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:

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