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AddiVortes: Bayesian Additive Voronoi Tessellations for Machine Learning

Source: R/AddiVortes-package.R
AddiVortes-package.Rd

AddiVortes implements Bayesian Additive Voronoi Tessellation models for machine learning regression, classification and non-parametric statistical modelling. 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 and classification.

Details

Key features include:

  • Machine learning regression and classification with Bayesian inference

  • Binary and multinomial probit models with latent-variable data augmentation

  • Alternative to BART using Voronoi tessellations

  • Spatial data analysis and modelling

  • Non-parametric regression capabilities

  • Complex function approximation

  • Uncertainty quantification through posterior inference

References

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

Stone, A.J., Ogundimu, E. and Gosling, J.P. (2026). Binary AddiVortes: (Bayesian) Additive Voronoi Tessellations for Binary Classification with an application to Predicting Home Mortgage Application Outcomes.

Albert, J.H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association.

Kindo, B.P., Wang, H. and Peña, E.A. (2016). Multinomial probit Bayesian additive regression trees. Stat.

See also

https://johnpaulgosling.github.io/AddiVortes/

Author

Maintainer: John Paul Gosling john-paul.gosling@durham.ac.uk (ORCID)

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