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.
You can install the development version of BMEmapping from GitHub with:
# install.packages("devtools")
devtools::install_github("KinsprideDuah/BMEmapping")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.
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.
Kinspride Duah
MIT + file LICENSE