Package: AddiVortes 1.0.1

AddiVortes: (Bayesian) Additive Voronoi Tessellations

Implements the Bayesian Additive Voronoi Tessellation model for non-parametric regression, classification and machine learning as introduced in Stone and Gosling (2025) <doi:10.1080/10618600.2024.2414104>. This package provides a flexible alternative to BART (Bayesian Additive Regression Trees) using Voronoi tessellations instead of trees. Users can fit Bayesian regression and probit classification models, estimate the associated posterior distributions and make predictions. It is particularly useful for spatial data analysis, machine learning, complex function approximation and Bayesian modelling where the underlying structure is unknown.

Authors:Adam Stone [aut], John Paul Gosling [aut, cre], Andrew Iskauskas [aut], Leo Thomson [aut]

AddiVortes_1.0.1.tar.gz
AddiVortes_1.0.1.zip(r-4.7-x86_64)AddiVortes_1.0.1.zip(r-4.7-arm64)AddiVortes_1.0.1.zip(r-4.6-x86_64)AddiVortes_1.0.1.zip(r-4.6-arm64)AddiVortes_1.0.1.zip(r-4.5-x86_64)
AddiVortes_1.0.1.tgz(r-4.6-x86_64)AddiVortes_1.0.1.tgz(r-4.6-arm64)AddiVortes_1.0.1.tgz(r-4.5-x86_64)AddiVortes_1.0.1.tgz(r-4.5-arm64)
AddiVortes_1.0.1.tar.gz(r-4.7-arm64)AddiVortes_1.0.1.tar.gz(r-4.7-x86_64)AddiVortes_1.0.1.tar.gz(r-4.6-arm64)AddiVortes_1.0.1.tar.gz(r-4.6-x86_64)
AddiVortes_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
AddiVortes/json (API)

# Install 'AddiVortes' in R:
install.packages('AddiVortes', repos = c('https://johnpaulgosling.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/johnpaulgosling/addivortes/issues

Pkgdown/docs site:https://johnpaulgosling.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

7.08 score 4 stars 8 scripts 566 downloads 3 exports 0 dependencies

Last updated from:af8287c793. Checks:15 OK. Indexed: yes.

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linux-devel-x86_64OK135
source / vignettesOK228
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linux-release-x86_64OK107
macos-release-arm64OK125
macos-release-x86_64OK285
macos-oldrel-arm64OK139
macos-oldrel-x86_64OK245
windows-devel-arm64OK332
windows-devel-x86_64OK116
windows-release-arm64OK337
windows-release-x86_64OK71
windows-oldrel-x86_64OK130
wasm-releaseOK75

Exports:AddiVortesnew_AddiVortestraceplots

Dependencies:

Classification with AddiVortes
How the response is interpreted | Binary classification | Multi-category classification | Notes on the classification prior
Rendered fromclassification.Rmdusingknitr::rmarkdown

Last update: 2026-09-11
Started: 2026-09-11

Machine Learning with AddiVortes: A Bayesian Alternative to BART
1. Loading the Package and Data | 2. Preparing the Data | 3. Training the Model | 4. Making Predictions and Evaluating Performance | 5. Visualising the Results
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-09-11
Started: 2025-07-07

Bayesian Regression and Prediction with AddiVortes
1. Generating a synthetic dataset | 2. Fitting the AddiVortes Model | 3. Out-of-Sample Prediction | 4. Visualising Prediction Performance | 5. Credible Intervals vs. Prediction Intervals
Rendered fromprediction.Rmdusingknitr::rmarkdown

Last update: 2026-09-11
Started: 2025-07-07

Using Categorical Covariates with AddiVortes
1. What is One-Hot Encoding? | 2. The catScaling Parameter | 3. Eskin Distance (cat.onehot = FALSE) | 4. Choosing Between One-Hot and Eskin | 5. A Synthetic Example | 6. Inspecting the Encoding | 7. Fitting the Model (One-Hot) | 8. Making Predictions | 9. Handling Unseen Category Levels | 10. Effect of catScaling | 11. Comparing One-Hot and Eskin Distances | 12. Summary of Key Points | References
Rendered fromcategorical.Rmdusingknitr::rmarkdown

Last update: 2026-08-06
Started: 2026-03-11

Modelling Spherical Data with AddiVortes
1. What is Spherical Data? | 2. Coordinate Convention | 3. Generating Synthetic Spherical Data | 4. Fitting the Spherical Model | 5. Out-of-Sample Evaluation | 6. Visualising Predictions | 7. Multiple Spherical Covariates | 8. Summary of Key Points
Rendered fromspherical.Rmdusingknitr::rmarkdown

Last update: 2026-06-05
Started: 2026-03-11