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  "Title": "Estimation and Simulation of Multi-Binary Response Models",
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  "Authors@R": "c(\nperson(\"George\", \"Vega Yon\", role=c(\"aut\", \"cre\"),\nemail=\"g.vegayon@gmail.com\", comment = c(ORCID = \"0000-0002-3171-0844\")),\nperson(\n\"Department of Veterans Affairs - Rehabilitation, Research, and Development Service\",\nrole = \"fnd\",\ncomment = \"Award/W81XWH-18-PH/TBIRP-LIMBIC under Award No. I01 RX003443\"\n),\nperson(\n\"U.S. Army Medical Research Acquisition Activity\", role = \"fnd\",\ncomment = \"ORION project, Award #W81XWH1910615\")\n)",
  "Description": "Multi-binary response models are a class of models that\nallow for the estimation of multiple binary outcomes\nsimultaneously. This package provides functions to estimate and\nsimulate these models using the Discrete Exponential-Family\nModels [DEFM] framework. In it, we implement the models\ndescribed in Vega Yon, Valente, and Pugh (2023)\n<doi:10.48550/arXiv.2211.00627>. DEFMs include\nExponential-Family Random Graph Models [ERGMs], which\ncharacterize graphs using sufficient statistics, which is also\nthe core of DEFMs. Using sufficient statistics, we can describe\nthe data through meaningful motifs, for example, transitions\nbetween different states, joint distribution of the outcomes,\netc.",
  "URL": "https://github.com/UofUEpiBio/defm,\nhttps://uofuepibio.github.io/defm/",
  "BugReports": "https://github.com/UofUEpiBio/defm/issues",
  "License": "MIT + file LICENSE",
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  "Author": "George Vega Yon [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-3171-0844>), Department of\nVeterans Affairs - Rehabilitation, Research, and Development\nService [fnd] (Award/W81XWH-18-PH/TBIRP-LIMBIC under Award No.\nI01 RX003443), U.S. Army Medical Research Acquisition Activity\n[fnd] (ORION project, Award #W81XWH1910615)",
  "Maintainer": "George Vega Yon <g.vegayon@gmail.com>",
  "Repository": "https://cranhaven.r-universe.dev",
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    "get_counters",
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    "get_X_names",
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    "logodds",
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    "ncol_defm_y",
    "new_defm",
    "nobs_defm",
    "nrow_defm",
    "nterms_defm",
    "print_stats",
    "rule_constrain_support",
    "rule_not_one_to_zero",
    "set_counter_info",
    "set_counters_names",
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    "summary_table",
    "td_formula",
    "td_generic",
    "td_logit_intercept",
    "td_ones",
    "texreg_fancy"
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      "name": "valentesns",
      "title": "Valente's SNS data",
      "object": "valentesns",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "year",
        "Hispanic",
        "Female",
        "Grades",
        "tobacco",
        "alcohol",
        "mj",
        "sibsmoke",
        "sibdrink",
        "adultdrink",
        "year_value",
        "present",
        "school",
        "has_sib",
        "exposure_smoke",
        "exposure_drink",
        "exposure_mj"
      ],
      "rows": 1730,
      "table": true,
      "tojson": true
    },
    {
      "name": "valentesnsList",
      "title": "Valente's SNS data",
      "object": "valentesnsList",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
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  "_help": [
    {
      "page": "DEFM",
      "title": "Discrete Exponential Family Model (DEFM)",
      "topics": [
        "DEFM",
        "defm",
        "init_defm",
        "morder_defm",
        "ncol_defm_x",
        "ncol_defm_y",
        "new_defm",
        "new_defm_cpp",
        "nobs_defm",
        "nrow_defm",
        "nterms_defm",
        "print_stats"
      ]
    },
    {
      "page": "defm_terms",
      "title": "Model specification for DEFM",
      "topics": [
        "+.DEFM",
        "defm_terms",
        "rule_constrain_support",
        "rule_not_one_to_zero",
        "td_formula",
        "td_generic",
        "td_logit_intercept",
        "td_ones",
        "terms_defm"
      ]
    },
    {
      "page": "defm-names",
      "title": "Access to the names of a model's datasets",
      "topics": [
        "defm-names",
        "get_X_names",
        "get_Y_names"
      ]
    },
    {
      "page": "get_counters",
      "title": "Extract the counters from a DEFM model",
      "topics": [
        "as.list.DEFM_counter",
        "as.list.DEFM_counters",
        "get_counters",
        "length.DEFM_counters",
        "set_counters_names",
        "set_counters_names.DEFM",
        "set_counters_names.DEFM_counters",
        "set_counter_info",
        "[.DEFM_counters"
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      "topics": [
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      ]
    },
    {
      "page": "loglike_defm",
      "title": "Log-Likelihood of DEFM",
      "topics": [
        "loglike_defm"
      ]
    },
    {
      "page": "defm_mle",
      "title": "Maximum Likelihood Estimation of DEFM",
      "topics": [
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        "logodds",
        "summary_table",
        "texreg_fancy"
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        "motif_census"
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    },
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      "title": "Simulate data using a DEFM",
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      ]
    },
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      "page": "valentesns",
      "title": "Valente's SNS data",
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