{"library":"properscoring","title":"Properscoring","description":"A Python library for evaluating probabilistic forecasts using proper scoring rules. Version 0.1 is the only release; the project is in maintenance mode with no recent updates. It provides implementations of the Continuous Ranked Probability Score (CRPS), Brier score, and related metrics for forecast verification.","language":"python","status":"maintenance","last_verified":"Fri May 01","install":{"commands":["pip install properscoring"],"cli":null},"imports":["from properscoring import crps_ensemble","from properscoring import crps_gaussian"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nfrom properscoring import crps_ensemble\n\n# Ensemble forecasts (10 members)\nensemble = np.random.randn(10, 100)\n# Observations\nobservations = np.random.randn(100)\n\n# Calculate CRPS for each observation\ncrps = crps_ensemble(observations, ensemble)\nprint(crps.mean())","lang":"python","description":"Compute the Continuous Ranked Probability Score (CRPS) for ensemble forecasts against observations.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}