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  "Title": "Statistical Framework to Define Subgroups in Complex Datasets",
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  "Date": "2025-09-12",
  "Authors@R": "c(person(given = \"Song\",\nfamily = \"Gao\",\nrole = \"aut\"),\nperson(given = \"Stefan\",\nfamily = \"Mutter\",\nrole = \"aut\"),\nperson(given = c(\"Aaron\", \"E.\"),\nfamily = \"Casey\",\nrole = \"aut\"),\nperson(given = \"Ville-Petteri\",\nfamily = \"Makinen\",\nrole = c(\"aut\", \"cre\"),\nemail = \"vpmakine@gmail.com\"))",
  "Maintainer": "Ville-Petteri Makinen <vpmakine@gmail.com>",
  "Description": "High-dimensional datasets that do not exhibit a clear\nintrinsic clustered structure pose a challenge to conventional\nclustering algorithms. For this reason, we developed an\nunsupervised framework that helps scientists to better subgroup\ntheir datasets based on visual cues, please see Gao S, Mutter\nS, Casey A, Makinen V-P (2019) Numero: a statistical framework\nto define multivariable subgroups in complex population-based\ndatasets, Int J Epidemiology, 48:369-37,\n<doi:10.1093/ije/dyy113>. The framework includes the necessary\nfunctions to construct a self-organizing map of the data, to\nevaluate the statistical significance of the observed data\npatterns, and to visualize the results.",
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        "###########################################################################",
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