Metadata-Version: 1.1
Name: lifelines
Version: 0.2.0
Summary: Survival analysis in Python
Home-page: https://github.com/CamDavidsonPilon/lifelines
Author: Cameron Davidson-Pilon
Author-email: cam.davidson.pilon@gmail.com
License: MIT
Description: lifelines
        =======
         
        [What is survival analysis and why should I learn it?](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/lifelines/master/Tutorial%20and%20Examples.ipynb) Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer *why do events occur now versus later* under uncertainity (where *events* might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like *what factors might influence deaths?*
        
        But outside of medicine and actuarial science, there are many other interesting and exicting applications of this 
        lesser-known technique, for example:
        - SaaS providers are interested in measuring customer lifetimes; 
        - ecommerce shops are interested the time between first and second order (called *repeat purchase rate*).
        - sociologists are interested in measure political parties lifetimes, or relationships, or marriages
        - and many others 
        
        *lifelines* is a pure Python implementation of the best parts of survival analysis. We'd love to hear if you use *lifelines*, please ping me at [@cmrn_dp](https://twitter.com/Cmrn_DP) and let me know your 
        thoughts on the library. 
        
        ####Dependencies:
        
        The usual Python data stack: numpy, scipy, pandas (a modern version please), matplotlib (optional).
        
        #### Installation:
        
        You can install *lifelines* using 
        
               pip install -U git+https://github.com/CamDavidsonPilon/lifelines.git
        
        from the command line. 
        
        
        ## (Quick) Intro to *lifelines* and survival analysis
        
        *Work in progress (75%)*
        
        If you are new to survival analysis, wondering why it is useful, or are interested in *lifelines* examples and use,
        I recommend running the `Tutorial and Examples.ipynb` notebook in a IPython notebook session. Alternatively, you can [view it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/lifelines/master/Tutorial%20and%20Examples.ipynb).
        
        
        ## Documentation
        
        *Work in progress (70%)*
        
        I've added documentation to a notebook, `Documentation.ipynb`, that adds detail to 
        the classes, methods and data types. You can use the IPython notebook to view it, or [view it online](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/lifelines/master/Documentation.ipynb).
        
        #### More examples
        
        There are some IPython notebook files in the repo, and you can view them online here (though they may 
        contain syntax from older versions of *lifelines*.)
        
        - [Divorce data](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/lifelines/master/datasets/Divorces%2520Rates.ipynb)
        - [Gehan's survival dataset](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/lifelines/master/datasets/The%2520Gehan%2520Survival%2520Data.ipynb)
        
        
        
        
        
Keywords: survival analysis statistics data analysis
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
