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 <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.

Version: 1.0.0
Depends: R (≥ 3.3.1)
Imports: deSolve, gdata, gptk, graphics, stats
Suggests: testthat, knitr, rmarkdown, ggplot2
Published: 2017-12-05
Author: Benn Macdonald [aut], Frank Dondelinger [aut, cre]
Maintainer: Frank Dondelinger <fdondelinger.work at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: deGradInfer results

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Reference manual: deGradInfer.pdf
Vignettes: ODE parameter inference
Package source: deGradInfer_1.0.0.tar.gz
Windows binaries: r-devel: deGradInfer_1.0.0.zip, r-release: deGradInfer_1.0.0.zip, r-oldrel: deGradInfer_1.0.0.zip
OS X binaries: r-release: deGradInfer_1.0.0.tgz, r-oldrel: deGradInfer_1.0.0.tgz

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