{"library":"moocore","type":"library","category":null,"description":"moocore provides fast implementations of core mathematical functions and algorithms for multi-objective optimization. While available in R, this entry focuses on the Python package (v0.2.0). It offers functionalities for generating and transforming non-dominated sets, identifying dominated vectors, and computing various quality metrics like hypervolume and epsilon indicator. The critical functionality is implemented in C for high performance. The project maintains a frequent release cadence, often with minor updates.","language":"python","status":"active","version":"0.2.0","tags":["multi-objective optimization","optimization","algorithms","evolutionary computation","hypervolume","empirical attainment function"],"install":[{"cmd":"pip install moocore","imports":["import moocore","import moocore\npoints = moocore.filter_dominated(data)","import moocore\nhv_calculator = moocore.Hypervolume(reference=ref_point)"]}],"homepage":"https://multi-objective.github.io/moocore/python/","github":"https://github.com/multi-objective/moocore","docs":"https://multi-objective.github.io/moocore/python/","changelog":null,"pypi":"https://pypi.org/project/moocore/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":80,"avg_install_s":3.8,"avg_import_s":0.4,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/moocore/compatibility"},"provenance":{"verified_status":"passing","verified_at":"Sun Jun 28","last_verified":"Sun Jun 28","next_check":"Tue Jul 28","install_tag":null}}