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skggm2

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0.2.10 skggm2-0.2.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
skggm2-0.2.10-cp39-cp39-macosx_11_0_x86_64.whl
skggm2-0.2.10-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Wheel Details

Project: skggm2
Version: 0.2.10
Filename: skggm2-0.2.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Download: [link]
Size: 3385845
MD5: bb03a55f453db42895f24fff14c8067b
SHA256: e4ea9bcb27820521aab1125a3328e9737ffaa3d2f1c2450f0758a8b4d0e881e9
Uploaded: 2023-03-23 11:21:49 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: skggm2
Version: 0.2.10
Summary: Gaussian graphical models for scikit-learn.
Author: Jason Laska and Manjari Narayan
Author-Email: jlaska[at]gmail.com
Home-Page: https://github.com/skggm/skggm
License: MIT
Requires-Dist: Cython (>=0.24)
Requires-Dist: nilearn (>=0.2.4)
Requires-Dist: numpy (>=1.12.1)
Requires-Dist: scikit-learn (>=1.1.13)
Requires-Dist: scipy (>=0.23.0)
Requires-Dist: pytest (>=2.9.2)
Requires-Dist: seaborn (>=0.7.1)
Requires-Dist: nose (>=1.3.6)
Requires-Dist: tabulate (==0.7.5)
Requires-Dist: joblib (>=0.16.0)
License-File: LICENSE
[No description]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.40.0)
Root-Is-Purelib: false
Tag: cp39-cp39-manylinux_2_17_x86_64
Tag: cp39-cp39-manylinux2014_x86_64

RECORD

Path Digest Size
inverse_covariance/rank_correlation.py sha256=obc_hq8k174hMyN4UwndKmSQbNvSNlFGxDfTCS8GZII 5352
inverse_covariance/adaptive_graph_lasso.py sha256=ZhdFv9QkuIl3gk6QZU_rFfn9fu3ozmeODG2aUmX-GA0 5065
inverse_covariance/model_average.py sha256=vt0jSzHgM5qMwVzrKNB0fLY3SzPSpHp3jNhXLtIVo7o 15099
inverse_covariance/__init__.py sha256=IR4rB4OOrLqR0VCz1mbNda-y_7fnsglAhoW3uy5d-Bo 967
inverse_covariance/plot_util.py sha256=YxzStPPW3ypIw3xLr18NN2_dxINLoZ-tir4ofLAjL2g 4268
inverse_covariance/quic_graph_lasso.py sha256=RF4MxBTcBsgn8SDVZQnhsn-Kzi2yzjXvA6osn9T6Hww 33223
inverse_covariance/inverse_covariance.py sha256=AgXzgWUaLbH2P73Mf9BB85XmfOojmGqfJ7dIlB4NjgU 11606
inverse_covariance/metrics.py sha256=sYA7XbqBmNi1oFH5xlZ2lF9tql0HtRDWQ1q6iVzeTPs 3784
inverse_covariance/profiling/erdos_renyi_graph.py sha256=7ZUsIP_2d6Tc25K9QTswCuU4CiEJRlLB0ssBdrSpFI0 1745
inverse_covariance/profiling/__init__.py sha256=GAb90rF9tKwaqhaQ08QlpnMCHyJbvRm2cm9bzaI4dZA 763
inverse_covariance/profiling/monte_carlo_profile.py sha256=Z-6GfS4kjJGuIy2q6Ek6VgRCY5cdLdTc9j-sCZKBXc4 10405
inverse_covariance/profiling/lattice_graph.py sha256=uyx2nd9v-xid6ecbGLluCis8tDynOIsIjvx5NetA-_k 2076
inverse_covariance/profiling/cluster_graph.py sha256=YIPYjf5SNWPsvtGDhxHuCQcfLJRWwcq9PqyNzqryj48 1759
inverse_covariance/profiling/graphs.py sha256=GqDYokQLAupikFQlwzvz5vpmyE52G5VFyh-DDXC8O8c 6616
inverse_covariance/profiling/metrics.py sha256=JUTolqmG0lw8ptZQj-C9D0AYUcFY7o5aw5dNbNFjn3A 2996
inverse_covariance/pyquic/__init__.py sha256=FW7LufzTzIoOy-U5Q7h84z8aO_6wbNJ0CmEyTdBaZ3g 41
inverse_covariance/pyquic/setup.py sha256=v43nWu2VPLR5qbRYCuStYYa9yuwVRyMPD7SVnjyw5Vo 938
inverse_covariance/pyquic/pyquic.cpython-39-x86_64-linux-gnu.so sha256=0wuLfl6d6hq2oCDHgvhA1U6JhrlH1ebH3eplQz6OnsI 276705
skggm2.libs/liblapack-1ad85175.so.3.4.2 sha256=wrfEW65bG_XaXB2gB_JZR24uf8UxvWTrvVe6nIqfwu4 5682809
skggm2.libs/libquadmath-96973f99.so.0.0.0 sha256=k0wi3tDn0WnE1GeIdslgUa3z2UVF2pYvYLQWWbB12js 247609
skggm2.libs/libgfortran-91cc3cb1.so.3.0.0 sha256=VePrZzBsL_F-b4oIEOqg3LJulM2DkkxQZdUEDoeBRgg 1259665
skggm2.libs/libblas-357956a1.so.3.4.2 sha256=eLqolYX0nkk2PP64pkqa0Nif8hllaovrJMGbx8jU5W0 374265
skggm2-0.2.10.dist-info/LICENSE sha256=WPlqSgP6sS9exuZMeV866kQEwxC4QQ0jjnvrLtLv5ig 1087
skggm2-0.2.10.dist-info/WHEEL sha256=gREe7-l-MJWbGZG46A7WHnwwUSxA3XJYHQvGGLzmBNU 148
skggm2-0.2.10.dist-info/top_level.txt sha256=yVLH9GC5EI5vh9Jfvqh4MBQfDxbqc1xEftg1e8cGlPw 26
skggm2-0.2.10.dist-info/RECORD — —
skggm2-0.2.10.dist-info/METADATA sha256=G_k_I1zdedXLNMGpi_MoIhV7mIwdxIw39ltCFSArnPI 609

top_level.txt

inverse_covariance
pyquic