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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.

Versions across snapshots

VersionRepositoryFileSize
0.7.1 rolling linux/jammy R-4.5 survalis_0.7.1.tar.gz 922.4 KiB
0.7.1 rolling linux/noble R-4.5 survalis_0.7.1.tar.gz 494.4 KiB
0.7.1 rolling source/ R- survalis_0.7.1.tar.gz 494.4 KiB
0.7.1 latest linux/jammy R-4.5 survalis_0.7.1.tar.gz 922.4 KiB
0.7.1 latest linux/noble R-4.5 survalis_0.7.1.tar.gz 494.4 KiB
0.7.1 latest source/ R- survalis_0.7.1.tar.gz 494.4 KiB
0.7.1 2026-04-26 source/ R- survalis_0.7.1.tar.gz 494.4 KiB
0.7.1 2026-04-23 source/ R- survalis_0.7.1.tar.gz 0 B

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