{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Computes Krippendorff's alpha, a statistical measure of inter-rater agreement, with optimized performance for reliability data matrices.","skillfed_tags":["statistics","annotation-validation","research"],"use_cases":["Validate agreement between multiple human annotators on a text classification or tagging task before using their labels for training.","Measure consistency of coding in qualitative research (e.g., interview analysis, content coding) across multiple independent coders.","Assess reliability of crowdsourced labeling by computing agreement among workers on the same items.","Evaluate inter-rater agreement in medical or scientific studies where multiple experts independently assess the same cases.","Benchmark annotation quality in NLP dataset creation by quantifying coder consensus."],"what_it_does":"Krippendorff is a specialized statistics library that computes Krippendorff's alpha, a widely-used agreement coefficient in content analysis, linguistics, and social science research. It measures how consistently multiple coders or raters assign values to the same units, accounting for the possibility of incomplete or partial overlap in what each coder rated. The package is optimized for speed by avoiding nested loops over coders, making it faster than naive implementations.\n\nThe library is minimal and focused: it takes a reliability data matrix as input and returns the alpha coefficient. It depends only on numpy and runs on modern Python versions (3.10 and later). The implementation is based on Thomas Grill's algorithm and is actively maintained. It is most useful in research workflows where you need to validate inter-rater reliability before proceeding with analysis.","worth_installing":"Yes, if you need to compute inter-rater agreement in research or annotation workflows. Low install friction, active maintenance, no security vulnerabilities, and a focused, well-scoped implementation. The GPL-3.0-or-later license requires careful review if you plan to use it in proprietary software; otherwise it is a straightforward choice for academic and open-source projects."},"id":"krippendorff","links":{"html":"https://skillfed.io/packages/krippendorff","md":"https://skillfed.io/packages/krippendorff.md","pypi":"https://pypi.org/project/krippendorff/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-11-03","license_spdx":"GPL-3.0-or-later","license_treatment":"copyleft","name":"krippendorff","python_support":"supports_current","summary":"Fast computation of the Krippendorff's alpha measure."},"popularity":{"monthly_downloads":123821,"position":11899,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.2"}