{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"lifelines implements survival analysis methods\u2014Kaplan-Meier, Nelson-Aalen, and regression models\u2014to estimate time-to-event distributions and identify factors influencing when events occur.","skillfed_tags":["survival-analysis","time-to-event","statistical-modeling"],"use_cases":["Estimate patient survival curves and test whether treatment groups differ in time-to-event outcomes.","Measure SaaS subscriber lifetime and identify factors (pricing, features, cohort) that influence churn.","Analyze A/B test results to determine which variant leads to faster user action completion.","Study political party or relationship lifespans to understand what factors predict dissolution.","Quantify inventory demand by treating stock-outs as censoring events in demand estimation."],"what_it_does":"lifelines is a pure Python library for survival analysis\u2014a statistical discipline originally developed in medicine and actuarial science to answer when and why events occur under uncertainty. It provides implementations of classical estimators (Kaplan-Meier, Nelson-Aalen) and regression models to analyze time-to-event data, where observations may be censored (incomplete). The library depends on numpy, scipy, pandas, matplotlib, autograd, autograd-gamma, and formulaic to handle numerical computation, data manipulation, visualization, and automatic differentiation.\n\nBeyond medical applications, survival analysis applies to subscriber lifetime measurement in SaaS, inventory stock-outs, political party or relationship lifespans, and A/B testing for time-to-action. lifelines abstracts the mathematical complexity, allowing researchers and data scientists to fit models, estimate survival curves, and test hypotheses on censored data without implementing the algorithms from scratch.","worth_installing":"Yes. lifelines is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It is the standard Python library for survival analysis and well-suited for anyone analyzing time-to-event data with censoring. Install it if you need to fit survival models or estimate event-time distributions."},"id":"lifelines","links":{"html":"https://skillfed.io/packages/lifelines","md":"https://skillfed.io/packages/lifelines.md","pypi":"https://pypi.org/project/lifelines/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-05","license_spdx":null,"license_treatment":"permissive","name":"lifelines","python_support":"supports_current","summary":"Survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression"},"popularity":{"monthly_downloads":2547917,"position":3006,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.30.3"}