survalis-package: survalis: Interpretable Survival Machine Learning

In survalis: Interpretable Survival Machine Learning Framework

survalis: Interpretable Survival Machine Learning

Description

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(s)

Maintainer: Imad El Badisy [e-mail]

See Also

Useful links:

• https://elbadisyimad.com/survalis/

• https://github.com/ielbadisy/survalis

• https://CRAN.R-project.org/package=survalis

• Report bugs at https://github.com/ielbadisy/survalis/issues

survalis documentation built on Nov. 14, 2026, 5:10 p.m.