{
  "channels": [
    "conda-forge",
    "defaults"
  ],
  "conda_build_version": "3.15.1",
  "conda_private": false,
  "conda_version": "4.5.11",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "extra": {
    "copy_test_source_files": true,
    "final": true,
    "recipe-maintainers": [
      "johanneskoester",
      "bgruening",
      "daler",
      "jdblischak",
      "cbrueffer",
      "dbast"
    ]
  },
  "home": "http://github.com/pmszulc/bigstep",
  "identifiers": [],
  "keywords": [],
  "license": "GPL-3",
  "license_family": "GPL3",
  "license_file": "C:\\bld\\r-bigstep_1540065156366\\_h_env\\R\\share\\licenses\\GPL-3",
  "root_pkgs": [
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    "pytz 2018.5 py_0",
    "pywin32 223 py36hfa6e2cd_1",
    "pyyaml 3.13 py36hfa6e2cd_1001",
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    "ruamel_yaml 0.15.37 py36hfa6e2cd_2",
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  "summary": "Selecting linear and generalized linear models for large data sets using modified stepwise procedure and modern selection criteria (like modifications of Bayesian Information Criterion). Selection can be performed on data which exceed RAM capacity. Special selection strategy is available, faster than classical stepwise procedure.",
  "tags": []
}