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 |
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 El Capitan binaries: | r-release: deGradInfer_1.0.0.tgz |
OS X Mavericks binaries: | r-oldrel: deGradInfer_1.0.0.tgz |
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