deGradInfer: 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.

Package details

AuthorBenn Macdonald [aut], Frank Dondelinger [aut, cre]
MaintainerFrank Dondelinger <[email protected]>
Package repositoryView on CRAN
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deGradInfer documentation built on Dec. 6, 2017, 1:03 a.m.