Overview
AddiVortes implements the Bayesian Additive Voronoi Tessellation model for machine learning regression and non-parametric statistical modeling. This R package provides a flexible alternative to BART (Bayesian Additive Regression Trees), using Voronoi tessellations instead of trees for spatial partitioning.
Key Features
- Machine Learning Regression: Advanced Bayesian regression modeling for complex datasets
- Alternative to BART: Uses Voronoi tessellations instead of trees for more flexible spatial modeling
- Spatial Data Analysis: Excellent for geographic and spatial datasets
- Non-parametric Modeling: No assumptions about functional form
- Bayesian Framework: Full posterior inference with uncertainty quantification
- Complex Function Approximation: Captures non-linear relationships and interactions
Applications
AddiVortes is particularly well-suited for:
- Spatial regression and geographic data analysis
- Machine learning tasks requiring interpretable models
- Non-parametric regression where the functional form is unknown
- Bayesian modeling with uncertainty quantification
- Complex surface modeling and function approximation
- Alternative to BART for researchers seeking different ensemble approaches
Installation
You can install the latest version of AddiVortes from GitHub with:
# install.packages("devtools")
devtools::install_github("johnpaulgosling/AddiVortes",
build_vignettes = TRUE)Quick Start
library(AddiVortes)
# Load your data
# X <- your_predictors
# y <- your_response
# Fit the AddiVortes model
# model <- AddiVortesFit(X, y)
# Make predictions
# predictions <- predict(model, newdata = X_test)Comparison with BART
While BART (Bayesian Additive Regression Trees) uses tree-based partitioning, AddiVortes uses Voronoi tessellations, which can provide:
- More natural spatial partitioning
- Flexible geometric boundaries
- Alternative ensemble approach for machine learning
- Enhanced performance on spatial data