Metadata-Version: 2.3
Name: HydroErr
Version: 2.0.0rc1
Summary: Goodness of fit metrics for use in comparison studies, specifically for use in the field of hydrology
Keywords: hydrology,error,metrics,comparison,statistics,forecast,observed
Author: Wade Roberts, Gustavious Williams
Author-email: Wade Roberts <wade.a.roberts@protonmail.com>, Gustavious Williams <gus.williams@byu.edu>
License: MIT
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Hydrology
Requires-Dist: numpy~=2.0
Requires-Dist: scipy~=1.0
Requires-Python: >=3.10, <=3.15
Project-URL: repository, https://github.com/BYU-Hydroinformatics/HydroErr
Description-Content-Type: text/markdown

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Goodness of fit metrics for use in comparison studies, specifically for use in the field of hydrology

- **Documentation:** https://hydroerr.readthedocs.io/en/stable/
- **Source:** https://github.com/BYU-Hydroinformatics/HydroErr
- **Bug reports:** https://github.com/BYU-Hydroinformatics/HydroErr/issues

HydroErr provides:

- A library of over 70 error metrics to measure hydrologic skill
- Treatment of NaN, Inf, negative, and zero values.
- Scholarly journal sources and code examples for metric methods in the documentation.

Publications:
- Roberts, W., Williams, G., Jackson, E., Nelson, E., Ames, D., 2018. Hydrostats: A Python Package for Characterizing Errors between Observed and Predicted Time Series. Hydrology 5(4) 66, doi:10.3390/hydrology5040066

We would request that if you use this package that you please cite the above paper. The journal paper is open access and can be found at https://doi.org/10.3390/hydrology5040066. BibTeX, EndNote, and RIS files can be downloaded at the journal site.

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