spDBL: Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models
Provides tools for Bayesian learning of spatiotemporal dynamical mechanistic models.
Includes methods for parameter estimation, simulation, and inference using hierarchical and
state-space modeling approaches, following Banerjee, Chen, Frankenburg and Zhou (2025)
<https://jmlr.org/papers/v26/22-0896.html>.
| Version: |
1.0.2 |
| Depends: |
R (≥ 4.0) |
| Imports: |
Rcpp, matrixsampling, invgamma, deSolve, ReacTran, LaplacesDemon, matrixcalc, mniw, utils, stats, ggpubr, ggplot2, readr, magrittr, rlang, scales |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
testthat (≥ 3.0.0), here, knitr, rmarkdown |
| Published: |
2026-06-09 |
| DOI: |
10.32614/CRAN.package.spDBL (may not be active yet) |
| Author: |
Xiang Chen [aut, cre],
Sudipto Banerjee [aut] |
| Maintainer: |
Xiang Chen <xiangchen at ucla.edu> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
yes |
| Citation: |
spDBL citation info |
| Materials: |
README |
| CRAN checks: |
spDBL results |
Documentation:
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