{ "_id": "6a27b3a224555f66ed538019", "Package": "cooccure", "Title": "Co-Occurrence Network Construction and Manipulation", "Version": "0.1.2", "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).", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-US", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "URL": "https://github.com/mohsaqr/cooccure", "BugReports": "https://github.com/mohsaqr/cooccure/issues", "Repository": "https://mohsaqr.r-universe.dev", "Date/Publication": "2026-05-10 21:16:26 UTC", "RemoteUrl": "https://github.com/mohsaqr/cooccure", "RemoteRef": "HEAD", "RemoteSha": "a978aa3708b8ac02957f19fb832ea89ce650458e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-09 05:57:56 UTC", "User": "root" }, "Author": "Mohammed Saqr [aut, cre, cph],\nSonsoles López-Pernas [aut, cph],\nKamila Misiejuk [aut, cph]", "Maintainer": "Mohammed Saqr ", "MD5sum": "6f551640a220ff0276bddacf26f281d0", "_user": 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"package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "graphics", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "igraph", "role": "Suggests" }, { "package": "cograph", "role": "Suggests" }, { "package": "Nestimate", "role": "Suggests" }, { "package": "tidygraph", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "shiny", "role": "Suggests" }, { "package": "DT", "role": "Suggests" } ], "_owner": "mohsaqr", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-16", "n": 39 }, { "week": "2026-17", "n": 19 }, { "week": "2026-18", "n": 4 }, { "week": "2026-19", "n": 1 } ], "_tags": [], "_stars": 1, "_contributors": [ { "user": "mohsaqr", "count": 60, "uuid": 194697827 }, { "user": "sonsoleslp", "count": 14, "uuid": 10948559 }, { "user": "kamilamisiejuk", "count": 4, "uuid": 33844198 } ], "_userbio": { "uuid": 194697827, "type": "user", "name": "Mohammed Saqr", "description": "Professor of Computer Science" }, "_downloads": { "count": 506, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/cooccure" }, "_devurl": "https://github.com/mohsaqr/cooccure", "_searchresults": 0, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/cooccure.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/mohsaqr/cooccure", "_realowner": "mohsaqr", "_cranurl": true, "_releases": [ { "version": "0.1.1", "date": "2026-04-24" } ], "_exports": [ "as_cograph", "as_igraph", "as_matrix", "as_netobject", "as_tidygraph", "co", "cooccurrence", "launch_app" ], "_datasets": [ { "name": "actor_genres", "title": "IMDB actor-genre long table (1970-2024)", "object": "actor_genres", "class": [ "data.frame" ], "fields": [ "actor", "genre" ], "rows": 2502, "table": true, "tojson": true }, { "name": "actors", "title": "IMDB actor-movie long table (1970-2024)", "object": "actors", "class": [ "data.frame" ], "fields": [ "actor", "tconst", "primaryTitle", "startYear", "decade", "genres", "averageRating" ], "rows": 1267, "table": true, "tojson": true }, { "name": "demo", "title": "Demo actor-movie-genre table", "object": "demo", "class": [ "data.frame" ], "fields": [ "movie", "actor", "genre" ], "rows": 34, "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 } ], "_help": [ { "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": "cooccurrence", "title": "Build a co-occurrence network", "topics": [ "co", "cooccurrence" ] }, { "page": "demo", "title": "Demo actor-movie-genre table", "topics": [ "demo" ] }, { "page": "launch_app", "title": "Launch the cooccure Shiny explorer", "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": "summary.cooccurrence", "title": "Summarise a cooccurrence network", "topics": [ "summary.cooccurrence" ] } ], "_pkglogo": "https://github.com/mohsaqr/cooccure/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/mohsaqr/cooccure/raw/HEAD/README.md", "_rundeps": [ "lattice", "Matrix" ], "_vignettes": [ { "source": "imdb-tutorial.Rmd", "filename": "imdb-tutorial.html", "title": "Co-occurrence Networks with IMDB Movie Data", "engine": "knitr::rmarkdown", "headings": [ "Data", "1. Genre co-occurrence (delimited field)", "Comparing similarity measures", "Which similarity to use?", "2. Counting methods", "3. Scaling", "4. Filtering", "5. Actor co-occurrence (long/bipartite format)", "6. Splitting by groups", "7. Output formats", "Gephi", "cograph (with Gephi layout)", "igraph", "Matrix", "8. Converters", "9. Six input formats, one result", "10. 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