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>.
|Maintainer||Daniel Kaschek <email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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