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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",
  "RemoteUrl": "https://github.com/s-kganz/scutr",
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  "Packaged": {
    "Date": "2026-05-13 07:13:55 UTC",
    "User": "root"
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  "Author": "Keenan Ganz [aut, cre]",
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  "_user": "s-kganz",
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    "author": "Keenan Ganz <38630604+s-kganz@users.noreply.github.com>",
    "committer": "GitHub <noreply@github.com>",
    "message": "Merge pull request #2 from s-kganz/cran-release\n\nSubmit v0.2.0 to cran",
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    "name": "Keenan Ganz",
    "description": "PhD Student @ Remote Sensing and Geospatial Analysis Laboratory, UW Seattle"
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    "undersample_hclust",
    "undersample_kmeans",
    "undersample_mclust",
    "undersample_mindist",
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      ],
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        "y",
        "minor"
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      "title": "An imbalanced dataset with randomly placed normal distributions around the origin. The nth class has n * 10 observations.",
      "object": "imbalance",
      "class": [
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      ],
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        "V2",
        "V3",
        "V4",
        "V5",
        "V6",
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    },
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      "title": "Type and chemical analysis of three different kinds of wine.",
      "object": "wine",
      "class": [
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        "x8",
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        "x13"
      ],
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      "table": true,
      "tojson": true
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      ]
    },
    {
      "page": "imbalance",
      "title": "An imbalanced dataset with randomly placed normal distributions around the origin. The nth class has n * 10 observations.",
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      ]
    },
    {
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      "title": "Oversample a dataset by SMOTE.",
      "topics": [
        "oversample_smote"
      ]
    },
    {
      "page": "resample_random",
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      "topics": [
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      ]
    },
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      "page": "sample_classes",
      "title": "Stratified index sample of different values in a vector.",
      "topics": [
        "sample_classes"
      ]
    },
    {
      "page": "SCUT",
      "title": "SMOTE and cluster-based undersampling technique.",
      "topics": [
        "SCUT",
        "SCUT_parallel"
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      "topics": [
        "undersample_hclust"
      ]
    },
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      "page": "undersample_kmeans",
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        "undersample_kmeans"
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      "topics": [
        "undersample_mindist"
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    },
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      "topics": [
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      ]
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