scikit-survival and survhive
Scikit-survival provides the foundational statistical and machine learning survival analysis implementations that survhive wraps with convenience abstractions and deep learning extensions, making them complements rather than direct competitors.
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 survhive
compbiomed-unito/survhive
Convenient, opinionated wrapper around some (deep) survival models
This tool helps researchers and analysts in fields like medicine or economics predict how long it will take for a specific event to occur, such as patient recovery or equipment failure. You provide data on past events and their timings, and it outputs predictions and analyses about future event durations. It's designed for data scientists and statisticians working with time-to-event data.
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