Metadata-Version: 2.1
Name: cobsurv
Version: 0.0.1
Summary: Cobra Ensemble for Conditional Survival
Author-email: Rahul Goswami <yuvrajiro@gmail.com>, Rahul Goswami <arabin.k.dey@gmail.com>
License: MIT License
        
        Copyright (c) 2023 Rahul Goswami 
        
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Project-URL: Homepage, https://github.com/yuvrajiro/cobsurv
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Project-URL: Bug Tracker, https://github.com/yuvrajiro/cobsurv/issues
Project-URL: Release Notes, https://cobsurv.readthedocs.io/en/latest/release_notes.html
Keywords: Proximity,Machine Learning,Survival Analysis,Ensemble Learning
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
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# cobsurv : Cobra Ensemble for Conditional Survival

[![Documentation Status](https://readthedocs.org/projects/cobsurv/badge/?version=latest)](https://cobsurv.readthedocs.io/en/latest/?badge=latest)



![cobsurv](Population.png)





Cobra Ensemble for Conditional Survival are algorithms, designed for survival prediction
using proximity information. The k-NN survival, Random Survival Forest, Kernel Survival
are some examples of Cobra Ensemble for Conditional Survival. While this package tends 
to provide those algorithms later, currently the package provides the following algorithms:

- COBRA Survival 

For now other algorithms are taken from scikit-survival and np_survival to provide as
a base learner for the ensemble algorithms.

## installation

```
pip install cobsurv
```

The documentation is available at [https://cobsurv.readthedocs.io/en/latest/](https://cobsurv.readthedocs.io/en/latest/)



## Citation
```
@misc{goswami2023areanorm,
      title={Area-norm COBRA on Conditional Survival Prediction}, 
      author={Rahul Goswami and Arabin Kr. Dey},
      year={2023},
      eprint={2309.00417},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```
