{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Computes empirical calibration weights to correct bias in survey samples and observational data through convex optimization, supporting weight clipping and inexact calibration.","skillfed_tags":["survey-sampling","causal-inference","bias-correction"],"use_cases":["Reweight survey respondents to match known population distributions when the sample is biased or non-random.","Balance covariate distributions between treatment and control groups in observational causal studies.","Correct for selection bias in administrative or real-world datasets where sampling was not random.","Compute inverse-probability weights for observational studies with complex covariate imbalance.","Apply weight clipping to avoid extreme weights that can destabilize downstream analysis."],"what_it_does":"Empirical calibration is a statistical weighting method that corrects bias in survey samples and observational data by reweighting observations to match target population characteristics. The package solves the calibration problem as a convex optimization task in dual form, supporting multiple objectives, weight clipping, and inexact calibration. It depends on numpy, scipy, scikit-learn, pandas, patsy, absl-py, and six to handle numerical computation and data manipulation.\n\nThe package is designed for survey sampling (where samples are unrepresentative) and causal inference (where treatment assignment correlates with covariates). However, the repository is archived and maintenance is abandoned as of May 2024, meaning no bug fixes, security updates, or compatibility work with newer Python or dependency versions will occur.","worth_installing":"No. The package is abandoned (archived repository, no commits since May 2024) and has high install friction with seven dependencies. While empirical calibration is a legitimate statistical technique, the lack of maintenance means no support for modern Python or dependency versions, no bug fixes, and no security updates. Use only if you have a frozen environment and cannot migrate to actively maintained alternatives."},"id":"empirical-calibration","links":{"html":"https://skillfed.io/packages/empirical-calibration","md":"https://skillfed.io/packages/empirical-calibration.md","pypi":"https://pypi.org/project/empirical-calibration/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2024-05-05","license_spdx":null,"license_treatment":"permissive","name":"empirical-calibration","python_support":"unspecified","summary":"Package for empirical calibration"},"popularity":{"monthly_downloads":319751,"position":7642,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12"}