bart-survival 0.1.1
Survival analyses with Bayesian Additivie Regression Trees using PyMC-BART as BART backend.

## Overview

BART-Survival is a Python package that allows time-to-event (survival analyses) in discrete-time using the non-parametric machine learning algorithm, Bayesian Additive Regression Trees (BART). BART_Survival combines the performance of the BART algorithm from the PyMC-BART library with the complementary data and model structural formatting required to provide a convenient approach to conducting high performance, non-parametric survival analysis. 

This repository contains the source code and documentation for the BART_SURVIVAL package as well as user-guides/example notebooks. We additionally provide the code used in conducting the validation study of the algorithm.

### Installation


### API


### User Guide


### Validation study links


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Repository for BART Survival Package. 

Based on the PYMC Bayesian Additive Regression Trees implementation, this package provides a preconfigured model for BART applied in the survival setting and necessary data processing algorithms to implement such model.

Installation
`pip install BART_SURVIVAL`

Documentation:
https://twj8cdc.github.io/BART_SURVIVAL/