Survival Analysis Python Packages

lifelines

Survival analysis in Python

scikit-survival

Survival analysis built on top of scikit-learn

reliability

Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/

skpro

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

pycox

Survival analysis with PyTorch

xgbse

Improving XGBoost survival analysis with embeddings and debiased estimators

surpyval

A Python package for survival analysis. The most flexible survival analysis package available. SurPyval can work with arbitrary combinations of observed, censored, and truncated data. SurPyval can also fit distributions with 'offsets' with ease, for example the three parameter Weibull distribution.

torchsurv

Deep survival analysis made easy

survivaleval

The most comprehensive Python package for evaluating survival analysis models.

survival-rl

Explorations into Survival Reinforcement Learning, proposed by Tiofack et al. of Inria earlier this year

comprisk

Scalable, scikit-learn-compatible competing-risks survival analysis in pure Python — CR random survival forest, Fine-Gray, cause-specific Cox, Aalen-Johansen CIF, Gray's test, and exact TreeSHAP. 10–22× faster than randomForestSRC on real EHR and 16.6–544× vs scikit-survival (n=5k→50k); scales to n=10⁶ in ~1 min.

gibbus

Flexible analytic log-concave distributions fitted by maximum likelihood, with support-aware modeling and optional mixtures for multimodal data.

veridist

Evidence-first Python distribution fitting with reproducible inference, reliability models, resumable execution, and explicit statistical contracts.

predictr

Weibull Analysis Tools

kaplanmeier

kaplanmeier is an python library to create survival curves using kaplan-meier, and compute the log-rank test.

orange3-survival-analysis

🍊 :heavy_plus_sign: Survival Analysis add-on for Orange3 data mining suite.

fracability

Python toolbox to analyse fracture networks for digitalized rock outcrops.

bess

Best Subset Selection algorithm for Regression, Classification, Count, Survival analysis

hazardous

Competing Risks and Survival Analysis

greenwood

Modern survival analysis for Python.

gen-surv

Simulate survival data with a known truth: twelve models spanning proportional hazards, accelerated failure time, competing risks, cure fractions, recurrent events and multi-state processes. Docs: https://diogoribeiro7.github.io/genSurvPy/

bioconverge

Python framework for biological score integration, patient stratification, concordance analysis, archetype discovery, and validation-aware computational biology.

pirao

pirão — Probabilistic Inference for Reliability Analysis from Observed data: Bayesian reliability with Stan, sibling of farofa and faultree

melinoe

A local-first, validity-aware workbench for reproducible multimodal cancer research