{
  "channels": [
    "conda-forge",
    "defaults"
  ],
  "conda_build_version": "3.18.8",
  "conda_private": false,
  "conda_version": "4.7.10",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "extra": {
    "copy_test_source_files": true,
    "final": true,
    "recipe-maintainers": [
      "conda-forge/r",
      "philip-khor"
    ]
  },
  "home": "https://github.com/grf-labs/grf",
  "identifiers": [],
  "keywords": [],
  "license": "GPL-3",
  "license_family": "GPL3",
  "license_file": "D:\\bld\\r-grf_1563689603859\\_h_env/lib/R/share/licenses/GPL-3",
  "root_pkgs": [
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    "python 3.6.7 he025d50_1005",
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    "pytz 2019.1 py_0",
    "pywin32 224 py36hfa6e2cd_1000",
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    "vc 14.1 h0510ff6_4",
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    "wheel 0.33.4 py36_0",
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  ],
  "summary": "A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables). This package is currently in beta, and we expect to make continual improvements to its performance and usability.",
  "tags": []
}