
There are quite a few R packages available to simulate (pharmacometric) models in R. This package is not intended to be yet another simulation package, but rather a package that can be used alongside available simulation frameworks. This means that the package does not include functionality to perform the actual simulations, but rather amplifies it:
deSolve, rxode2, nlmixr2 and
mrgsolve)shiny applications of (translated)
modelsCurrently the package is under active development and can be installed using:
devtools::install_github("LeidenAdvancedPKPD/amp.sim")The pkgdown website contains various articles on how to use the package and what should be taken into account when translating models.
This package was initially developed as an in-house package at LAP&P, and was started in 2017. Various versions were developed, where many people within LAP&P helped in making the package better and more robust. Without them this package wouldn’t be possible!
The main packages in R to perform simulations for pharmacometrics are
nlmixr2 and mrgsolve. The deSolve
package is a more general solution which could require some optimization
in case of large/complex simulations. Besides simulations in R, NONMEM
is a tool often used to perform simulations as well. Because NONMEM
itself is low level, R packages like NMSim can make these
type of simulations much easier. For translating models, there are
solutions like nonmem2mrgsolve, pharmpy and
nonmem2rx (called in this package).
This package only amplifies packages like nlmixr2 and
mrgsolve. The functionality for NONMEM simulations differ
from the NMSim package. The amp.sim package is
more low level and has no advanced functionality to start/run NONMEM.
Implementation is centered towards the dataset, in which a control
stream is tailored towards. There is an overlap with the model
translation options, although this package aims to combine different
translations and extends this with other simulation tools and possible
shiny implementation.
To test how well this package can translate NONMEM models a separate repository is available here. In here dozens of models from the ddmore repository are tested. This test indicate that almost all translations are successful. The main reason some models could not be fully translated is mainly caused by a subtle difference in model syntax which can be fixed quite easily.