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arboreto

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0.1.6 arboreto-0.1.6-py2.py3-none-any.whl

Wheel Details

Project: arboreto
Version: 0.1.6
Filename: arboreto-0.1.6-py2.py3-none-any.whl
Download: [link]
Size: 15504
MD5: eb58a93dc468b7145743a477ff5bc9a6
SHA256: 6c70074b9d7273efaed0f89dd508c886b83c22ef81ae07ca923b7d21e7bbd057
Uploaded: 2021-02-09 11:20:19 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: arboreto
Version: 0.1.6
Summary: Scalable gene regulatory network inference using tree-based ensemble regressors
Author: Thomas Moerman
Author-Email: thomas.moerman[at]gmail.com
Home-Page: https://github.com/aertslab/arboreto
License: BSD 3-Clause License
Keywords: gene,regulatory,network,inference,regression,ensemble,scalable,dask
Platform: any
Requires-Dist: dask[complete]
Requires-Dist: distributed
Requires-Dist: numpy (>=1.16.5)
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scipy
[Description omitted; length: 4851 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.34.2)
Root-Is-Purelib: true
Tag: py2-none-any
Tag: py3-none-any

RECORD

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arboreto/algo.py sha256=rSpHvqIkNBXEyEEalWZV2nLFyhfp5zR3LFAGpCrG9gM 9518
arboreto/core.py sha256=ftPMXXyFJlcg3qUzKGfD0A_zwkzCxOjzexgO9j7J910 20280
arboreto/utils.py sha256=Ub77EAs8bew3CWyOKDsNYH9TeMbZuzGfwj9F-vjNoSM 325
tests/__init__.py sha256=u8rAsNRgfaTMjXjgLWeDdnmgQ5iTQphQYO1Fe4UprWU 106
tests/test_algo.py sha256=erORxLQFfBi7qkwkcd2yNtH5FLGbbMwmNGRScL0u69k 3301
tests/test_core.py sha256=J4BBdZjA1jbrKDNx9v6RQ9W3J0sd1p55-zULeF6syHU 9668
arboreto-0.1.6.dist-info/LICENSE.txt sha256=F2dxX5SNa1qSyvEIl-HZOnGn2k-uBrDPjXnh341NeGQ 1514
arboreto-0.1.6.dist-info/METADATA sha256=1Mqh-s6X5EJDkFYXQqt0TU3vFLrynRRk_gsRWks73ZY 5387
arboreto-0.1.6.dist-info/WHEEL sha256=kGT74LWyRUZrL4VgLh6_g12IeVl_9u9ZVhadrgXZUEY 110
arboreto-0.1.6.dist-info/top_level.txt sha256=DT5t-syvZdS9Ow4jLJ00NcRqYGJrTbtm6y_NCJQDBwo 15
arboreto-0.1.6.dist-info/RECORD — —

top_level.txt

arboreto
tests