dMod: Dynamic Modeling and Parameter Estimation in ODE Models
The framework provides functions to generate ODEs of reaction
networks, parameter transformations, observation functions, residual functions,
etc. The framework follows the paradigm that derivative information should be
used for optimization whenever possible. Therefore, all major functions produce
and can handle expressions for symbolic derivatives. The methods used in dMod
were published in Kaschek et al, 2019, <doi:10.18637/jss.v088.i10>.
| Version: |
1.0.2 |
| Depends: |
cOde (≥ 1.0) |
| Imports: |
deSolve, rootSolve, ggplot2, parallel, stringr, plyr, dplyr, foreach, doParallel |
| Suggests: |
MASS, reticulate, pander, knitr, rmarkdown |
| Published: |
2021-01-27 |
| Author: |
Daniel Kaschek |
| Maintainer: |
Daniel Kaschek <daniel.kaschek at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Citation: |
dMod citation info |
| In views: |
DifferentialEquations |
| CRAN checks: |
dMod results |
Documentation:
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