(Bayesian) Additive Voronoi Tessellations.

AddiVortes: Bayesian Additive Voronoi Tessellations

Overview

AddiVortes implements the Bayesian Additive Voronoi Tessellation model for machine learning regression and non-parametric statistical modeling. This R package provides a flexible alternative to BART (Bayesian Additive Regression Trees), using Voronoi tessellations instead of trees for spatial partitioning.

Key Features

• Machine Learning Regression: Advanced Bayesian regression modeling for complex datasets
• Alternative to BART: Uses Voronoi tessellations instead of trees for more flexible spatial modeling
• Spatial Data Analysis: Excellent for geographic and spatial datasets
• Non-parametric Modeling: No assumptions about functional form
• Bayesian Framework: Full posterior inference with uncertainty quantification
• Complex Function Approximation: Captures non-linear relationships and interactions

Applications

AddiVortes is particularly well-suited for:

• Spatial regression and geographic data analysis
• Machine learning tasks requiring interpretable models
• Non-parametric regression where the functional form is unknown
• Bayesian modeling with uncertainty quantification
• Complex surface modeling and function approximation
• Alternative to BART for researchers seeking different ensemble approaches

Installation

You can install the latest version of AddiVortes from GitHub with:

# install.packages("devtools")
devtools::install_github("johnpaulgosling/AddiVortes", 
                         build_vignettes = TRUE)

Quick Start

library(AddiVortes)

# Load your data
# X <- your_predictors
# y <- your_response

# Fit the AddiVortes model
# model <- AddiVortesFit(X, y)

# Make predictions
# predictions <- predict(model, newdata = X_test)

Documentation

• Getting Started Guide
• Prediction Examples
• Function Reference

Comparison with BART

While BART (Bayesian Additive Regression Trees) uses tree-based partitioning, AddiVortes uses Voronoi tessellations, which can provide:

• More natural spatial partitioning
• Flexible geometric boundaries
• Alternative ensemble approach for machine learning
• Enhanced performance on spatial data

Cite Us

If you use this package in your research, please cite:

citation("AddiVortes")

References

Stone, A. and Gosling, J.P. (2025). AddiVortes: (Bayesian) additive Voronoi tessellations. Journal of Computational and Graphical Statistics.

Keywords

Bayesian machine learning, BART alternative, Voronoi tessellation, spatial regression, non-parametric regression, ensemble methods, statistical modeling, R package.