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.

Maintainer: Philipp H Boersch-Supan <pboesu at gmail.com>

Author(s): Philipp H Boersch-Supan*, Leah R Johnson*, Sadie J Ryan*

Install package and any missing dependencies by running this line in your R console:

install.packages("deBInfer")

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
Enhances
Linking to
Reverse
depends
Reverse
imports
Reverse
suggests
Reverse
enhances
Reverse
linking to

Package deBInfer
Materials
URL
Task Views Bayesian
Version 0.4.1
Published 2016-09-14
License GPL-3
BugReports
SystemRequirements
NeedsCompilation no
Citation
CRAN checks deBInfer check results
Package source deBInfer_0.4.1.tar.gz