Changelog

Source: NEWS.md

admixr2 0.4.1

CRAN release: 2026-09-16

New features

• A conditional source is cut on the span of both models, not the product grid – 729 studies become 81, where the analysis exponents are fixed.

• The projected design is admitted on a symbolic certificate: the covariate loading must differentiate to zero in every estimated parameter, or decline.

• A truncated span certifies its cloud before using it – against a coarser cloud and against integrands the recombination did not match – or declines.

• One rxSolve per group of studies, not one per study, across differing doses and schedules. A four-study fit: 126.4 s to 38.9 s, objective unchanged.

• The mean and covariance panels are per SOURCE, not per stratum. A conditional source is collapsed by the mixture law, so _s1/_s2 never reaches a figure.

• The panels name what V contains – BSV + covariate spread + sigma, read off the fit – and the predicted ribbon shows the pre-sigma part inside.

• A covariate the model does not read is dropped, not refused, so a nested pair shares one studies object and needs no fix(0).

• covMethod = "r,s": standard errors scored against the model’s own sampling law – an ADF sandwich H^-1 J H^-1, on all four estimators.

• v_denom declares which denominator a study’s V uses, per study, rather than a hand-applied (n-1)/n correction. Default "ml".

• sigdig now controls the fit, not just the output tables, and is opt-in: the default NULL leaves rxode2’s own tolerances alone.

• adfo differentiates its structural thetas analytically, so grad = "analytical" (LBFGS) is now its default.

• linCmt() models are supported at second order, by promotion to the explicit ODE form.

• Finite-difference steps are measured per parameter (Shi 2021), replacing the fixed pmax(abs(p), 0.1) * h scale.

• anova() on nested fits: the likelihood-ratio test on the objective difference against a chi-squared reference.

• A study can contribute as a published MODEL, not only as digitised aggregate data. No standard error is reported for a fit containing one.

• Covariate marginalisation over a declared distribution (covDist()), for admc and adgh, instead of solving at the covariate mean.

• A sparse-grid route for several covariates: cov_integration = "sparse" uses a Smolyak grid – 49 points against the product grid’s 81.

• A paper-shaped study API: admStudy()/admStudies() describe a source as a publication does, and admPopulation() reads a baseline table.

• The covariate integral is collapsed onto the directions it actually has – same answers, fewer design points.

• plot(fit, which = "covariate"): the fitted covariate effect, and each study’s mean residual against it – a slope is a bad covariate form.

• The effect panel compares the estimated effect against its sources: a dotted line, against each source’s own published model.

• A conditional source draws its OWN regression over the range it covers; a marginal one draws a whisker, because what it reported is a distribution.

• One mark per source per facet, not one per stratum, so a source conditional on sex no longer draws twice on every other covariate’s panel.

• Both covariate panels key colour on the source, so a source keeps one colour across the figure; its strata are joined in grey instead.

• A residual facet is dropped when the sources’ contrast is sampling noise, measured against a typical within-study 10th-90th.

• Point area is the study’s sample size on both covariate panels, with a legend. The residual panel encoded it already and said so nowhere.

• Whether a covariate is conditional or marginal is derived, not declared: conditional when the source’s own model ESTIMATED its coefficient.

• admMoments(fit) gives the observed and predicted first two moments per source, with the structural variance share and the standardised residual.

Changes that can move an existing fit

Several changes in this release alter results for scripts that do not name a new argument. None is a bug fix, so all are listed here rather than below.

• covDist(joint = ) and a population carrying its own sampler are refused, pending the vine-copula work; cor is the supported route to dependence.

• The sandwich’s G is evaluated at tau, not at the observed summary, so every covMethod = "r,s" standard error moves slightly.

• covMethod now defaults to "r,s", so reported standard errors change for every script that does not name it.

• Transform-both-sides endpoints are composed exactly, by quadrature, and their estimates move.

• The per-parameter Shi (2021) step is not optional, so any fit relying on a finite difference moves slightly.

• gill is removed from all four controls, superseded by the Shi (2021) step search.

• Forward finite differences are removed: grad = "fd" is now a central difference, and grad = "cfd" is gone.

• adirmcControl(grad = "fd") differences with grad_h, not a hard-coded 1e-6.

• adfoControl()’s new grad = "analytical" default brings the grad_bounds box with it.

• admStudy(stratify = ) is removed, conditioning being derived: a script naming fewer covariates than its model uses moves. covStrata() still takes it.

• range truncates a MARGINAL covariate’s declared distribution too, not only a conditional one’s: the enrolled span holds however the covariate is used.

• Nodes need an ESTIMATED coefficient, not just a covariate the model reads, so a fixed allometric exponent leaves weight marginal.

• fit$env$strataNodes is now one entry per covariate the model reads, rather than one number per fit, and anova() compares where the two overlap.

Bug fixes

• adgh on a no-IIV (n_eta = 0) model failed under covariate marginalisation, with a dimnames-length error from a phantom "eta." name.

• A DISCRETE covariate latently correlated with any other margin is refused, rather than integrated as if it were independent; a level is not a point.

• The estimated-effect curve and the source marks were different quantities for a staged model, so every source drew a constant factor off the fitted line.

• A by = source got no mark and no regression line on covariate_effect, its own model being reachable under no name the panels use.

• print() told a by = source its estimated coefficient was asserted, and that it conditions on nothing, contradicting its own reported by line.

• The no-enrolled-range warning fired for DISCRETE conditional covariates, where truncation is a no-op; it asked for a span for a two-level factor.

• A range given as c(lo =, hi =) was applied by the fit and ignored by the plot, and one keyed by covariate names without being a list is now refused.

• An unnamed range is resolved against the SOURCE’s conditional covariates, not the analysis-narrowed set: one studies object now serves a nested pair.

• The source marks and the source regression line took the FIRST assignment to a parameter, where the solve uses the last; a staged model drew a zig-zag.

• An enrolled range is refused against a cov_dist with its own joint sampler, rather than accepted and silently ignored.

• anova() skipped the resolution check when only one fit’s stamp was named, which is the pre-rename fit the unnamed comparison exists to catch.

• fit$env$strataNodes tells a marginalised covariate from one pinned at a single node, and reports the whole set of node counts rather than its maximum.

• A cov entry longer than one took the covariate_resid panel out, through a label the panel computed and never read.

• A free-scaled covariate facet could be ticked at another covariate’s levels; the breaks are read per covariate now.

• The diagnostic-plots article called an undefined function and could not be built.

• datagen() cut every study that declared a covariate distribution into nodes, having no model of its own to derive from. It needs stratify now.

• stratify = FALSE stopped reaching the spec, so a study that refused conditioning had one derived for it – the opt-out became its opposite.

• strata_nodes was recorded only when something was cut into nodes, so it could vary between studies of one fit – which anova() refuses to compare.

• anova() refused the nested pair a covariate test is made of, the null having dropped the term and so having no nodes; measured, they agree to 5e-05.

• range was silently dropped by a source conditional on nothing, the transcribed mean +/- SD it exists for.

• The rebuilt stratum sampler was discarded one line later, leaving correlated conditional margins to be drawn independently of each other.

• A continuous covariate conditional at strata_nodes <= 8 was drawn on a discrete axis, a dot per quadrature node and the ticks on that grid.

• An unnamed range crashed the covariate_effect panel out of existence, the error being caught and the panel reported as absent.

• A stratum’s source is recorded rather than recovered by regex, so two studies a user named a_s1 and a_s2 are no longer merged into one.

• admMoments() returned NULL where it documents an empty data frame.

• A source conditional on a covariate the analysis model does not read could not be reduced: the sampler’s inputs are kept, so it rebuilds on the subset.

• A discrete covariate latently correlated with another margin is refused, rather than integrated as if it were independent.

• A joint collapse probes Omega as well as the structural parameters before it freezes the design’s rank.

• Derivative-free fits no longer inherit nloptr’s loose xtol_rel = 1e-4.

• Parallel restarts could fail to read the compiled-model cache whenever a second R session was using admixr2 at the same time.

• adfo could report NA for every standard error on a fit that converged normally.

• A joint study normalised before the model was known kept NULL block outputs.

• .admCacheWrite() could delete another session’s valid cache entry.

• A sensitivity model that failed to build was reported as quietly as one refused by design.

• A fit that stops on the gradient box constraint now says so audibly.

• The order-2 linCmt() promotion did not run for a linCmt assigned to a variable, so adfo kept finite-differencing its structural thetas there.

• adghControl() accepted an invalid nloptr algorithm, and would hand a derivative-free one a gradient.

• adirmcControl() validated neither ci nor returnAdmr.

• A cache write that fails no longer discards the model it just compiled, or kills the fit.

• The session-ownership guard rejected nlmixr2est’s own sensitivity model unconditionally.

• The order-2 linCmt() promotion could write a theta’s value into the wrong THETA[k] slot.

• The gradient-box warning judged the fit against the wrong point, and stayed silent for the parameters most likely to need it.

• An explicit adfoControl(grad = "analytical") that cannot build a sensitivity model warns again.

• Normalising a study twice no longer leaves its endpoint unset.

• Dev-mode parallel restarts could not see any function this release introduced.

• Generated models are built under role-tagged names, in their own directory, and a cached one is checked before it is trusted.

• Normalising a study twice turned it into a joint (same-subject) study.

• Non-finite parameters no longer reach the ODE solver.

• A cache-key collision solved fits at another model’s fixed value.

• A stale sensitivity cache entry could survive a change to what it caches.

• linCmt() second-order promotion built its direction set from the pre-promotion model.

• A struct theta missing from the cached direction map crashed the fit.

• A transformed endpoint no longer pays for second-order compartments it cannot use.

• A parallel worker no longer walks on from a model it could not load.

• .admNLL() gained the non-finite screen the other estimators got.

• Compiled models are held in a session cache, instead of being reloaded from the disk cache on every call.

• The diagnostic panels solved a covariate study at the covariate mean, so its predicted moments lost the spread the observed ones carry.

• A covariate the model no longer reads keeps its declared distribution, so the residual panel can still plot a deleted term against it.

• Stratified studies are titled by the covariate value they condition at, not a bare _s1/_s2 index.

• No effect panel for a covariate the model does not estimate – a fixed allometric exponent is not a finding to check agreement on.

• A marginalised level mix sits at its mean, not its median, so an even binary split no longer greys a studied level or loses its residual facet.

• The lm trend is guarded per covariate, not across the figure, so a facet with two studies gets no line through its two points.

• A source keeps one colour across both covariate panels, and the effect panel’s curves split only on covariates some study conditions at a point.

• Merging a conditioned mark into a marginal one no longer invents a spread for it; the merged mark is drawn as the conditioned one it contains.

• A shaded discrete level no longer costs the axis its level-only ticks, which had a three-level factor ticked at 0.5 and 1.5.

• The covariate panels’ legends keep a fixed order. ggplot2 sorts equal-priority guides by a hash, which is not stable across sessions.

• Coincident marks from different sources are no longer merged, which invented labels like 3 studies for studies that share only a position.

• An eighth source gets its own colour instead of the first one’s, via an HCL ramp past the seven Okabe-Ito hues.

• Black is reserved for the fit in the covariate panels, so no source is drawn in the colour of the thing it is compared against.

Internal changes

• vignette("multiple-studies") combines two trials that actually differ, where it split one dataset into two statistically identical halves.

• Compiled objects are no longer tracked, .gitignore having named them since before they were committed.

• The covariate panels are built by .admCovEffectPanel() and .admCovResidPanel(), not inline in plot.admFit(), which loses 170 lines.

• Visual regression tests for the covariate panels, on synthetic studies and no fit. Requires the new vdiffr suggested dependency.

• vignette("diagnostic-plots") renders the covariate panels from its own three-source fit, instead of describing them in prose.

• print.admFit() reaches nlmixr2est’s printer through getS3method(), not an asNamespace() lookup.

• The sensitivity-model builder takes an order argument: 1L is the existing direction set, 2L adds the cross block adfo needs.

• CI: R-CMD-check gained a workflow_dispatch trigger and a dependency cache-version bump.

admixr2 0.4.0

New features

• Student-t residual error (cp ~ add(a) + t(nu)) is supported, as nlmixr2’s scale family: the residual is scale * T_nu.

• The transform-both-sides transforms call rxode2’s own kernel, instead of an inline reimplementation.

• .admBackTransform() uses rxode2::probitInv() instead of an inline low + (high - low) * pnorm(p). Numerically identical.

• New resid_nodes control argument on all four estimators: the Gauss-Hermite node count for a transform-both-sides residual integral.

• New vignette: “Choosing a residual error model” (vignette("error-models", package = "admixr2")).

Bug fixes

• Dropped the qs2 dependency: the compiled-model and sensitivity disk caches use saveRDS()/readRDS().

• IRMC importance-sampling shift was wrong for every non-exp mu-referenced theta.

• A fix()ed prediction-dependent residual lost its gradient.

• binom(20L, p) was refused as a non-constant size.

• A non-positive nbinomMu size now gives a clear domain error.

• beta precision denominator is guarded against a zero draw.

• Standard errors: sigma SEs were uninitialised memory, and omega was excluded.

• Omega and sigma standard errors are reported, on the scale the estimates are printed on.

• A printed standard error now belongs to the parameter it is printed beside.

• Count endpoints could not be fitted with the default gradient.

• The covariance Hessian used the starting lambda for a transformed endpoint.

• beta() endpoints were only ever right on the plain NLL path.

• The ar() and ordinal guards judged every study, not the affected one.

• Ordinal categories were grouped by exact floating-point time equality.

• The moment expansion and its derivative capped the same pole differently.

• A parallel worker could invert a transform with another model’s lambda.

• plot() back-transformed three residual roles on the wrong scale.

• A binom size written as a model constant was refused as non-constant.

• A count or beta endpoint alongside another endpoint is now refused.

• datagen() refuses an ordinal endpoint.

• resid_nodes no longer changes what a positional call means.

• The ordinal same-time grouping is defined once.

• Endpoints transformed differently from one another refused the sensitivity model.

• A joint (same-subject) study had no aggregate diagnostics.

• Documented: an adfo standard error describes scatter, not accuracy.

• A failed covariance is no longer silent.

• A study ev containing observation records now warns.

• Residual parameters fixed with fix() were silently dropped.

• A prop()/pow() term on a transform-both-sides endpoint contributed nothing.

• The post-fit covariance was a Hessian of the wrong objective for several error models.

• adfo dropped ar() from its objective while keeping it in the gradient.

• An out-of-support transform aborted the whole fit.

• The sensitivity-model cache could serve a stale transform spec.

• 0^negative in the moment expansion.

• ordinal endpoints are supported.

• dv() is now refused.

• ar() combined with prop()/pow()/combined is now refused.

• Known upstream issue: simulating an ar() fit will not reproduce its covariance.

• Prediction-dependent residual error is composed correctly (prop(), pow(), lnorm(), combined).

• lnorm() analytic gradients were computed against the wrong quantity.

• delay() (DDE) models get an accurate sensitivity solve.

Internal changes

• The post-fit covariance’s reported-scale rotation and its non-PD omega fallback are now shared helpers.

• The residual variance’s dependence on (mu, var_f) is computed once per study/unit, not three times.

• The residual V-composition tail is one helper, .admApplyResidTail().

admixr2 0.3.0

New features

• Analytical gradients for non-mu-referenced (“unpaired”) structural thetas.

• Residual error models pow(), addPow() and combined1() are supported, with analytical gradients.

• Multi-compartment fitting (multiple observed outputs).

• Parallel restarts run on mirai daemons.

• New nDisplayProgress control argument.

• The aggregate-data estimators carry type and description attributes, classifying them as Model Based Meta Analysis.

Bug fixes

• pow() models no longer fit the wrong residual model, silently.

• combined1() is honoured.

• An unrepresentable residual model is refused rather than approximated.

• propT/propF, norm/dnorm and dlnorm/logn/dlogn no longer emit spurious approximation warnings.

• Lognormal residual error is applied to the plotted predicted mean.

• The solver progress bar no longer appears during covariance/gradient batches.

• Hard-coded numeric constants in a model({}) block are no longer zeroed.

• adgh computes gradients for non-mu-referenced (unpaired) structural thetas.

• Parallel restarts under devtools::load_all() warn once about the installed package.

Internal changes

• adgh gradient-mode fits are about twice as fast: the objective and the gradient share one solve.

• Model loading and per-fit memory follow nlmixr2est’s own conventions.

• admClearCache() is removed; use rxode2::rxClean().

• print() on a fit no longer writes into rmarkdown’s namespace.

admixr2 0.2.0

CRAN release: 2026-07-02

New features

• New estimator est = "adgh": deterministic Gauss-Hermite quadrature over the random-effects prior, via adghControl(). Noise-free and exact (#65).
• datagen() gains FO-approximated population moments (method = "fo") 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 accept any nloptr algorithm; the default is chosen from the gradient mode, and grad/algorithm are reconciled (#70).

Bug fixes

• Fix an infinite recursion that aborted the first fit of an R session when a covariance matrix was requested. All four estimators (#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.

Documentation

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

Dependencies

• Declare minimum versions for rxode2 (>= 5.1.2), nlmixr2est (>= 6.0.1) and the suggested nlmixr2 (>= 5.0.0).

admixr2 0.1.0

CRAN release: 2026-06-02

• 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 generic works natively.
• Integrates with the nlmixr2/rxode2 ecosystem.