![nonmem s matrix algorithmically singular nonmem s matrix algorithmically singular](https://www.researchgate.net/profile/Gavin-Kilpatrick/publication/221755193/figure/fig2/AS:337754199216129@1457538284418/Diagrammatic-representation-of-typical-1-recirculation-and-2-mammillary_Q320.jpg)
![nonmem s matrix algorithmically singular nonmem s matrix algorithmically singular](https://s1.manualzz.com/store/data/007496093_1-e2a8504732cb6d259c08eccdb2c4ba59.png)
The approach is similar to NONMEM’s mu-referencing. The order of these parameters do not matter.
![nonmem s matrix algorithmically singular nonmem s matrix algorithmically singular](https://www.researchgate.net/profile/Gavin-Kilpatrick/publication/221755193/figure/fig2/AS:337754199216129@1457538284418/Diagrammatic-representation-of-typical-1-recirculation-and-2-mammillary_Q640.jpg)
Nlmixr models are contained in a R function with two blocks: ini and model. Initial Estimates and boundaries for population parameters This language is inspired by both R and NONMEM, since these languages are familiar to many pharmacometricians. The nlmixr mini-modeling language (Domain Specific Language) attempts to address this issue by incorporating a common language. However, a model definition language that is common between estimation methods, and an output object that is uniform, will make it easier to switch between estimation routines and will facilitate interfacing output with external packages like xpose and other user-written packages. The unique syntax of each routine makes the routines themselves easier to maintain and expand, and allows interfacing with existing packages that are outside of nlmixr (like ). Sometimes, legacy estimation routines like have their own syntax that is outside of the control of the nlmixr package. Often the models are specified in ways that are most intuitive for one estimation routine, but do not make sense for another estimation routine. Nlmixr estimation routines have their own way of specifying models.