Package: AddiVortes 0.6.6

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) <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 models (estimating the associated posterior distributions and make predictions. 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.

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

AddiVortes_0.6.6.tar.gz
AddiVortes_0.6.6.zip(r-4.7)AddiVortes_0.6.6.zip(r-4.6)AddiVortes_0.6.6.zip(r-4.5)
AddiVortes_0.6.6.tgz(r-4.6-x86_64)AddiVortes_0.6.6.tgz(r-4.6-arm64)AddiVortes_0.6.6.tgz(r-4.5-x86_64)AddiVortes_0.6.6.tgz(r-4.5-arm64)
AddiVortes_0.6.6.tar.gz(r-4.7-arm64)AddiVortes_0.6.6.tar.gz(r-4.7-x86_64)AddiVortes_0.6.6.tar.gz(r-4.6-arm64)AddiVortes_0.6.6.tar.gz(r-4.6-x86_64)
AddiVortes_0.6.6.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

6.84 score 3 stars 5 scripts 228 downloads 4 exports 1 dependencies

Last updated from:5236b6fe42. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK199
linux-devel-x86_64OK151
source / vignettesOK225
linux-release-arm64OK219
linux-release-x86_64OK150
macos-release-arm64OK104
macos-release-x86_64OK174
macos-oldrel-arm64OK105
macos-oldrel-x86_64OK154
windows-develOK97
windows-releaseOK90
windows-oldrelOK88
wasm-releaseOK156

Exports:AddiVortescellIndicesnew_AddiVortestraceplots

Dependencies:pbapply

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

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: 2026-04-01
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: 2026-03-23
Started: 2025-07-07

Using Categorical Covariates with AddiVortes
1. What is One-Hot Encoding? | 2. The catScaling Parameter | 3. A Synthetic Example | 4. Inspecting the Encoding | 5. Fitting the Model | 6. Making Predictions | 7. Handling Unseen Category Levels | 8. Effect of catScaling | 9. Summary of Key Points
Rendered fromcategorical.Rmdusingknitr::rmarkdown

Last update: 2026-03-23
Started: 2026-03-11