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 <>, Macdonald 2017 <>), 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.


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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1.0.1 by Frank Dondelinger, 2 years ago

Browse source code at

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