survalis provides a unified interface for fitting, predicting, evaluating, tuning, comparing, interpreting and standardising survival machine-learning models across 24 learners.
The seven verbs
The recommended interface, consistent with the funcml package:
fit()fit a learner by id, returning a
survalis_fit.predict()typed predictions on a recorded time grid (
"survival","risk","chf","hazard","rmst","quantile","median").evaluate()resampled performance for a fit or a formula.
tune()grid search, returning the refit best model.
compare()paired multi-model leaderboard on shared folds.
interpret()one entry point to the explanation methods.
estimate()standardised binary-treatment contrast by G-computation.
Resampling is described by cv(), holdout(), groupcv() and
bootstrap(). Discover components with listlearners(),
listmetrics() and listmethods().
Custom learners plug into every verb as model = list(fit = , predict = );
see fit().
Author
Maintainer: Imad El Badisy elbadisyimad@gmail.com