scikit-survival and pysurvival
These are competitors offering overlapping survival analysis functionality, with scikit-survival being the more mature and actively maintained option, while pysurvival appears to be unmaintained (evidenced by zero monthly downloads).
About scikit-survival
sebp/scikit-survival
Survival analysis built on top of scikit-learn
This tool helps researchers and analysts predict when an event will occur, like patient recovery or machine failure, even when some subjects haven't experienced the event yet. You input data with observed event times and censored data (where the event hasn't happened or wasn't observed within the study period), and it outputs models that estimate event probabilities over time. This is for data scientists, statisticians, and researchers working with time-to-event data.
About pysurvival
square/pysurvival
Open source package for Survival Analysis modeling
This tool helps you predict when a specific event is likely to occur, such as customer churn or loan default. You provide historical data on events and their timings, and it outputs models that estimate event probabilities over time. Data scientists, risk analysts, and marketing strategists can use this to understand and forecast 'time-to-event' scenarios.
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