lifelines is a complete survival analysis library, written in pure Python. What benefits does lifelines have?
- easy installation
- internal plotting methods
- simple and intuitive API
- handles right, left and interval censored data
- contains the most popular parametric, semi-parametric and non-parametric models
pip install lifelines
or
conda install -c conda-forge lifelines
Available on Github, CamDavidsonPilon/lifelines. Please report bugs, issues and feature extensions there. We also have discussion channel available to discuss survival analysis and lifelines:
The following link will bring you to a page where you can find the latest citation for lifelines: Citation for lifelines
.. toctree:: :maxdepth: 1 :caption: Quickstart & Intro Quickstart Survival Analysis intro
.. toctree:: :maxdepth: 1 :caption: Univariate Models Survival analysis with lifelines jupyter_notebooks/Piecewise Exponential Models and Creating Custom Models.ipynb jupyter_notebooks/Modelling time-lagged conversion rates.ipynb
.. toctree:: :maxdepth: 1 :caption: Regression Models Survival Regression jupyter_notebooks/Custom Regression Models.ipynb Compatibility with scikit-learn Time varying survival regression jupyter_notebooks/Proportional hazard assumption.ipynb
.. toctree:: :maxdepth: 1 :caption: Additional documentation References Examples
.. toctree:: :maxdepth: 1 :caption: About lifelines Changelog Development blog <https://dataorigami.net/blogs/napkin-folding/tagged/lifelines> Citing lifelines <Citing lifelines> Support lifelines <https://github.com/sponsors/CamDavidsonPilon>
.. toctree:: :maxdepth: 1 :caption: Questions? Suggestions? Discussion forum <https://github.com/camdavidsonpilon/lifelines/discussions> Create a GitHub issue <https://github.com/camdavidsonpilon/lifelines/issues>
.. toctree:: :maxdepth: 1 :caption: Developer Documentation Contributing