{
  "channels": [
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  "conda_build_version": "3.18.7",
  "conda_private": false,
  "conda_version": "4.7.8",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "extra": {
    "copy_test_source_files": true,
    "final": true,
    "recipe-maintainers": [
      "conda-forge/r"
    ]
  },
  "home": "https://CRAN.R-project.org/package=flexclust",
  "identifiers": [],
  "keywords": [],
  "license": "GPL-2",
  "license_family": "GPL2",
  "license_file": "D:\\bld\\r-flexclust_1563468597993\\_h_env/lib/R/share/licenses/GPL-2",
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    "pywin32 224 py36hfa6e2cd_1000",
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    "vc 14.1 h0510ff6_4",
    "vs2008_express_vc_python_patch 1.0.0 0",
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  ],
  "summary": "The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.",
  "tags": []
}