Introduction | Main Functions | Input Requirements | Data parameters | Variogram parameters | Optional parameters | A Data Example | Loading Required Libraries | The utsnowload Dataset | Overview of the utsnowload Dataset | Organizing Hard and Soft Data | Visualizing Hard and Soft Data | BME Estimation Methods | Conventional Bayesian Maximum Entropy (CBME) | Quantile-Based Bayesian Maximum Entropy (QBME) | CBME Implementation | Variogram Estimation | Posterior Density Estimation | Spatial Prediction | Cross-Validation Analysis | The QBME Implementation | Posterior Distribution Estimation at Individual Locations | Conclusion
Package: BMEmapping 1.2.2.9000
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>, Serre and Christakos (1999) <doi:10.1007/s004770050029> and Duah (2025, 2026) <doi:10.1016/j.spasta.2026.100974>.
Authors:
BMEmapping_1.2.2.9000.tar.gz
BMEmapping_1.2.2.9000.zip(r-4.7)BMEmapping_1.2.2.9000.zip(r-4.6)BMEmapping_1.2.2.9000.zip(r-4.5)
BMEmapping_1.2.2.9000.tgz(r-4.6-any)BMEmapping_1.2.2.9000.tgz(r-4.5-any)
BMEmapping_1.2.2.9000.tar.gz(r-4.7-any)BMEmapping_1.2.2.9000.tar.gz(r-4.6-any)
BMEmapping_1.2.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BMEmapping/json (API)
| # Install 'BMEmapping' in R: |
| install.packages('BMEmapping', repos = c('https://kinsprideduah.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kinsprideduah/bmemapping/issues
Datasets:
- casnowload - California Snow Load Data
- utsnowload - A detrended reliability-targeted design ground snow loads in Utah
bayesian-maximum-entropyspatial-interpolatio
Last updated from:d12ceb10cf. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 161 | ||
| source / vignettes | OK | 252 | ||
| linux-release-x86_64 | OK | 163 | ||
| macos-release-arm64 | OK | 206 | ||
| macos-oldrel-arm64 | OK | 149 | ||
| windows-devel | OK | 106 | ||
| windows-release | OK | 98 | ||
| windows-oldrel | OK | 98 | ||
| wasm-release | OK | 133 |
Exports:bme_cvbme_mapbme_predictprob_zkq_bme_cvq_bme_predictq_prob_zk
Dependencies:abindclassclassIntclicpp11DBIe1071farverFNNggplot2gluegridExtragstatgtableintervalsisobandKernSmoothlabelinglatticelifecycleMASSmvtnormproxyR6RColorBrewerRcpprlangs2S7scalessfsftimespspacetimestarsunitsvctrsviridisLitewithrwkxtszoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Leave-one-out cross validation (LOOCV) at hard data locations. | bme_cv |
| Create BMEmapping object | bme_map |
| BME prediction | bme_predict |
| California Snow Load Data | casnowload |
| Plot Method for BMEmapping Objects (ggplot2) | plot.BMEmapping |
| Posterior Density Estimation at a Single Location | prob_zk |
| Leave-one-out cross validation (LOOCV) at hard data locations using the random quantile approach. | q_bme_cv |
| BME prediction using the random quantile approach | q_bme_predict |
| Posterior Density Estimation at a Single Location | q_prob_zk |
| Summary Method for BME Cross-Validation Results | summary.BMEmapping |
| A detrended reliability-targeted design ground snow loads in Utah | utahsnowload utsnowload |
