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.

Version: 1.0.1
Depends: R (≥ 3.3.1)
Imports: deSolve, gdata, gptk, graphics, stats
Suggests: testthat, knitr, rmarkdown, ggplot2
Published: 2020-01-20
Author: Benn Macdonald [aut], Frank Dondelinger [aut, cre]
Maintainer: Frank Dondelinger < at>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: deGradInfer results


Reference manual: deGradInfer.pdf
Vignettes: ODE parameter inference


Package source: deGradInfer_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): deGradInfer_1.0.1.tgz, r-release (x86_64): deGradInfer_1.0.1.tgz, r-oldrel: deGradInfer_1.0.1.tgz
Old sources: deGradInfer archive


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