{"library":"empirical-calibration","type":"library","category":null,"description":"Empirical Calibration (EC) is a Python library (version 0.12) designed for correcting bias in data samples using generic weighting methods. It formulates the calibration problem as a convex optimization, solved efficiently in a dual form, and aims to reduce data biases in various statistical fields, such as survey sampling and causal studies with observational data. The library is actively maintained, with the latest release in May 2024 and ongoing development on GitHub.","language":"python","status":"active","version":"0.12","tags":["calibration","weighting","bias correction","survey sampling","causal inference","convex optimization","statistics","machine learning"],"install":[{"cmd":"pip install empirical-calibration","imports":["import empirical_calibration"]},{"cmd":"pip install -q git+https://github.com/google/empirical_calibration","imports":[]}],"homepage":null,"github":"https://github.com/google/empirical_calibration","docs":null,"changelog":null,"pypi":"https://pypi.org/project/empirical-calibration/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":25,"avg_install_s":14.5,"avg_import_s":null,"wheel_type":"sdist"},"url":"https://checklist.day/v1/registry/empirical-calibration/compatibility"},"provenance":{"verified_status":"import_fail","verified_at":"Fri Jul 03","last_verified":"Fri Jul 03","next_check":"Fri Jul 10","install_tag":null}}