BMEmapping: Spatial Interpolation using Bayesian Maximum Entropy (BME)
Provides an accessible and robust implementation of core BME
methodologies for spatial prediction. It enables the systematic integration
of heterogeneous data sources including both hard data (precise
measurements) and soft interval data (bounded or uncertain observations)
while incorporating prior knowledge and supporting variogram-based spatial
modeling. The BME methodology is described in Christakos (1990)
<doi:10.1007/BF00890661> and Serre and Christakos (1999)
<doi:10.1007/s004770050029>.
| Version: |
1.2.2 |
| Depends: |
R (≥ 3.5) |
| Imports: |
ggplot2, gridExtra, mvtnorm, stats, utils |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2025-08-19 |
| DOI: |
10.32614/CRAN.package.BMEmapping |
| Author: |
Kinspride Duah 
[aut, cre, cph],
Yan Sun [aut] |
| Maintainer: |
Kinspride Duah <kinspride2020 at gmail.com> |
| BugReports: |
https://github.com/KinsprideDuah/BMEmapping/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/KinsprideDuah/BMEmapping |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
BMEmapping results |
Documentation:
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