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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:

Reference manual: spDBL.html , spDBL.pdf
Vignettes: PDE Emulation with FFBS (source, R code)

Downloads:

Package source: spDBL_1.0.2.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): spDBL_1.0.2.tgz, r-oldrel (arm64): spDBL_1.0.2.tgz, r-release (x86_64): spDBL_1.0.2.tgz, r-oldrel (x86_64): spDBL_1.0.2.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=spDBL to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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