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deBInfer  

Bayesian Inference for Differential Equations
View on CRAN: Click here


Download and install deBInfer package within the R console
Install from CRAN:
install.packages("deBInfer")

Install from Github:
library("remotes")
install_github("cran/deBInfer")

Install by package version:
library("remotes")
install_version("deBInfer", "0.4.4")



Attach the package and use:
library("deBInfer")
Maintained by
Philipp H Boersch-Supan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-09-14
Latest Update: 2022-11-17
Description:
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.
How to cite:
Philipp H Boersch-Supan (2016). deBInfer: Bayesian Inference for Differential Equations. R package version 0.4.4, https://cran.r-project.org/web/packages/deBInfer. Accessed 07 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:31), 0.4.1 (2016-09-14 21:44), 0.4.2 (2018-04-18 17:38), 0.4.3 (2022-04-21 13:20)
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Complete documentation for deBInfer
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