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: | 1.0.1 |
| 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 <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_1.0.1.tar.gz |
| Windows binaries: | r-devel: deGradInfer_1.0.1.zip, r-release: deGradInfer_1.0.1.zip, r-oldrel: deGradInfer_1.0.1.zip |
| macOS binaries: | r-release: deGradInfer_1.0.1.tgz, r-oldrel: deGradInfer_1.0.1.tgz |
| Old sources: | deGradInfer archive |
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