AddiVortes: (Bayesian) Additive Voronoi Tessellations

Implements the Bayesian Additive Voronoi Tessellation model for non-parametric regression and machine learning as introduced in Stone and Gosling (2025) . This package provides a flexible alternative to BART (Bayesian Additive Regression Trees) using Voronoi tessellations instead of trees. Users can fit Bayesian regression models, estimate posterior distributions, and visualise the resulting tessellations. It is particularly useful for spatial data analysis, machine learning regression, complex function approximation and Bayesian modeling where the underlying structure is unknown. The method is well-suited to capturing spatial patterns and non-linear relationships.