{
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  "Title": "Ecological Inference by Linear Programming under Homogeneity",
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  "Authors@R": "c(person(given = \"Jose M.\",\nfamily = \"Pavía\",\nrole = c(\"aut\", \"cre\"),\nemail = \"jose.m.pavia@uv.es\",\ncomment = c(ORCID = \"0000-0002-0129-726X\")),\nperson(given = \"Rafael\",\nfamily = \"Romero\",\nrole = \"aut\",\nemail = \"rromero@eio.upv.es\"))",
  "Description": "Provides a bunch of algorithms based on linear programming\nfor estimating, under the homogeneity hypothesis, RxC\necological contingency tables (or vote transition matrices)\nusing mainly aggregate data (from voting units). References:\nPavía and Romero (2024) <doi:10.1177/00491241221092725>. Pavía\nand Romero (2024) <doi:10.1093/jrsssa/qnae013>. Pavía (2023)\n<doi:10.1007/s43545-023-00658-y>. Pavía (2024)\n<doi:10.1080/0022250X.2024.2423943>. Pavía (2024)\n<doi:10.1177/07591063241277064>. Pavía and Penadés (2024). A\nbottom-up approach for ecological inference. Romero, Pavía,\nMartín and Romero (2020) <doi:10.1080/02664763.2020.1804842>.\nAcknowledgements: The authors wish to thank Consellería de\nEducación, Cultura, Universidades y Empleo, Generalitat\nValenciana (grants AICO/2021/257, CIAICO/2023/031) and\nMICIU/AEI/10.13039/501100011033/FEDER, UE (grant\nPID2021-128228NB-I00) for supporting this research.",
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  "Author": "Jose M. Pavía [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-0129-726X>), Rafael Romero [aut]",
  "Maintainer": "Jose M. Pavía <jose.m.pavia@uv.es>",
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    "lphom",
    "lphom_dual",
    "lphom_joint",
    "nslphom",
    "nslphom_dual",
    "nslphom_joint",
    "rslphom",
    "tslphom",
    "tslphom_dual",
    "tslphom_joint"
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      "title": "2017 French Presidential Election. Department official results.",
      "object": "France2017D",
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        "ABSTENTION",
        "BLANK_NULL",
        "MACRON",
        "LE_PEN",
        "FILLON",
        "MELENCHON",
        "HAMON",
        "DUPONT.AIGNAN",
        "OTHERS",
        "ABSTENTION2",
        "BLANK_NULL2",
        "MACRON2",
        "LE_PEN2"
      ],
      "rows": 108,
      "table": true,
      "tojson": true
    },
    {
      "name": "France2017P",
      "title": "2017 French Presidential Election. Regional provisional results.",
      "object": "France2017P",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ABSTENTION",
        "MACRON",
        "LE_PEN",
        "FILLON",
        "MELENCHON",
        "HAMON",
        "DUPONT",
        "OTHERS",
        "ABSTENTION2",
        "BLANK_NULL",
        "MACRON2",
        "LE_PEN2"
      ],
      "rows": 13,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "adjust2integers",
      "title": "Integer-adjusting of outputs of the lphom-family functions",
      "topics": [
        "adjust2integers"
      ]
    },
    {
      "page": "confidence_intervals_pjk",
      "title": "Confidence Intervals for lphom estimates",
      "topics": [
        "confidence_intervals_pjk"
      ]
    },
    {
      "page": "error_lphom",
      "title": "Global error of a lphom estimated table",
      "topics": [
        "error_lphom"
      ]
    },
    {
      "page": "France2017D",
      "title": "2017 French Presidential Election. Department official results.",
      "topics": [
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      ]
    },
    {
      "page": "France2017P",
      "title": "2017 French Presidential Election. Regional provisional results.",
      "topics": [
        "France2017P"
      ]
    },
    {
      "page": "lclphom",
      "title": "Implements lclphom algorithm",
      "concept": [
        "linear programing ecological inference functions"
      ],
      "topics": [
        "lclphom"
      ]
    },
    {
      "page": "lp_apriori",
      "title": "Implements lp_apriori models",
      "concept": [
        "linear programing ecological inference functions"
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        "lp_apriori"
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    },
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      "page": "lphom",
      "title": "Implements lphom algorithm",
      "concept": [
        "linear programing ecological inference functions"
      ],
      "topics": [
        "lphom"
      ]
    },
    {
      "page": "lphom_dual",
      "title": "Implements lphom_dual algorithm",
      "concept": [
        "linear programing ecological inference functions"
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      "topics": [
        "lphom_dual"
      ]
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      "page": "lphom_joint",
      "title": "Implements the lphom_joint algorithm",
      "concept": [
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      "page": "nslphom",
      "title": "Implements nslphom algorithm",
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        "nslphom"
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      "title": "Implements the nslphom_dual algorithm",
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      "title": "Print a summary of a lphom-family object",
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