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  "Package": "IBclust",
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  "Title": "Information Bottleneck Methods for Clustering Mixed-Type Data",
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  "Authors@R": "c(person(given = \"Angelos\",\nfamily = \"Markos\",\nrole = c(\"aut\", \"cre\"),\nemail = \"amarkos@gmail.com\"),\nperson(given = \"Efthymios\",\nfamily = \"Costa\",\nrole = \"aut\"),\nperson(given = \"Ioanna\",\nfamily = \"Papatsouma\",\nrole = \"aut\"))",
  "Description": "Implements multiple variants of the Information Bottleneck\n('IB') method for clustering datasets containing continuous,\ncategorical (nominal/ordinal) and mixed-type variables. The\npackage provides deterministic, agglomerative, generalised,\nsequential, and standard IB clustering algorithms that preserve\nrelevant information while forming interpretable clusters. The\nDeterministic Information Bottleneck is described in Costa et\nal. (2026) <doi:10.1016/j.patcog.2026.113580>. The standard IB\nmethod originates from Tishby et al. (2000)\n<doi:10.48550/arXiv.physics/0004057>, the agglomerative variant\nfrom Slonim and Tishby (1999)\n<https://papers.nips.cc/paper/1651-agglomerative-information-bottleneck>,\nthe generalised IB from Strouse and Schwab (2017)\n<doi:10.1162/NECO_a_00961>, and the sequential IB from Slonim\net al. (2002) <doi:10.1145/564376.564401>. Diagnostic and\nplotting functions are provided to summarise, visualise, and\npredict from the resulting clusterings.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
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  "Date/Publication": "2026-08-25 12:51:49 UTC",
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      "title": "IBclust: Information Bottleneck Clustering for Mixed-Type Data",
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        "IBclust"
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        "fitted.aibclust",
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}