Metadata-Version: 2.4
Name: derivkit
Version: 1.2.0
Summary: A robust toolkit for stable numerical derivatives
Author-email: Nikolina Šarčević <nikolina.sarcevic@gmail.com>, Matthijs van der Wild <matthijs@vanderwild.com>, Cynthia Trendafilova <cyntrendafilova@gmail.com>
License-Expression: MIT
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numdifftools
Requires-Dist: numpy
Requires-Dist: multiprocess
Requires-Dist: scipy
Requires-Dist: getdist
Requires-Dist: emcee
Provides-Extra: jax
Requires-Dist: jax>=0.4; extra == "jax"
Requires-Dist: jaxlib>=0.4; extra == "jax"
Dynamic: license-file

# derivkit

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**DerivKit** is a robust Python toolkit for stable numerical derivatives, built for scientific computing, cosmology, and any domain requiring accurate gradients or higher-order expansions.

Detailed documentation, examples, and API reference can be found at the [derivkit documentation](https://docs.derivkit.org).


## Citation

If you use **DerivKit** in your research, please cite the associated paper:

```bibtex
@misc{sarcevic2026derivkitstablenumericalderivatives,
  title        = {DerivKit: stable numerical derivatives bridging Fisher forecasts and MCMC},
  author       = {Šarčević, Nikolina and van der Wild, Matthijs and Trendafilova, Cynthia},
  year         = {2026},
  eprint       = {2602.08078},
  archivePrefix= {arXiv},
  primaryClass = {astro-ph.IM},
  url          = {https://arxiv.org/abs/2602.08078}
}
```


## License
MIT License © 2025 Niko Šarčević, Matthijs van der Wild, Cynthia Trendafilova and contributors.
