{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"Properscoring provides strictly proper scoring rules for evaluating probabilistic forecasts, including Continuous Ranked Probability Score (CRPS) and Brier Score methods that work with ensemble forecasts, Gaussian distributions, and arbitrary cumulative distribution functions.","skillfed_tags":["forecast-evaluation","proper-scoring-rules","probabilistic-metrics"],"use_cases":["Evaluate weather forecast model accuracy by computing CRPS scores across many observations and comparing to baseline forecasts.","Calculate skill scores to measure forecast improvement relative to a baseline, normalized to a 0\u20131 scale.","Score ensemble forecasts from climate or weather models using finite samples without requiring the underlying distribution.","Compute Brier scores for binary probabilistic predictions or threshold exceedances.","Batch-process large arrays of forecast\u2013observation pairs to generate aggregate performance metrics."],"what_it_does":"Properscoring is a Python library for computing strictly proper scoring rules\u2014evaluation metrics that cannot be artificially improved through hedging\u2014making them fair and reliable for assessing the accuracy of probabilistic forecasts. It implements Continuous Ranked Probability Score (CRPS) and Brier Score in multiple forms: CRPS can be calculated exactly for Gaussian distributions, via numerical integration for arbitrary distributions, from finite ensemble samples, or weighted by probability density values; Brier Score is provided for binary forecasts and threshold exceedances. All functions handle multi-dimensional arrays and missing values (NaN).\n\nThe library was created by researchers at The Climate Corporation and is particularly useful in weather forecasting and other domains where probabilistic predictions need rigorous evaluation. It requires external dependencies and optional Numba support for performance improvements on ensemble-based calculations. However, the package is no longer actively maintained\u2014the last release and commit occurred years ago\u2014so compatibility with recent Python versions may be limited.","worth_installing":"Yes, if you need strictly proper scoring rules for probabilistic forecast evaluation and can tolerate an abandoned package. The core algorithms are scientifically sound and the library has no known vulnerabilities. However, verify compatibility with your Python version before relying on it in production; consider seeking an actively maintained alternative if you need ongoing support or updates."},"id":"properscoring","links":{"html":"https://skillfed.io/packages/properscoring","md":"https://skillfed.io/packages/properscoring.md","pypi":"https://pypi.org/project/properscoring/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2015-11-12","license_spdx":null,"license_treatment":"permissive","name":"properscoring","python_support":"unspecified","summary":"Proper scoring rules in Python"},"popularity":{"monthly_downloads":167190,"position":10478,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1"}