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type: software
license: MIT
title: 'survalis: Interpretable Survival Machine Learning Framework'
version: 0.7.1
doi: 10.32614/CRAN.package.survalis
abstract: 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) <https://doi.org/10.1111/j.2517-6161.1972.tb00899.x>,
  Royston and Parmar (2002) <https://doi.org/10.1002/sim.1203>, Ishwaran et al. (2008)
  <https://doi.org/10.1214/08-AOAS169>, Jaeger et al. (2019) <https://doi.org/10.1214/19-AOAS1261>,
  Harrell et al. (1982) <https://doi.org/10.1001/jama.1982.03320430047030>, Graf et
  al. (1999) <https://doi.org/10.1002/(SICI)1097-0258(19990915/30)18:17/18%3C2529::AID-SIM274%3E3.0.CO;2-5>,
  Friedman (2001) <https://doi.org/10.1214/aos/1013203451>, Apley and Zhu (2020) <https://doi.org/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.
authors:
- family-names: El Badisy
  given-names: Imad
  email: elbadisyimad@gmail.com
repository: https://ielbadisy.r-universe.dev
repository-code: https://github.com/ielbadisy/survalis
commit: 3dae3c6cae000ecd6f49476cfd3ac07a288544bf
url: https://github.com/ielbadisy/survalis
date-released: '2026-04-22'
contact:
- family-names: El Badisy
  given-names: Imad
  email: elbadisyimad@gmail.com