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est = "adgh": deterministic Gauss-Hermite
quadrature over the random-effects prior, configured via
adghControl(). The objective is noise-free (no Monte Carlo
draws), the analytical gradient is exact, and it is unbiased at any IIV
magnitude. For models with up to ~4 random effects it is the fastest
exact estimator (#65).datagen() gains FO-approximated population moments
(method = "fo", matching est = "adfo") for
design evaluation and optimal-design work (#56).adirmcControl(kappa_method = "linearized_gh"):
GH-averaged kappa baseline for the IRMC inner loop.admClearCache() prunes the session-level compiled-model
cache (#10).nloptr algorithm; the
default is chosen from the gradient mode, and
grad/algorithm are reconciled automatically
(#70).covMethod = "r").
Accessing ui$simulationModel left a self-referential
compiled-model object in ui$meta, which nlmixr2’s
ui-cloning during fit assembly could not traverse. admixr2 now clears
that transient artifact in .admLoadModel(), keeping the ui
in the canonical state nlmixr2 expects. Affected all four estimators
(adfo/admc/adgh/adirmc)
(#81).1/n_sim) consistently in the MC
gradient kernels, matching the NLL (#48).adirmc multi-restart (#45).rxode2 (>= 5.1.2) and
nlmixr2est (>= 6.0.1), and for the suggested
nlmixr2 (>= 5.0.0) (used in examples and tests).est = "admc") via
admControl().est = "adirmc") via adirmcControl().grad = "sens").furrr/future.traceplot() support: admixr2 fits populate the standard
parHistData slot, so the nlmixr2 traceplot()
generic works natively (best restart, natural scale, no burn-in
marker).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.
Health stats visible at Monitor.