`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities. In addition to supporting the Earth system science communities, it also has wide potential application in machine learning and other domains such as economics. `scores` not only includes common scores (e.g. Mean Absolute Error), it also includes novel scores not commonly found elsewhere (e.g. FIxed Risk Multicategorical (FIRM) score, Flip-Flop Index), complex scores (e.g. threshold-weighted continuous ranked probability score), and statistical tests (such as the Diebold Mariano test). It also contains isotonic regression which is becoming an increasingly important tool in forecast verification and can be used to generate stable reliability diagrams. Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools. All of the scores and statistical techniques in this package have undergone a thorough scientific and software review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice. `scores` primarily supports `xarray` datatypes for Earth system data, allowing it to work with NetCDF4, HDF5, Zarr and GRIB data sources among others. `scores` uses Dask for scaling and performance. It has expanding support for `pandas`. The software repository can be found at https://github.com/nci/scores/

翻译：`scores`是一个包含数学函数的Python包，用于预测、预报或模型的验证、评估和优化。该包主要支持地球科学领域，特别是气象学、气候学和海洋学领域。除支持地球系统科学领域外，它在机器学习以及经济学等其他领域也具有广泛的应用潜力。`scores`不仅包含常见的评分指标（如平均绝对误差），还包含其他工具中不常见的创新评分（如固定风险多类别评分、翻转指数）、复杂评分（如阈值加权连续排名概率评分）以及统计检验（如Diebold Mariano检验）。该包还包含等渗回归方法——该方法正成为预报验证中日益重要的工具，可用于生成稳定的可靠性图。此外，它还提供预处理工具，用于准备各种格式的数据以计算评分，包括累积分布函数。截至撰写本文时，`scores`包含超过50个指标、统计技术和数据处理工具。该包中的所有评分和统计技术均经过严格的科学和软件审查。每个评分都配有Jupyter Notebook教程，演示其实际应用。`scores`主要支持地球系统数据的`xarray`数据类型，可处理NetCDF4、HDF5、Zarr和GRIB等数据源，并使用Dask进行扩展和性能优化，同时也在扩展对`pandas`的支持。软件仓库地址为：https://github.com/nci/scores/