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Merge pull request #84 from Quantmetry/dev
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.github/workflows/test.yml

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run: |
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mypy qolmat
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echo you should uncomment mypy qolmat and delete this line
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- name: Upload coverage reports to Codecov
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uses: codecov/codecov-action@v3
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env:
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CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

.gitignore

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# documentation specific
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docs/_build/
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docs/generated/
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docs/examples/tutorials/
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# Distribution / packaging
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.pre-commit-config.yaml

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rev: v1.1.1
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hooks:
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- id: mypy
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args: [--ignore-missing-imports]
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additional_dependencies: [types-requests]

AUTHORS.rst

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Credits
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=======
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Development Lead
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Development Team
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----------------
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* Julien Roussel <[email protected]>
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Maintainers
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------------
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* Mikail Duran <[email protected]>
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* Anh Khoa Ngo Ho <[email protected]>
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* Charles-Henri Prat <[email protected]>
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* Guillaume Saës <[email protected]>
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Contributors
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------------
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Past Contributors
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-----------------
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2016
* Hong-Lan Botterman
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* Nicolas Brunel
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* Firas Dakhli
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* Mikaïl Duran
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* Rima Hajou
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* Vianey Taquet
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* Thomas Morzadec

HISTORY.rst

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History
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0.1.0 (2023-10-11)
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-------------------
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* VAR(p) EM sampler implemented, founding on a VAR(p) modelization such as the one described in `Lütkepohl (2005) New Introduction to Multiple Time Series Analysis`
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* EM and RPCA matrices transposed in the low-level impelmentation, however the API remains unchanged
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* Sparse matrices introduced in the RPCA implementation so as to speed up the execution
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* Implementation of SoftImpute, which provides a fast but less robust alterantive to RPCA
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* Implementation of TabDDPM and TsDDPM, which are diffusion-based models for tabular data and time-series data, based on Denoising Diffusion Probabilistic Models. Their implementations follow the work of Tashiro et al., (2021) and Kotelnikov et al., (2023).
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* ImputerDiffusion is an imputer-wrapper of these two models TabDDPM and TsDDPM.
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* Docstrings and tests improved for the EM sampler
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* Fix ImputerPytorch
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* Update Benchmark Deep Learning
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0.0.15 (2023-08-03)
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