{
  "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"
    ]
  },
  "home": "https://CRAN.R-project.org/package=DDRTree",
  "identifiers": [],
  "keywords": [],
  "license": "Artistic 2.0",
  "license_family": "OTHER",
  "root_pkgs": [
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    "cryptography 2.7 py36hb32ad35_0",
    "decorator 4.4.0 py_0",
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    "ipython_genutils 0.2.0 py_1",
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    "python 3.6.7 he025d50_1005",
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    "python-libarchive-c 2.8 py36_1004",
    "pytz 2019.1 py_0",
    "pywin32 224 py36hfa6e2cd_1000",
    "pyyaml 5.1.1 py36hfa6e2cd_0",
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    "soupsieve 1.9.2 py36_0",
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    "vc 14.1 h0510ff6_4",
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    "vs2015_runtime 14.15.26706 h3a45250_0",
    "wheel 0.33.4 py36_0",
    "wincertstore 0.2 py36_1002",
    "win_inet_pton 1.1.0 py36_0",
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    "zstd 1.4.0 hd8a0e53_0"
  ],
  "summary": "Provides an implementation of the framework of reversed graph embedding (RGE) which\nprojects data into a reduced dimensional space while constructs a principal tree\nwhich passes through the middle of the data simultaneously. DDRTree shows superiority\nto alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic\nstructure of the single cell genomics data. In general, it could be used to reconstruct\nthe temporal progression as well as bifurcation structure of any datatype.\n",
  "tags": []
}