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  "Package": "scutr",
  "Title": "Balancing Multiclass Datasets for Classification Tasks",
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  "Authors@R": "person(given = \"Keenan\",\nfamily = \"Ganz\",\nrole = c(\"aut\", \"cre\"),\nemail = \"ganzkeenan1@gmail.com\")",
  "Maintainer": "Keenan Ganz <ganzkeenan1@gmail.com>",
  "Description": "Imbalanced training datasets impede many popular\nclassifiers. To balance training data, a combination of\noversampling minority classes and undersampling majority\nclasses is useful. This package implements the SCUT (SMOTE and\nCluster-based Undersampling Technique) algorithm as described\nin Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their\npaper uses model-based clustering and synthetic oversampling to\nbalance multiclass training datasets, although other resampling\nmethods are provided in this package.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "URL": "https://github.com/s-kganz/scutr",
  "BugReports": "https://github.com/s-kganz/scutr/issues",
  "Config/testthat/edition": "2",
  "Config/pak/sysreqs": "libglpk-dev libxml2-dev",
  "Repository": "https://s-kganz.r-universe.dev",
  "Date/Publication": "2023-11-18 18:34:36 UTC",
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    "Date": "2026-07-12 07:30:15 UTC",
    "User": "root"
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  "Author": "Keenan Ganz [aut, cre]",
  "_user": "s-kganz",
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        "y",
        "minor"
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      "class": [
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    },
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      "topics": [
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      "page": "SCUT",
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        "SCUT_parallel"
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