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survalis  

Interpretable Survival Machine Learning Framework
View on CRAN: Click here


Download and install survalis package within the R console
Install from CRAN:
install.packages("survalis")

Install from Github:
library("remotes")
install_github("cran/survalis")

Install by package version:
library("remotes")
install_version("survalis", "0.7.1")



Attach the package and use:
library("survalis")
Maintained by
Imad El Badisy
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-23
Latest Update: 2026-04-23
Description:
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.
How to cite:
Imad El Badisy (2026). survalis: Interpretable Survival Machine Learning Framework. R package version 0.7.1, https://cran.r-project.org/web/packages/survalis. Accessed 04 Jul. 2026.
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