A Bayesian framework for parameter inference in differential equations. This approach offers a rigorous methodology for parameter inference as well as modeling the link between unobservable model states and parameters, and observable quantities. Provides templates for the DE model, the observation model and data likelihood, and the model parameters and their prior distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs to estimate the posterior distributions of the parameters and any derived quantities, including the model trajectories. Further functionality is provided to facilitate MCMC diagnostics and the visualisation of the posterior distributions of model parameters and trajectories.
Version: 0.4.4 Depends: R (≥ 3.5.0), deSolve Imports: truncdist, coda, RColorBrewer, MASS, stats, mvtnorm, graphics, grDevices, plyr, PBSddesolve, methods Suggests: testthat, knitr, rmarkdown, devtools, R.rsp, beanplot Published: 2022-11-17 DOI: 10.32614/CRAN.package.deBInfer Author: Philipp H Boersch-Supan [aut, cre], Leah R Johnson [aut], Sadie J Ryan [aut] Maintainer: Philipp H Boersch-Supan <pboesu at gmail.com> BugReports: https://github.com/pboesu/debinfer/issues License: GPL-3 URL: https://github.com/pboesu/debinfer NeedsCompilation: no Citation: deBInfer citation info Materials: README NEWS In views: Bayesian CRAN checks: deBInfer results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=deBInfer to link to this page.
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