vineknockoffs: Vine copula based knockoffs ========================================== |Unit tests| |codecov| |Python version| .. |Unit tests| image:: https://github.com/MalteKurz/vineknockoffs/actions/workflows/unitest.yml/badge.svg :target: https://github.com/MalteKurz/vineknockoffs/actions/workflows/unitest.yml .. |PyPI version| image:: https://badge.fury.io/py/vineknockoffs.svg :target: https://badge.fury.io/py/vineknockoffs .. |codecov| image:: https://codecov.io/gh/MalteKurz/vineknockoffs/branch/main/graph/badge.svg?token=E3O3ZOLLBE :target: https://codecov.io/gh/MalteKurz/vineknockoffs .. |Python version| image:: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue :target: https://www.python.org The repo ``_ contains an implementation of vine copula knockoffs for high-dimensional controlled variable selection, see `Kurz (2022) `_ for details. Main features ------------- The python package vineknockoffs can be used to estimate (for details see `Kurz (2022) `_) - Gaussian knockoff models, - Gaussian copula knockoffs models, - Vine copula knockoff models. Citation -------- If you use the vineknockoffs package a citation is highly appreciated: Kurz, M. S. (2022). Vine copula based knockoff generation for high-dimensional controlled variable selection, arXiv:`2210.11196 `_. .. code-block:: TeX @misc{Kurz2022vineknockoffs, title={Vine copula based knockoff generation for high-dimensional controlled variable selection}, author={M. S. Kurz}, year={2022}, eprint={2210.11196}, archivePrefix={arXiv}, primaryClass={stat.ME}, note={arXiv:\href{https://arxiv.org/abs/2210.11196}{2210.11196} [stat.ME]} } Acknowledgements ---------------- Funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) is acknowledged – Project Number 431701914. References ---------- Kurz, M. S. (2022). Vine copula based knockoff generation for high-dimensional controlled variable selection, arXiv:`2210.11196 `_. .. toctree:: :hidden: intro api