{
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  "Package": "cooccure",
  "Title": "Co-Occurrence Network Construction and Manipulation",
  "Version": "0.4.1",
  "Authors@R": "c(\nperson(\"Mohammed\", \"Saqr\", email = \"saqr@saqr.me\",\nrole = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Sonsoles\", \"López-Pernas\", email = \"sonsoles.lopez@uef.fi\",\nrole = c(\"aut\", \"cph\")),\nperson(\"Kamila\", \"Misiejuk\", role = c(\"aut\", \"cph\")))",
  "Description": "Constructs co-occurrence networks from several types of\ninput data, such as delimited fields, long/bipartite tables,\nbinary matrices, or wide sequences. Returns tidy edge data\nframes and supports optional scaling, splitting into several\nnetworks, thresholding, and subsetting. Provides eight\nsimilarity measures, including Jaccard, cosine, and association\nstrength. Supports export to several network and file formats.\nNetwork construction and analysis methods follow Saqr,\nLopez-Pernas, Conde, and Hernandez-Garcia (2024,\n<doi:10.1007/978-3-031-54464-4_15>).",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Language": "en-US",
  "LazyData": "true",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
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  "URL": "https://github.com/mohsaqr/cooccure",
  "BugReports": "https://github.com/mohsaqr/cooccure/issues",
  "Repository": "https://mohsaqr.r-universe.dev",
  "Date/Publication": "2026-10-08 18:38:18 UTC",
  "RemoteUrl": "https://github.com/mohsaqr/cooccure",
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    "User": "root"
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  "Author": "Mohammed Saqr [aut, cre, cph],\nSonsoles López-Pernas [aut, cph],\nKamila Misiejuk [aut, cph]",
  "Maintainer": "Mohammed Saqr <saqr@saqr.me>",
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  "_created": "2026-10-08T22:21:02.000Z",
  "_published": "2026-10-08T22:25:47.379Z",
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    "message": "Docs links: canonical URL is pak.dynasite.org\n",
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    "email": "saqr@saqr.me",
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    "description": "",
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    "name": "Mohammed Saqr",
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    "description": "Professor of Computer Science"
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  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
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      "date": "2026-04-24"
    },
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    "as_igraph",
    "as_matrix",
    "as_netobject",
    "as_tidygraph",
    "co",
    "cooccurrence",
    "launch_app"
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      "title": "IMDB actor-genre long table (1970-2024)",
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      "class": [
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      ],
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        "genre"
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      "tojson": true
    },
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      "title": "IMDB actor-movie long table (1970-2024)",
      "object": "actors",
      "class": [
        "data.frame"
      ],
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        "actor",
        "tconst",
        "primaryTitle",
        "startYear",
        "decade",
        "genres",
        "averageRating"
      ],
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      "table": true,
      "tojson": true
    },
    {
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      "title": "Demo actor-movie-genre table",
      "object": "demo",
      "class": [
        "data.frame"
      ],
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        "movie",
        "actor",
        "genre"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "movies",
      "title": "IMDB movie metadata (1970-2024)",
      "object": "movies",
      "class": [
        "data.frame"
      ],
      "fields": [
        "tconst",
        "primaryTitle",
        "startYear",
        "genres",
        "decade",
        "averageRating",
        "numVotes"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    }
  ],
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    {
      "page": "actor_genres",
      "title": "IMDB actor-genre long table (1970-2024)",
      "topics": [
        "actor_genres"
      ]
    },
    {
      "page": "actors",
      "title": "IMDB actor-movie long table (1970-2024)",
      "topics": [
        "actors"
      ]
    },
    {
      "page": "as_cograph",
      "title": "Convert to cograph network",
      "topics": [
        "as_cograph",
        "as_cograph.cooccurrence"
      ]
    },
    {
      "page": "as_igraph",
      "title": "Convert to igraph",
      "topics": [
        "as_igraph",
        "as_igraph.cooccurrence"
      ]
    },
    {
      "page": "as_matrix",
      "title": "Extract the co-occurrence matrix",
      "topics": [
        "as_matrix",
        "as_matrix.cooccurrence"
      ]
    },
    {
      "page": "as_netobject",
      "title": "Convert to Nestimate netobject",
      "topics": [
        "as_netobject",
        "as_netobject.cooccurrence"
      ]
    },
    {
      "page": "as_tidygraph",
      "title": "Convert to tidygraph",
      "topics": [
        "as_tidygraph",
        "as_tidygraph.cooccurrence"
      ]
    },
    {
      "page": "as.data.frame.summary.cooccurrence",
      "title": "Tidy node or group table from a cooccurrence summary",
      "topics": [
        "as.data.frame.summary.cooccurrence"
      ]
    },
    {
      "page": "cooccurrence",
      "title": "Build a co-occurrence network",
      "topics": [
        "co",
        "cooccurrence"
      ]
    },
    {
      "page": "demo",
      "title": "Demo actor-movie-genre table",
      "topics": [
        "demo"
      ]
    },
    {
      "page": "launch_app",
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      "topics": [
        "launch_app"
      ]
    },
    {
      "page": "movies",
      "title": "IMDB movie metadata (1970-2024)",
      "topics": [
        "movies"
      ]
    },
    {
      "page": "plot.cooccurrence",
      "title": "Plot a cooccurrence network",
      "topics": [
        "plot.cooccurrence"
      ]
    },
    {
      "page": "print.cooccurrence",
      "title": "Print a cooccurrence edge list",
      "topics": [
        "print.cooccurrence"
      ]
    },
    {
      "page": "print.summary.cooccurrence",
      "title": "Print a cooccurrence summary",
      "topics": [
        "print.summary.cooccurrence"
      ]
    },
    {
      "page": "summary.cooccurrence",
      "title": "Summarize a cooccurrence network",
      "topics": [
        "summary.cooccurrence"
      ]
    }
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        "Comparing similarity measures",
        "Which similarity to use?",
        "2. Counting methods",
        "3. Scaling",
        "4. Filtering",
        "5. Actor co-occurrence (long/bipartite format)",
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        "9. Six input formats, one result",
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        "References"
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