# BARMPy: Bayesian Additive Regression Models Python Package

arXiv: https://arxiv.org/abs/2404.04738 · HTML: https://mlsanity.com/paper/2404.04738/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 6 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [NeuralSens: Sensitivity Analysis of Neural Networks](https://mlsanity.com/paper/2002.11423/) (2020)
- [Bayesian Additive Regression Trees with Model Trees](https://mlsanity.com/paper/2006.07493/) (2020)
- [Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy](https://mlsanity.com/paper/2202.09924/) (2022)
- [The Rise of GitHub in Scholarly Publications](https://mlsanity.com/paper/2208.04895/) (2022)

## Abstract (arXiv)

We make Bayesian Additive Regression Networks (BARN) available as a Python package, textttbarmpy, with documentation at urlhttps://dvbuntu.github.io/barmpy/ for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use textttbarmpy, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can textttpip install barmpy from the official PyPi repository. textttbarmpy also serves as a baseline Python library for generic Bayesian Additive Regression Models.