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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 |
| Reference manual: | dMod.pdf |
| Vignettes: |
dMod |
| Package source: | dMod_1.0.2.tar.gz |
| Windows binaries: | r-devel: dMod_1.0.2.zip, r-release: dMod_1.0.2.zip, r-oldrel: dMod_1.0.2.zip |
| macOS binaries: | r-release (arm64): dMod_1.0.2.tgz, r-oldrel (arm64): dMod_1.0.2.tgz, r-release (x86_64): dMod_1.0.2.tgz, r-oldrel (x86_64): dMod_1.0.2.tgz |
| Old sources: | dMod archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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