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We're happy to announce the release of GraSPy 0.3! GraSPy is a Python package for
understanding the properties of random graphs that arise from modern datasets, such as
social networks and brain networks.
For more information, please visit our [website](http://graspy.neurodata.io/) and our [tutorials](https://graspy.neurodata.io/tutorial.html)
Highlights
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This release is the result of over 5 months of work with over 11 pull requests by
7 contributors. Highlights include:
- Added seeded graph matching as a capability for graph matching, renamed graph matching class to ``GraphMatch``
- Added functions for simulating a pair of correlated RDPG graphs.
- Deprecated Python 3.5
- Added different backend hypothesis tests for the ``LatentDistributionTest`` from Hyppo
- Added a correction to make ``LatentDistributionTest`` valid for differently sized graphs
Improvements
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- Updated default value of ``rescale`` in RDPG simulation
- Updated default value of ``scaled`` in MASE estimation
- Improved error throwing in ``AutoGMM``
- Clarified the API for ``inference`` submodule
API Changes
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- ``FastApproximateQAP`` was renamed to ``GraphMatch``
- ``fit`` method of ``LatentDistributionTest`` and ``LatentPositionTest`` now returns self instead of a p-value
Deprecations
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- Python 3.5
Contributors to this release
----------------------------
- [Jaewon Chung](https://github.com/j1c)
- [Benjamin Pedigo](https://github.com/bdpedigo)
- [Ali Saad-Eldin](https://github.com/asaadeldin11)
- [Shan Qiu](https://github.com/SHAAAAN)
- [Bijan Varjavand](https://github.com/bvarjavand)
- [Anton Alyakin](https://github.com/alyakin314) (new contributor!)
- [Casey Weiner](https://github.com/caseypw) (new contributor!)