AddiVortes implements Bayesian Additive Voronoi Tessellation models for machine learning regression and non-parametric statistical modeling. This package provides a flexible alternative to BART (Bayesian Additive Regression Trees), using Voronoi tessellations instead of trees for spatial partitioning. The method is particularly effective for spatial data analysis, complex function approximation, and Bayesian regression modeling.
Maintainer: John Paul Gosling john-paul.gosling@durham.ac.uk (ORCID)
Authors:
Adam Stone adam.stone2@durham.ac.uk (ORCID)
Key features include:
Machine learning regression with Bayesian inference
Alternative to BART using Voronoi tessellations
Spatial data analysis and modeling
Non-parametric regression capabilities
Complex function approximation
Uncertainty quantification through posterior inference
Stone, A. and Gosling, J.P. (2025). AddiVortes: (Bayesian) additive Voronoi tessellations. Journal of Computational and Graphical Statistics.