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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
|
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 |
Reference manual: | deBInfer.pdf |
Vignettes: |
Chytrid DDE example Logistic ODE example Speeding up parameter inference with compiled models |
Package source: | deBInfer_0.4.4.tar.gz |
Windows binaries: | r-devel: deBInfer_0.4.4.zip, r-release: deBInfer_0.4.4.zip, r-oldrel: deBInfer_0.4.4.zip |
macOS binaries: | r-release (arm64): deBInfer_0.4.4.tgz, r-oldrel (arm64): deBInfer_0.4.4.tgz, r-release (x86_64): deBInfer_0.4.4.tgz, r-oldrel (x86_64): deBInfer_0.4.4.tgz |
Old sources: | deBInfer archive |
Please use the canonical form https://CRAN.R-project.org/package=deBInfer 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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