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

Project description

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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The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.

This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.

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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/

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