BKP: An R Package for Beta Kernel Process Modeling

We present BKP, a user-friendly and extensible R package that implements the Beta Kernel Process (BKP)—a fully nonparametric and computationally efficient framework for modeling spatially varying binomial probabilities. The BKP model combines localized kernel-weighted likelihoods with conjugate beta priors, resulting in closed-form posterior inference without requiring latent variable augmentation or intensive MCMC sampling. The package supports binary and aggregated binomial responses, allows flexible choices of kernel functions and prior specification, and provides loss-based kernel hyperparameter tuning procedures. In addition, BKP extends naturally to the Dirichlet Kernel Process (DKP) for modeling spatially varying multinomial data.

Features

• ✅ Bayesian modeling for binomial and multinomial count data
• ✅ Kernel-based local information sharing
• ✅ Posterior prediction and uncertainty quantification
• ✅ Class label prediction using threshold or MAP rule
• ✅ Simulation from posterior (Beta or Dirichlet) distributions

Installation

You can install the stable version of BKP from CRAN with:

install.packages("BKP")

Or install the development version from GitHub with:

# install.packages("pak")
pak::pak("Jiangyan-Zhao/BKP")

Documentation

The statistical foundations and example applications are described in the following vignette:

• BKP User Guide (PDF)

Citing

If you use BKP in your work, please cite both the methodology paper and the R package:

• Methodology paper
  Zhao, J., Qing, K., and Xu, J. (2025). BKP: An R Package for Beta Kernel Process Modeling.
  arXiv:2508.10447. https://arxiv.org/abs/2508.10447

• R package
  Zhao, J., Qing, K., and Xu, J. (2025). BKP: Beta Kernel Process Modeling.
  R package version 0.2.3. https://cran.r-project.org/package=BKP

You can also obtain the citation information directly within R:

citation("BKP")

Development

The BKP package is under active development. Contributions and suggestions are welcome via GitHub issues or pull requests.