M, Moura, F.A.S and\nMigon, H.S.(2014)\n<https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.",
  "URL": "https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886,\nhttps://github.com/pedrosfig/BayesSampling",
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  "Repository": "https://pedrosfig.r-universe.dev",
  "Date/Publication": "2021-05-01 18:20:39 UTC",
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  "Author": "Pedro Soares Figueiredo [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-2279-2881>),\nKelly C. M. Gonçalves [aut, ths] (ORCID:\n<https://orcid.org/0000-0002-4524-547X>)",
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    "sampling"
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    "BLE_Ratio",
    "BLE_Reg",
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    "BLE_SSRS"
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      "name": "BigCity",
      "title": "Full Person-level Population Database",
      "object": "BigCity",
      "class": [
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      ],
      "fields": [
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        "PersonID",
        "Stratum",
        "PSU",
        "Zone",
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        "Income",
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      "page": "BigCity",
      "title": "Full Person-level Population Database",
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      ]
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      "page": "BLE_Categorical",
      "title": "Bayes Linear Method for Categorical Data",
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    {
      "page": "BLE_Ratio",
      "title": "Ratio BLE",
      "topics": [
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      ]
    },
    {
      "page": "BLE_Reg",
      "title": "General BLE case",
      "topics": [
        "BLE_Reg"
      ]
    },
    {
      "page": "BLE_SRS",
      "title": "Simple Random Sample BLE",
      "topics": [
        "BLE_SRS"
      ]
    },
    {
      "page": "BLE_SSRS",
      "title": "Stratified Simple Random Sample BLE",
      "topics": [
        "BLE_SSRS"
      ]
    },
    {
      "page": "C",
      "title": "calculates the C factor",
      "topics": [
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      ]
    },
    {
      "page": "create1",
      "title": "creates vector of 1's to be used in the estimators",
      "topics": [
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      ]
    },
    {
      "page": "E_beta",
      "title": "calculates the BLE for Beta",
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      ]
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    {
      "page": "E_theta_Reg",
      "title": "calculates the BLE for the individuals not in the sample",
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      ]
    },
    {
      "page": "T_Reg",
      "title": "calculates BLE for the total T",
      "topics": [
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      ]
    },
    {
      "page": "V_beta",
      "title": "calculates the risk matrix associated with the BLE for Beta",
      "topics": [
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      ]
    },
    {
      "page": "V_theta_Reg",
      "title": "calculates the risk matrix associated with the BLE for the individuals not in the sample",
      "topics": [
        "V_theta_Reg"
      ]
    },
    {
      "page": "VT_Reg",
      "title": "calculates risk matrix associated with the BLE for for the total T",
      "topics": [
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