Generate a list of models based on a single dataset and estimation method

Usage

tar_nlmixr_multimodel(
  name,
  ...,
  data,
  est,
  control = list(),
  table = nlmixr2est::tableControl(),
  env = parent.frame()
)

name: Symbol, name of the target. A target name must be a valid name for a symbol in R, and it must not start with a dot. Subsequent targets can refer to this name symbolically to induce a dependency relationship: e.g. tar_target(downstream_target, f(upstream_target)) is a target named downstream_target which depends on a target upstream_target and a function f(). In addition, a target's name determines its random number generator seed. In this way, each target runs with a reproducible seed so someone else running the same pipeline should get the same results, and no two targets in the same pipeline share the same seed. (Even dynamic branches have different names and thus different seeds.) You can recover the seed of a completed target with tar_meta(your_target, seed) and run tar_seed_set() on the result to locally recreate the target's initial RNG state.
...: Named arguments with the format "Model description" = modelFunction
data: nlmixr data
est: estimation method (all methods are shown by `nlmixr2AllEst()`). Methods can be added for other tools
control: The estimation control object. These are expected to be different for each type of estimation method
table: The output table control object (like `tableControl()`)
env: The environment where the model is setup (not needed for typical use)

A list of targets for the model simplification, data simplification, and model estimation.