API Library Credits
This page lists the top-level Python packages used by the Brinkpy API. These are documented for software attribution and citation purposes only — they are not installation requirements for the CLI.
Only direct, top-level dependencies are listed — transitive packages installed automatically as dependencies of these libraries are not included.
Preferred Citation Sources
If you need to cite an API dependency, use the preferred references below:
- SciPy: Virtanen, P., Gommers, R., Oliphant, T. E., et al. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17, 261-272. https://doi.org/10.1038/s41592-019-0686-2
- NumPy: Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357-362. https://doi.org/10.1038/s41586-020-2649-2
- Matplotlib: Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science and Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55
- FastAPI: Ramírez, S. FastAPI (software). https://github.com/fastapi/fastapi/tree/master
- pandas: McKinney, W. (2010). Data structures for statistical computing in Python. In Proceedings of the 9th Python in Science Conference, 56-61. https://doi.org/10.25080/Majora-92bf1922-00a
- Pydantic: Colvin, S., et al. Pydantic Validation (software). Citation metadata: https://github.com/pydantic/pydantic/blob/main/CITATION.cff
- scikit-learn: Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830. https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
- ArviZ: Kumar, R., Carroll, C., Hartikainen, A., and Martin, O. (2019). ArviZ a unified library for exploratory analysis of Bayesian models in Python. Journal of Open Source Software, 4(33), 1143. https://doi.org/10.21105/joss.01143
- statsmodels: Seabold, S., and Perktold, J. (2010). statsmodels: Econometric and statistical modeling with python. In Proceedings of the 9th Python in Science Conference. https://proceedings.scipy.org/articles/Majora-92bf1922-011
For the remaining packages, see the project pages below.
Main API Libraries (Top-Level)
algopy: https://pypi.org/project/algopy/arviz: https://www.arviz.org/en/latest/support.html#citearviz-plots: https://pypi.org/project/arviz-plots/auth0-api-python: https://pypi.org/project/auth0-api-python/auth0-fastapi-api: https://pypi.org/project/auth0-fastapi-api/autograd: https://pypi.org/project/autograd/bandit: https://pypi.org/project/bandit/fastapi: https://fastapi.tiangolo.com/fastapi-cli: https://pypi.org/project/fastapi-cli/gitpython: https://pypi.org/project/gitpython/gunicorn: https://pypi.org/project/gunicorn/jinja2: https://jinja.palletsprojects.com/joblib: https://joblib.readthedocs.io/matplotlib: https://matplotlib.org/stable/users/project/citing.htmlnumdifftools: https://numdifftools.readthedocs.io/numpy: https://numpy.org/citing-numpy/orjson: https://pypi.org/project/orjson/pandas: https://pandas.pydata.org/about/citing.htmlpip-chill: https://pypi.org/project/pip-chill/platformdirs: https://pypi.org/project/platformdirs/psutil: https://pypi.org/project/psutil/pydantic: https://docs.pydantic.dev/pytest: https://docs.pytest.org/python-dotenv: https://pypi.org/project/python-dotenv/python-multipart: https://pypi.org/project/python-multipart/redis: https://redis.readthedocs.io/requests: https://requests.readthedocs.io/scikit-learn: https://scikit-learn.org/stable/about.html#citing-scikit-learnscipy: https://scipy.org/citing-scipy/slowapi: https://pypi.org/project/slowapi/statsmodels: https://www.statsmodels.org/stable/#citationstripe: https://pypi.org/project/stripe/tornado: https://pypi.org/project/tornado/tqdm: https://tqdm.github.io/typer: https://typer.tiangolo.com/typer-slim: https://pypi.org/project/typer-slim/uvloop: https://pypi.org/project/uvloop/watchfiles: https://pypi.org/project/watchfiles/websockets: https://pypi.org/project/websockets/xlsxwriter: https://pypi.org/project/xlsxwriter/