{
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  "Package": "distrr",
  "Title": "Estimate and Manage Empirical Distributions",
  "Description": "Tools to estimate and manage empirical distributions,\nwhich should work with survey data. One of the main features is\nthe possibility to create data cubes of estimated statistics,\nthat include all the combinations of the variables of interest\n(see for example functions dcc5() and dcc6()).",
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  "URL": "https://gibonet.github.io/distrr,\nhttps://github.com/gibonet/distrr,\nhttps://gibonet.github.io/distrr/",
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  "Repository": "https://gibonet.r-universe.dev",
  "Date/Publication": "2024-11-23 14:55:20 UTC",
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    "Date": "2026-05-15 05:52:39 UTC",
    "User": "root"
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    "extra/citation.html",
    "extra/citation.json",
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  "_realowner": "gibonet",
  "_cranurl": true,
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      "date": "2019-01-03"
    },
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      "date": "2020-07-14"
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    "dcc2",
    "dcc5",
    "dcc6",
    "dcc6_fixed",
    "extract_unique",
    "extract_unique2",
    "extract_unique3",
    "extract_unique4",
    "extract_unique5",
    "Fhat_conditional_",
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    "jointfun_",
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        "tbl",
        "data.frame"
      ],
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        "sector",
        "education",
        "wage",
        "sample_weights"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    }
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    {
      "page": "combn_char",
      "title": "Generate all combinations of the elements of a character vector",
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        "combn_char"
      ]
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      "title": "Data cube creation (dcc)",
      "topics": [
        "dcc",
        "dcc2",
        "dcc5"
      ]
    },
    {
      "page": "dcc6",
      "title": "Data cube creation",
      "topics": [
        "dcc6",
        "dcc6_fixed"
      ]
    },
    {
      "page": "extract_unique",
      "title": "Functions to be used in conjunction with 'dcc' family",
      "topics": [
        "extract_unique",
        "extract_unique2",
        "extract_unique3",
        "extract_unique4",
        "extract_unique5"
      ]
    },
    {
      "page": "Fhat_conditional_",
      "title": "Weighted empirical cumulative distribution function (ecdf), conditional on one or more variables",
      "topics": [
        "Fhat_conditional_"
      ]
    },
    {
      "page": "Fhat_df_",
      "title": "Weighted empirical cumulative distribution function (data frame version)",
      "topics": [
        "Fhat_df_"
      ]
    },
    {
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      "title": "Invented dataset with wages of men and women.",
      "topics": [
        "invented_wages"
      ]
    },
    {
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      "title": "A minimal function which counts the number of observations by groups in a data frame",
      "topics": [
        "jointfun_"
      ]
    },
    {
      "page": "only_joint",
      "title": "Keeps only joint distribution (removes '.total').",
      "topics": [
        "only_joint"
      ]
    },
    {
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      "title": "Empirical weighted quantile",
      "topics": [
        "wq"
      ]
    }
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