pycox and pysurvival
These are **competitors**: both provide end-to-end survival analysis modeling frameworks in Python, with pycox offering more active development (PyTorch-based with modern deep learning methods) while pysurvival provides a broader sklearn-style interface for classical and machine learning approaches, forcing practitioners to choose one as their primary toolkit.
About pycox
havakv/pycox
Survival analysis with PyTorch
This tool helps researchers, clinicians, and data scientists predict the time until an event occurs, such as disease recurrence, customer churn, or machine failure. You provide data including individual characteristics and observed event times, and it outputs models that estimate the probability of the event happening over time. It's designed for anyone working with 'time-to-event' data who needs to forecast when things will happen.
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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