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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 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.

Details

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

References

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

See also

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

Author

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

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