admixr2 0.2.0

New features

New estimator 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).
Control objects now accept any nloptr algorithm; the default is chosen from the gradient mode, and grad/algorithm are reconciled automatically (#70).

Fix an infinite recursion (“evaluation nested too deeply” / “node stack overflow”) that aborted the first fit of an R session when a covariance matrix was requested (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).
Use the ML denominator (1/n_sim) consistently in the MC gradient kernels, matching the NLL (#48).
Fix parallel multi-restart dispatch for fork/PSOCK, and fix adirmc multi-restart (#45).
Guard non-positive predicted variance in the diagonal-NLL paths (#57).
Correct the FO diagonal omega gradient scaling, plus assorted plot, output-variable detection, caching, and worker-serialization fixes.

Add Gauss-Hermite sections across the vignettes and fix the pkgdown reference index so the documentation site builds (#79).

Declare minimum versions for the imported rxode2 (>= 5.1.2) and nlmixr2est (>= 6.0.1), and for the suggested nlmixr2 (>= 5.0.0) (used in examples and tests).

Initial release.
Monte Carlo estimator (est = "admc") via admControl().
Iterative Reweighting Monte Carlo estimator (est = "adirmc") via adirmcControl().
Analytical CRN gradient with sensitivity equations (grad = "sens").
Multi-restart parallelism via furrr/future.
Diagnostic plots: observed vs predicted mean/covariance, NLL trace, parameter trace.
traceplot() support: admixr2 fits populate the standard parHistData slot, so the nlmixr2 traceplot() generic works natively (best restart, natural scale, no burn-in marker).
Integrates with the nlmixr2/rxode2 ecosystem.