Parameter Inference for Systems of Differential Equation

Efficient Bayesian parameter inference for systems of ordinary differential equations. The inference is based on adaptive gradient matching (AGM, Dondelinger et al. 2013 < http://proceedings.mlr.press/v31/dondelinger13a.pdf>, Macdonald 2017 < http://theses.gla.ac.uk/7987/1/2017macdonaldphd.pdf>), which offers orders-of-magnitude improvements in computational efficiency over standard methods that require solving the differential equation system. Features of the package include flexible specification of custom ODE systems as R functions, support for missing variables, Bayesian inference via population MCMC.


News

deGradInfer 1.0

Initial release of the package. Features include:

  • Efficient ODE parameter estimation using gradient matching and population MCMC.
  • Parallel tempering of parameter determining mismatch between gradients and ODE model.
  • Ability to deal with missing variables.
  • Ability to specify user-defined ODE systems.
  • Visualisation of MCMC evolution.

Reference manual

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install.packages("deGradInfer")

1.0.0 by Frank Dondelinger, a year ago


Browse source code at https://github.com/cran/deGradInfer


Authors: Benn Macdonald [aut] , Frank Dondelinger [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports deSolve, gdata, gptk, graphics, stats

Suggests testthat, knitr, rmarkdown, ggplot2


See at CRAN