Spatial Interpolation for data comprising hard and soft-interval forms

The Bayesian Maximum Entropy (BME) framework provides a flexible and principled approach to space-time data analysis by combining Bayesian inference with the maximum entropy principle. It supports optimal estimation using both precise (hard) and uncertain (soft) data, such as intervals or probability distributions—making it ideal for complex, real-world datasets. The BMEmapping R package implements core BME methods for spatial interpolation, enabling the integration of heterogeneous data, variogram-based modeling, and uncertainty quantification.

Installation

You can install the development version of BMEmapping from GitHub with:

# install.packages("devtools")
devtools::install_github("KinsprideDuah/BMEmapping")

Functions

bme_map - creates a BMEmapping object that contains all the data information necessary for BME interpolation.

prob_zk - computes and optionally plots the posterior density estimate at a single unobserved location.

bme_predict - predicts the posterior mean or mode and the associated variance at an unobserved location.

bme_cv - performs a cross-validation on the hard data to assess model performance.

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.

Author

Kinspride Duah

License

MIT + file LICENSE