survalis: Interpretable Survival Machine Learning Framework
A modular toolkit for interpretable survival machine learning
with a unified interface for fitting, prediction, evaluation, and
interpretation.
It includes semiparametric, parametric, tree-based, ensemble, boosting,
kernel, and deep-learning survival learners, together with benchmarking,
scoring, calibration, and model-agnostic interpretation utilities.
Representative methodological anchors include Cox (1972)
<doi:10.1111/j.2517-6161.1972.tb00899.x>, Royston and Parmar (2002)
<doi:10.1002/sim.1203>, Ishwaran et al. (2008) <doi:10.1214/08-AOAS169>,
Jaeger et al. (2019) <doi:10.1214/19-AOAS1261>, Harrell et al. (1982)
<doi:10.1001/jama.1982.03320430047030>, Graf et al. (1999)
<doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18%3C2529::AID-SIM274%3E3.0.CO;2-5>,
Friedman (2001) <doi:10.1214/aos/1013203451>, Apley and Zhu (2020)
<doi:10.1111/rssb.12377>, and Lundberg and Lee (2017)
<https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions>,
and other related methods for survival modeling, prediction, and
interpretation.
| Version: |
0.7.1 |
| Depends: |
R (≥ 4.1) |
| Imports: |
survival, ggplot2, functionals, nnls, rpart, tibble, rsample, aftgee, aorsf, bnnSurvival, pec, party, ranger, survdnn, survivalsvm, randomForestSRC, xgboost, BART, flexsurv, glmnet, mboost, rstpm2, timereg, partykit, gower, pracma, torch, data.table, dplyr, glue, cli, purrr, rlang, tidyr |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, roxygen2, covr, stats, utils |
| Published: |
2026-04-23 |
| DOI: |
10.32614/CRAN.package.survalis (may not be active yet) |
| Author: |
Imad El Badisy [aut, cre] |
| Maintainer: |
Imad El Badisy <elbadisyimad at gmail.com> |
| BugReports: |
https://github.com/ielbadisy/survalis/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/ielbadisy/survalis |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
survalis results |
Documentation:
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