deBInfer: Bayesian Inference for Differential Equations

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: 0.4.1
Depends: R (≥ 2.10), deSolve
Imports: truncdist, coda, RColorBrewer, MASS, stats, mvtnorm, graphics, grDevices, plyr, PBSddesolve, methods
Suggests: testthat, knitr, rmarkdown, devtools, R.rsp, microbenchmark, beanplot
Published: 2016-09-14
Author: Philipp H Boersch-Supan [aut, cre], Leah R Johnson [aut], Sadie J Ryan [aut]
Maintainer: Philipp H Boersch-Supan <pboesu at>
License: GPL-3
NeedsCompilation: no
Citation: deBInfer citation info
Materials: README
In views: Bayesian
CRAN checks: deBInfer results


Reference manual: deBInfer.pdf
Vignettes: Chytrid DDE example
Logistic ODE example
Speeding up parameter inference with compiled models
Package source: deBInfer_0.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: deBInfer_0.4.1.tgz
OS X Mavericks binaries: r-oldrel: deBInfer_0.4.1.tgz


Please use the canonical form to link to this page.