Package 'nlmixr2est'

The fitted model object from which to retrieve the simulation model.

dispatch environment (provides 'ui', 'control', 'data', 'table')

estimation-method string; also the slot the raw fit is stored under (e.g. '"nlm"' -> '.ret[["nlm"]]')

'function(ui, dataSav)' running the optimizer

'function(fit, ui)' returning the full theta vector

'function(env)' translating the control to a focei-style control for output assembly

rxode2 control flag name that short-circuits and returns the raw optimizer result (e.g. '"returnNlm"')

optional 'function(fit)' returning the raw objective; when 'NULL' the driver does not set '$objective' (a 'postSetup' closure did)

'function(fit)' returning the '$message' (default 'fit$message')

when TRUE, re-emit the warnings collected from 'fitModel' via 'warning()' (nlm does this; the others do not)

'$extra' print string, or a 'function(control)' returning it

when TRUE, run '.nlmFamilyAdjustOutput()'

optional 'function(ret, ui, fitList)' returning a modified 'ret', run right after the raw fit is stored and before '.nlmFamilyAdjustOutput()' (for methods that set cov/covMethod/objective with custom values)

rxode2 ui

estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools

nlmixr data

The estimation control object. These are expected to be different for each type of estimation method

rxode2 ui

estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools

nlmixr data

The estimation control object. These are expected to be different for each type of estimation method

model function/object

estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools

nlmixr data

The estimation control object. These are expected to be different for each type of estimation method

rxode2 ui

estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools

nlmixr data

The estimation control object. These are expected to be different for each type of estimation method

rxode2 ui

estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools

nlmixr data

The estimation control object. These are expected to be different for each type of estimation method

rxode2 ui

estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools

nlmixr data

The estimation control object. These are expected to be different for each type of estimation method

rxode2 ui model

estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools

nlmixr data

The estimation control object. These are expected to be different for each type of estimation method

rxode2 ui model

estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools

nlmixr data

The estimation control object. These are expected to be different for each type of estimation method

nlmixr2 fit without WRES/CWRES

Boolean indicating if the focei objective function is added. If not the foce objective function is added.

Boolean indicating if the original fit object should be updated. By default this is true.

Environment that should be checked for object to update. By default this is the global environment.

nlmixr2 fit object

Boolean indicating if original object should be updated. By default this is TRUE.

'tableControl()' list of options

Other ignored parameters.

Environment that should be checked for object to update. By default this is the global environment.

nlmixr2 family of objects

Update the object (default FALSE)

Saved data from

Internal theta/eta parameters

a 'tableControl()' list of options

Character Vector of items to keep

Character Vector of items to drop or NULL

Environment to search for updating

Random seed for the ADVI optimization (reparameterization sampling); default 42. The Monte-Carlo gradient is stochastic, so a fixed seed makes every fit reproducible. Reparameterization noise is drawn from a counter-based stream keyed by the global iteration index, so a shorter run is a bit-for-bit prefix of a longer one and results are independent of the number of cores.

Total number of ADVI (stochastic gradient ascent) iterations.

Number of Monte-Carlo samples used to approximate the ELBO gradient at each iteration (the paper's 'M'; typically 1-10).

Variational family in the unconstrained space. '"fullRank"' (default) uses a block full-rank Gaussian: a dense 'neta x neta' Cholesky factor per subject plus a dense block over the population vector (mean-field across blocks). '"meanField"' uses a fully factorized (diagonal) Gaussian. Mean-field is faster but is known to underestimate marginal variances.

When 'TRUE' (default) run a variational-EM hybrid: the variational posterior covers the per-subject etas only, and the population parameters (thetas / omega / residual error) are point estimates maximized by the ADVI gradient (stochastic maximum likelihood); output semantics match FOCEi/SAEM. When 'FALSE' run full Bayes: the variational posterior also covers the unconstrained population vector, with flat priors.

Stochastic optimizer. ‘"advi"' (default) uses the paper’s adaptive step-size sequence (Eqs 10-11); '"adam"' uses Adam.

When 'TRUE' (default) adaptively choose the step-size scale 'eta' by a short search over 'etaCandidates' before the main loop; when 'FALSE' use a fixed 'eta' (the first 'etaCandidates' entry).

Candidate step-size scales searched when 'adaptEta' is 'TRUE' (the paper searches 'c(0.01, 0.1, 1, 10, 100)').

Stabilizing constant 'tau > 0' in the step-size denominator (paper Eq 10); the step-size is insensitive to it.

Weighting 'alpha' in (0, 1) of new vs old gradient information in the step-size memory recursion (paper Eq 11).

Convergence tolerance on the relative change in the ELBO; the loop may stop early once the change stays below this. '0' disables early stopping (run all 'iters').

Inner likelihood used for the per-subject objective and gradient, run through the FOCEi inner interface: '"focei"' (default), '"foce"', '"focep"', or '"laplace"'.

When 'TRUE' return the raw ADVI optimization object instead of the nlmixr2 fit.

Optional warm-resume state: a previous 'est="advi"' fit (or its '$env$adviState'). The optimization continues from that state for 'iters' more iterations, bit-for-bit identical to a single fresh run of the combined length (the counter-based RNG is keyed by the global iteration index).

Either a scalar print-frequency ('0' = suppress, '1' (default) = every evaluation, 'N' = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to 'iterPrintControl(every = print, ncol = printNcol, useColor = useColor)'.

Logical (or 'NULL') emit ANSI bold/color escapes in the iteration print. 'NULL' (default) defers to [crayon::has_color()].

Integer (or 'NULL') parameter columns per row before wrapping. 'NULL' (default) uses 'floor((getOption("width") - 23) / 12)'.

Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of each individual's gradient cross-product (evaluated at the individual empirical Bayes estimates).

"sa" (default) Use the stochastic-approximation Fisher Information Matrix. After estimation, a dedicated covariance phase (nSaCov iterations) holds the parameters at the converged estimate and keeps resimulating the individual parameters, Monte-Carlo averaging the Louis observed-information integrand into a converged FIM decoupled from the cooling schedule (the approach used by Monolix; Kuhn & Lavielle 2005). Always includes every estimated population parameter (theta, the Omega diagonal variances, and residual).

"analytic" Compute the FOCEI analytic observed-information covariance at the converged SAEM estimates. When the model is out of analytic-covariance scope (e.g. linCmt(), a non-normal likelihood, or a non-SD IOV parameterization) or the result is not positive definite, it falls back to the linearized Fisher information (linFim) with a message.

"linFim" Use the Linearized Fisher Information Matrix to calculate the covariance.

"fim" Use the Fisher Information Matrix accumulated during SAEM estimation to calculate the covariance. Like sa it inverts the observed information to a full theta + Omega diagonal + residual covariance, but uses the (noisier) estimation-phase matrix rather than a dedicated cov phase.

For both fim and sa the simulation-based Fisher information covers the structural theta, the Omega diagonal variances, and additive residual error. Off-diagonal Omega covariances and proportional/combined residual error are not estimated reliably by the simulation FIM (the complete-data correction is unstable when between-subject variability dominates the residual), so those variance-block standard errors are spliced in from the linearized FIM (linFim).

"r,s" Uses the sandwich matrix to calculate the covariance, that is: $R^-1 \times S \times R^-1$

"r" Uses the Hessian matrix to calculate the covariance as $2\times R^-1$

"s" Uses the crossproduct matrix to calculate the covariance as $4\times S^-1$

"" Does not calculate the covariance step.

Optimize the rxode2 expression to speed up calculation. By default this is turned on.

Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

Type of additive-plus-proportional error: '"combined1"', where standard deviations add:

$y = f + (a + b\times f^c) \times \varepsilon$

; or '"combined2"', where variances add:

$y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon$

. Here y = observed, f = predicted, a = additive sd, b = proportional/power sd, c = power exponent (1 in the proportional case).

This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE

Should the object have compressed items

is a boolean to indicate if the objective function should be adjusted to be closer to NONMEM's default objective function. By default this is TRUE

Confidence level for some tables. By default this is 0.95 or 95% confidence.

Specifies the "significant digits" that the ode solving requests. When specified this controls the relative and absolute tolerances of the ODE solvers. By default the tolerance is 0.5*10^(-sigdig-2) for regular ODEs. For the sensitivity equations the default is ⁠0.5*10\^(-sigdig-1.5)⁠ (sensitivity changes only applicable for liblsoda). This also controls the atol/rtol of the steady state solutions. The ssAtol/ssRtol is ⁠0.5*10\^(-sigdig)⁠ and for the sensitivities ⁠0.5*10\^(-sigdig+0.625)⁠. By default this is unspecified (NULL) and uses the standard atol/rtol.

Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3.

The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.

Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.

The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced

When 'TRUE' (default), only subjects whose ODE solve produced NaN/Inf have their tolerances relaxed, and the relaxed tolerance persists across optimizer calls (sticky). When 'FALSE', all subjects have their tolerances relaxed on each retry and tolerances are reset afterward.

Controls how dosing/event-parameter ('alag', 'F', 'rate', 'dur') sensitivities are computed for THETA/ETA gradients: ‘"jump"' (default) uses rxode2’s analytic event sensitivities; '"fd"' uses the legacy finite-difference behavior.

'rxode2' ODE solving options during fitting, created with 'rxControl()'

Other arguments to control SAEM.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Number of Gauss-Hermite adaptive quadrature points. '0' disables AGQ; '1' is equivalent to Laplace. Cost grows quickly with ETAs: once the EBE is found, expect 'nAGQ^neta' (even 'nAGQ') or '(nAGQ^neta)-1' (odd 'nAGQ') additional evaluations per subject.

Parameters used in the default 'foceiControl()'

boolean, Interaction term for the model, in this case the default is 'TRUE'; For adaptive quadrature, with normal distribution the Hessian is calculated with the foce(i) approximation

The lower bound for adaptive quadrature log-likelihood. By default this is -Inf; in the original nlmixr's gnlmm it was -700.

The upper bound for adaptive quadrature log-likelihood. By default this is Inf; in the original nlmixr's gnlmm was 400.

Fit object

Fit object

Number of points for the quadratic approximation to the objective; must be in '[n+2, (n+1)(n+2)/2]'. Defaults to 'min(n*2, n+2)'.

Initial trust region radius (with 'rhoend', must satisfy '0 < rhoend < rhobeg'). Defaults to 'min(0.95, 0.2*max(abs(par)))'; adjusted upward if smaller than 'abs(upper-lower)/2'.

Final trust region radius. When 'NULL' (default) it is derived from 'sigdig' the way 'foceiControl()' does ('10^(-sigdig)'); otherwise the minqa '1e-6*rhobeg' default applies.

Controls amount of printing ('0'=none, '1'=start/end only, '2'=each new rho, '3'=every function evaluation, '>3'=every 'iprint' evaluations). Default '0'.

The maximum allowed number of function evaluations. If this is exceeded, the method will terminate.

return the bobyqa output instead of the nlmixr2 fit

The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.

Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.

The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced

When 'TRUE' (default), only subjects whose ODE solve produced NaN/Inf have their tolerances relaxed, and the relaxed tolerance persists across optimizer calls (sticky). When 'FALSE', all subjects have their tolerances relaxed on each retry and tolerances are reset afterward.

Logical (or 'NULL') emit ANSI bold/color escapes in the iteration print. 'NULL' (default) defers to [crayon::has_color()].

Integer (or 'NULL') parameter columns per row before wrapping. 'NULL' (default) uses 'floor((getOption("width") - 23) / 12)'.

Either a scalar print-frequency ('0' = suppress, '1' (default) = every evaluation, 'N' = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to 'iterPrintControl(every = print, ncol = printNcol, useColor = useColor)'.

Parameter normalization/scaling used to get scaled initial values for scaleType, of the form Vscaled = (Vunscaled-C1)/C2 (see Feature Scaling; rescale2 follows the OptdesX manual): "rescale2" scales all parameters to (-1, 1); "rescale" (min-max) scales to (0, 1); "mean" centers on the mean with range (0, 1); "std" standardizes by mean/sd; "len" scales to unit (Euclidean) length; "constant" performs no normalization (C1=0, C2=1).

The scaling scheme for nlmixr2: "nlmixr2" (default) scales as (current-init)*scaleC[i] + scaleTo, with scaleTo from normType and scales from scaleC; "norm" uses the simple scaling from normType; "mult" scales multiplicatively as current/init*scaleTo; "multAdd" scales linearly ((current-init)+scaleTo) for parameters in an exponential block (e.g. exp(theta)) and multiplicatively otherwise.

Maximum value of the scaleC to prevent overflow.

Minimum value of the scaleC to prevent underflow.

Scaling constant used with scaleType="nlmixr2"; when not specified, chosen by parameter type to keep gradient sizes similar on a log scale: '1' for exp()-transformed/power/boxCox/ yeoJohnson parameters, '0.5*abs(est)' for additive/proportional/ lognormal error parameters, 'abs(1/digamma(est+1))' for factorials, and 'log(abs(est))*abs(est)' for log-scale parameters. May be set explicitly per parameter if these defaults don't apply well.

Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed.

'rxode2' ODE solving options during fitting, created with 'rxControl()'

Optimize the rxode2 expression to speed up calculation. By default this is turned on.

Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

Type of additive-plus-proportional error: '"combined1"', where standard deviations add:

$y = f + (a + b\times f^c) \times \varepsilon$

; or '"combined2"', where variances add:

$y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon$

. Here y = observed, f = predicted, a = additive sd, b = proportional/power sd, c = power exponent (1 in the proportional case).

This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE

Should the object have compressed items

Method for calculating the covariance. "r" (the default) uses nlmixr2's nlmixr2Hess() Hessian; "" skips the covariance step.

is a boolean to indicate if the objective function should be adjusted to be closer to NONMEM's default objective function. By default this is TRUE

Confidence level for some tables. By default this is 0.95 or 95% confidence.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3.

Controls how dosing/event-parameter ('alag', 'F', 'rate', 'dur') sensitivities are computed for THETA/ETA gradients: ‘"jump"' (default) uses rxode2’s analytic event sensitivities; '"fd"' uses the legacy finite-difference behavior.

Ignored parameters

data to transform

Cox-box lambda parameter

Matrix to be Factorized.

Tolerance; Algorithm suggests (.Machine$double.eps) ^ (1 / 3), default

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Interaction term for the model, in this case the default is 'FALSE'; it cannot be changed, use 'focei' instead

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Ignored parameters

Precision of estimate for n1qn1 optimization.

Number of iterations for n1qn1 optimization.

Maximum number of L-BFGS-B optimization for outer problem.

Number of function evaluations for n1qn1 optimization.

Either a scalar print-frequency ('0' = suppress, '1' (default) = every evaluation, 'N' = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to 'iterPrintControl(every = print, ncol = printNcol, useColor = useColor)'.

Integer (or 'NULL') parameter columns per row before wrapping. 'NULL' (default) uses 'floor((getOption("width") - 23) / 12)'.

Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed.

Scale the initial objective function to this value. By default this is 0 (meaning do not scale)

Parameter normalization/scaling used to get scaled initial values for scaleType, of the form Vscaled = (Vunscaled-C1)/C2 (see Feature Scaling; rescale2 follows the OptdesX manual): "rescale2" scales all parameters to (-1, 1); "rescale" (min-max) scales to (0, 1); "mean" centers on the mean with range (0, 1); "std" standardizes by mean/sd; "len" scales to unit (Euclidean) length; "constant" performs no normalization (C1=0, C2=1).

The scaling scheme for nlmixr2: "nlmixr2" (default) scales as (current-init)*scaleC[i] + scaleTo, with scaleTo from normType and scales from scaleC; "norm" uses the simple scaling from normType; "mult" scales multiplicatively as current/init*scaleTo; "multAdd" scales linearly ((current-init)+scaleTo) for parameters in an exponential block (e.g. exp(theta)) and multiplicatively otherwise.

Maximum value of the scaleC to prevent overflow.

Minimum value of the scaleC to prevent underflow.

Length-2 increasing pair 'c(low, high)' (default ‘c(0.1, 10)'). Each 'theta'’s derivative-based scaling constant ('1/|init|' for a linear parameter, or the transform-specific formula) is kept when it lands inside this band, and otherwise replaced by the parameter's native magnitude '|init|'. This catches the singular cases – '1/|init|' blowing up for a small covariate initial estimate, 'log()' at init '1', 'logit' at the interval midpoint, 'factorial'/'gamma' at a digamma zero – while leaving the well-scaled common case (and its results) untouched.

Scaling constant used with scaleType="nlmixr2"; when not specified, chosen by parameter type to keep gradient sizes similar on a log scale: '1' for exp()-transformed/power/boxCox/ yeoJohnson parameters, '0.5*abs(est)' for additive/proportional/ lognormal error parameters, 'abs(1/digamma(est+1))' for factorials, and 'log(abs(est))*abs(est)' for log-scale parameters. May be set explicitly per parameter if these defaults don't apply well.

Number to adjust the scaling factor by if the initial gradient is zero.

Forward difference tolerances (relative, absolute); step size h = abs(x)*derivEps[1] + derivEps[2].

Derivative method for the outer problem: "switch", "central", or "forward". "switch" starts forward and toggles to central when abs(delta(OFV)) <= derivSwitchTol.

The tolerance to switch forward to central differences.

indicates the method for calculating the derivatives while calculating the covariance components (Hessian and S).

Method for calculating the covariance. "r,s" (the default) is the sandwich estimator (see below). "analytic" uses the exact analytic observed-information R-matrix (reported as $R^{-1}$) and additionally returns the residual and Omega standard errors; it covers FOCEI/FOCE fits with additive, proportional, or combined error, mu-referenced/covariate/other structural parameters (and non-mu-referenced etas), and SD-scale inter-occasion variability, and emits a message and falls back to the finite-difference Hessian for anything out of scope (FO, nAGQ > 1, censoring, DV-transformed error, bounded-parameter transforms, a structural theta shared by two etas, non-SD iovXform, or a pure-proportional variance that vanishes at a near-zero prediction). The finite-difference methods use R (the Hessian) and S (the sum of individual gradient cross-products at the empirical Bayes estimates): "r,s" sandwich (solve(R)%*%S%*%solve(R)), "r" Hessian-based (solve(R)), "s" cross-product-based (solve(S)), or "" to skip the covariance step. "sa" (SAEM Louis stochastic-approximation FIM) and "imp" (importance-sampling Monte-Carlo observed information) are also accepted for any method; they are computed post-fit at the converged estimates by the decoupled recompute engine.

absolute/relative ODE tolerance for the covariance solves – the augmented-sensitivity solves behind covMethod="analytic" and the perturbed solves behind the finite-difference methods. NULL (default) derives a tight tolerance from sigdig; supply a number to override it.

shape of fit$cov. TRUE (default) installs the full theta + residual sigma + Omega covariance (assembled analytically for covMethod="analytic", or by central finite differences over the same parameter set otherwise). For the finite-difference methods it follows covMethod: "r,s" is the full sandwich solve(Rfull) %*% Sfull %*% solve(Rfull), "s" is solve(Sfull), "r" is solve(Rfull). FALSE installs only the structural-theta block (the historical shape).

When TRUE, compute the outer (population) gradient analytically from Almquist (2015) sensitivity equations instead of by finite differences, and use the Eq-48 random-effect extrapolation for the next inner-problem starting values. Requires an analytic-scope model. Conditionally Gaussian endpoints route through the general (f,R) assembler, which covers more than the plain add/prop case – multiple endpoints, combined and power error, both-sides transforms and a single estimated boxCox/yeoJohnson lambda. A single non-Gaussian (ll()/generalized) endpoint instead differentiates the log-density directly, giving an exact inner Hessian and analytic outer gradient. Out of scope are linCmt(), fo, IOV, more than one estimated lambda, a theta mu-referenced by several random effects, and (for the non-Gaussian path) multiple endpoints, censoring or nAGQ > 1; those fall back to the finite-difference gradient with a message (linCmt() and out-of-scope log-likelihood models downgrade to fast=FALSE up front). When unspecified, the outer optimizer defaults to "lbfgsb3c" (vs "nlminb" for fast=FALSE); pairing fast=TRUE with a derivative-free outerOpt reverts to fast=FALSE. The *f methods (e.g. foceif) default this to TRUE.

is a double value representing the epsilon for the Hessian calculation. This is used for the R matrix calculation.

is a double value representing the epsilon for the Hessian calculation when doing focei generalized log-likelihood estimation. This is used for the R matrix calculation.

Hessian type for numeric-difference individual Hessians in generalized log-likelihood estimation: "central" (matches R's 'optimHess()', default) or "forward" (faster).

Hessian type for numeric-difference individual Hessians used for the covariance step/final likelihood: "central" (more accurate, used here) or "forward".

Treatment of the second derivative for censored (M2/M3/M4/BLQ) observations in the FOCEI family. "gauss" (the default) keeps the historic uncensored Gauss-Newton curvature, matching common PMx tools; "laplace" uses the exact censored second derivative of the objective (a proper Laplace inner Hessian and analytic covariance). Accepted by saemControl/nlmControl for a uniform interface but inert there – SAEM (stochastic EM) has no Laplace inner Hessian, and NLM uses a finite-difference Hessian that already reflects censoring exactly.

Event gradient type for dosing events; Can be "central" or "forward"

Controls how dosing/event-parameter ('alag', 'F', 'rate', 'dur') sensitivities are computed for THETA/ETA gradients: ‘"jump"' (default) uses rxode2’s analytic event sensitivities; '"fd"' uses the legacy finite-difference behavior.

Central difference tolerances (relative, absolute); step size h = abs(x)*derivEps[1] + derivEps[2].

An integer giving the number of BFGS updates retained in the "L-BFGS-B" method, It defaults to 7.

Projected-gradient convergence tolerance for "L-BFGS-B": iteration stops when max(| proj g_i |) <= lbfgsPgtol. Defaults to '0' (check suppressed).

Convergence factor for "L-BFGS-B": converges when the objective reduction is within lbfgsFactr * .Machine$double.eps. Default '1e10' (~4 sigdigs, 2e-6).

A boolean indicating if eigenvectors are calculated to include a condition number calculation.

Transformation used on the diagonal of chol(solve(omega)) (the FOCEi-estimated parameters): one of "sqrt" (default), "log", or "identity".

Transformation used on the diagonal of the IOV: one of "sd", "var", "logsd", or "logvar".

Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.

Optimize the rxode2 expression to speed up calculation. By default this is turned on.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

Confidence level for some tables. By default this is 0.95 or 95% confidence.

Logical (or 'NULL') emit ANSI bold/color escapes in the iteration print. 'NULL' (default) defers to [crayon::has_color()].

Tolerance for boundary issues.

This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE

Boolean to indicate if you should abort the FOCEi evaluation if it runs into troubles. (default TRUE)

Boolean indicate FOCEi should be used (TRUE) instead of FOCE (FALSE)

Controls how FOCE (interaction = FALSE) evaluates the residual variance R in the inner objective; ignored for FOCEi. Either "nonmem" (default) or "foce+":

• "nonmem" freezes R at the eta = 0 population prediction and holds it constant across the inner optimization, matching NONMEM's FOCE. Advantage: reproduces NONMEM FOCE objective and standard errors, and an ODE model agrees with its closed-form (linCmt) equivalent. Disadvantage: R ignores the individual (conditional) heteroscedasticity, so it can be slightly less accurate than "foce+" for proportional/combined error.

• "foce+" evaluates R at the current conditional eta (the live variance), keeping the truncated FOCE inner gradient. Advantage: uses the conditional variance and is a bit more accurate than NONMEM's FOCE in some cases. Disadvantage: does not match NONMEM FOCE. This was the FOCE behavior in nlmixr2est 6.0.1 and earlier. This does not use the gradient of eta like the full focei method, so it is not as accurate as focei.

tolerance for Generalized Cholesky Decomposition. Defaults to suggested (.Machine$double.eps)^(1/3)

Tolerance to accept a Generalized Cholesky Decomposition for a R or S matrix.

P-value for resetting an individual ETA to 0 during optimization, based on a z-test of chol(omega^-1) %*% eta or eta/sd(allEtas). '0' = never reset, '1' = always reset.

P-value for resetting mu-referenced THETAs based on ETA drift, checked at the start and near a local minimum (see resetThetaCheckPer). '0' = never reset; '1' is not allowed.

represents the p-value for reseting the population mu-referenced THETA parameters based on ETA drift during optimization, and resetting the optimization one final time.

Upper bound of the diagonal omega matrix, as diag(omega)*diagOmegaBoundUpper. '1' = no upper bound.

Lower bound of the diagonal omega matrix, as diag(omega)/diagOmegaBoundLower. '1' = no lower bound.

Boolean indicating if the generalized Cholesky should be used while optimizing.

Boolean indicating if the generalized Cholesky should be used while calculating the Covariance Matrix.

is a boolean indicating if this is a FO approximation routine.

If the R matrix is non-positive definite and cannot be corrected to be non-positive definite try estimating the Hessian on the unscaled parameter space.

optimization method for the outer problem

optimization method for the inner problem (not implemented yet.)

Initial trust region radius for the bobyqa outer optimizer (with 'rhoend', must satisfy '0 < rhoend < rhobeg'). Default '0.2' (20 'abs(upper-lower)/2'. (bobyqa)

Final trust region radius. If not defined, '10^(-sigdig)' is used. (bobyqa)

Number of points for bobyqa's quadratic approximation to the objective; must be in '[n+2, (n+1)(n+2)/2]'. Defaults to '2*n + 1'. (bobyqa)

Relative tolerance before nlminb stops (nlmimb).

X tolerance for nlmixr2 optimizer

Number of maximum evaluations of the objective function (nlmimb)

Maximum number of iterations allowed (nlmimb)

Absolute tolerance for nlmixr2 optimizer (BFGS)

tolerance for nlmixr2 (BFGS)

is a boolean representing if the individual Hessian is reset when ETAs are reset using the option resetEtaP.

Mu-referenced-FOCEI-family regression variant: "none" (default, ordinary FOCEI); "lin" (mfocei/mfoce/magq/mlaplace) profiles mu-referenced population thetas and covariate coefficients out of the outer optimizer via closed-form OLS regression of each subject's back-calculated value on the covariates (muModelTol/ muModelMaxCycles); "irls" (ifocei/ifoce/iagq/ilaplace) reweights that by inner-optimization curvature. Bounded mu parameters are regression-updated with a clamped step (muModelClampRetries); a user-fixed (fix()) mu theta is never updated.

When 'TRUE' (default), algebraic expressions that can be mu-referenced are internally rewritten as mu-referenced covariates and restored after optimization. Mirrors saemControl(muRefCovAlg=)/nlmeControl(muRefCovAlg=); for foceiControl() only takes effect when muModel != "none".

Convergence tolerance for the mu-referenced-FOCEI-family "re-optimize etas, then regress" cycle (muModel != "none"): repeats until the max mu-group theta change drops below this value or muModelMaxCycles is reached.

Maximum number of "re-optimize etas, regress" cycles per outer iteration (see muModel, muModelTol).

Maximum number of active-set re-solve passes per group per regression update when a bounded mu-referenced parameter must be clamped to its bound (see muModel); on hitting the cap the current clamped-feasible solution is used.

Trim state amounts/concentrations to this value.

The maximum number of steps for the optimization of the forward-difference step size. When not zero, use this instead of Gill differences.

The maximum number of steps for the optimization of the individual Hessian matrices in the generalized likelihood problem. When 0, un-optimized finite differences are used.

The maximum number of steps for the optimization of the individual Hessian matrices in the generalized likelihood problem for the covariance step. When 0, un-optimized finite differences are used.

The maximum number of steps for the optimization of the forward difference step size when using dosing events (lag time, modeled duration/rate and bioavailability)

Upper bound on the adaptive shi21 finite-difference step size for FOCEi gradients (both the inner eta and outer theta/covariate finite differences). The step-size search never probes a parameter by more than this on its estimation scale; a larger value lets the gradient of a flat, small-magnitude parameter (e.g. a covariate coefficient near 0) clear the ODE-solver noise floor, at the cost of risking a degenerate solve at the probe.

Lower bound on the adaptive shi21 finite-difference step size for FOCEi gradients. The floor is limited by the ODE solver tolerance (atol/rtol), not machine precision; below it the finite difference is dominated by solver noise.

Max steps to determine the optimal forward/central difference step size per parameter (Gill 1983). '0' = no optimal step size determined.

When looking for the optimal forward difference step size, this is This is the step size to increase the initial estimate by. So each iteration the new step size = (prior step size)*gillStep

The gillFtol is the gradient error tolerance that is acceptable before issuing a warning/error about the gradient estimates.

The relative tolerance used for Gill 1983 determination of optimal step size.

Max steps to determine the optimal forward/central difference step size per parameter (Gill 1983) during the covariance step. '0' = no optimal step size determined.

Same as gillK but for the generalized focei log-likelihood method (Gill 1986).

Same as above but during generalized focei log-likelihood

When looking for the optimal forward difference step size, this is This is the step size to increase the initial estimate by. So each iteration during the covariance step is equal to the new step size = (prior step size)*gillStepCov

The gillFtol is the gradient error tolerance that is acceptable before issuing a warning/error about the gradient estimates during the covariance step.

Same as above but applied during generalized log-likelihood estimation.

A parameter to normalize gradient step size by the parameter value during the calculation of the R matrix

A parameter to normalize gradient step size by the parameter value during the calculation of the R matrix if you are using generalized log-likelihood Hessian matrix.

A parameter to normalize gradient step size by the parameter value during the calculation of the S matrix

A parameter to normalize gradient step size by the parameter value during the calculation of the S matrix if you are using the generalized log-likelihood.

Use the Gill calculated optimal Forward difference step size for the instead of the central difference step size during the central difference gradient calculation.

Use the Gill calculated optimal Forward difference step size for the instead of the central difference step size during the central differences for optimization.

Small number used to compare covariance estimates (sandwich vs R/S matrix) before rejecting one as too small to be the final covariance estimate.

When 'TRUE', adjusts the likelihood by the 2*pi constant nlmixr2's objective function otherwise omits (to match NONMEM), more closely matching nlme/SAS likelihood approximations. The objective function itself always matches NONMEM regardless.

The parameter to adjust the gradient to if the |gradient| is very large.

Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.

The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced

A small number that represents the value where |grad| < gradCalcCentralSmall where forward differences switch to central differences.

A large number that represents the value where |grad| > gradCalcCentralLarge where forward differences switch to central differences.

When n1qn1 optimization of an ETA (starting at zero) misbehaves, reset the Hessian and nudge the ETA up by this value, then down if it still doesn't move. Defaults to 'qnorm(1-0.05/2)*1/sqrt(3)'. Falls back to etaNudge2, then to zero (stop optimizing) if unsuccessful.

This is the second eta nudge. By default it is qnorm(1-0.05/2)*sqrt(3/5), which is the n=3 quadrature point (excluding zero) times by the 0.95% normal region

If FOCEi doesn't fit with the current parameter estimates, randomly sample new parameter estimates and restart the problem. This is similar to 'PsN' resampling.

Integer seed (default '42') used to make a FOCEi fit reproducible and self-contained. The fit (including the 'mceta' Monte-Carlo initial-ETA draws, which pull from rxode2's threefry engine) runs inside [rxode2::rxWithSeed()], so it neither depends on the ambient RNG state nor advances/leaks it – repeated fits in the same session, and fits following other estimation methods, give identical results.

represents objective function % percentage below which resetThetaP is checked.

Initial (or final) ETA estimates; can also be a prior fit, whose final ETAs are then used as initial values. By default, uses the last fit's ETAs if supplied, else all ETAs start at zero ('NULL'). 'NA' disables reuse from a prior fit.

If the tolerances were reduced when calculating the initial Gill differences, the Gill difference is repeated up to a maximum number of times defined by this parameter.

The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.

When 'TRUE' (default), only subjects whose ODE solve produced NaN/Inf have their tolerances relaxed, and the relaxed tolerance persists across optimizer calls (sticky). When 'FALSE', all subjects have their tolerances relaxed on each retry and tolerances are reset afterward.

This is the time for a single objective function evaluation (in seconds) to start progress bars on gradient evaluations

Type of additive-plus-proportional error: '"combined1"', where standard deviations add:

$y = f + (a + b\times f^c) \times \varepsilon$

; or '"combined2"', where variances add:

$y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon$

. Here y = observed, f = predicted, a = additive sd, b = proportional/power sd, c = power exponent (1 in the proportional case).

The objective function adjustment when the ODE system cannot be solved. It is based on each individual bad solve.

Should the object have compressed items

'rxode2' ODE solving options during fitting, created with 'rxControl()'

Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3.

Fallback to the finite differences if the sensitivity equations do not solve.

Percentage of failed per-individual parameter gradients (replaced with the overall parameter gradient) out of the total ('ntheta*nsub') above which the S matrix is considered bad.

Factor multiplying the estimate when the lower bound is zero for a standard-deviation error parameter (add.sd, prop.sd, etc); e.g. estimate 0.15 with lower bound 0 assumes a lower bound of 0.00015. '0' disables this.

When 'TRUE' (default), reset a zero first gradient to 'sqrt(.Machine$double.eps)' instead of erroring; 'FALSE' errors; 'NA' ignores it only on the last reset attempt.

When 'TRUE' (default), reset a zero gradient encountered mid-run to 'sqrt(.Machine$double.eps)' instead of erroring.

When 'TRUE' (default), a zero-gradient reset switches to the gradient-free bobyqa method; 'NA' only does so for the first zero gradient.

Monte Carlo sampling for the best initial ETA estimate (based on 'omega'): '-2' (default) uses the Almquist (2015) Eq-48 extrapolation 'eta^0 = eta* + (d eta*/d theta)(theta_new - theta_old)' when the analytic gradient supplies 'd eta*/d theta' ('fast = TRUE'), accepting the extrapolated eta only when it is within the standardized-eta reset bound (else keeping the last eta, or resetting to 0 when that is also out of bound); '-1' jumps between the extrapolated eta and eta=0, keeping the better; both '-2' and '-1' fall back to keeping the last eta when no analytic 'd eta*/d theta' is available ('fast = FALSE'). '0' uses eta=0 for each inner optimization; for 'n>0', the last eta, eta=0, and n-1 etas sampled from omega are each evaluated and the best (by inner objective) is used.

Seeding of the n1qn1 inner-optimization Hessian: '"calc"' (default) warm-starts each inner problem with the eta Hessian calculated at the starting eta and the current theta; since theta moves between outer evaluations it is always recalculated, never reused from an earlier round. '"save"' uses the classic self-initialized Hessian.

Number of Gauss-Hermite adaptive quadrature points. '0' disables AGQ; '1' is equivalent to Laplace. Cost grows quickly with ETAs: once the EBE is found, expect 'nAGQ^neta' (even 'nAGQ') or '(nAGQ^neta)-1' (odd 'nAGQ') additional evaluations per subject.

The lower bound for adaptive quadrature log-likelihood. By default this is -Inf; in the original nlmixr's gnlmm it was -700.

The upper bound for adaptive quadrature log-likelihood. By default this is Inf; in the original nlmixr's gnlmm was 400.

Method used to compute the ODE parameter sensitivities: '"default"' (the default) defers to the global option 'getOption("nlmixr2est.adjoint")' (itself '"forward"' by default); '"forward"' uses the classic variational (forward) sensitivity ODEs; '"adjoint"' uses the in-engine discrete adjoint with the matching adjoint ('s') method.

Positive magnitude (default '0.001') used to nudge a population parameter ('theta') whose initial estimate is exactly '0' off zero before estimation. FOCEi scales a linear parameter by its native magnitude '|init|', which is '0' (no scale) for a zero initial estimate, so the parameter is moved to '+zeroTheta' when it is within the parameter's bounds, otherwise '-zeroTheta'; if neither is within the bounds an error is raised. Fixed parameters (including those fixed at '0') are left untouched.

When 'TRUE' (default), bounded parameters are transformed for unbounded optimization methods and back-transformed for final estimates. 'FALSE' optimizes on the original scale with bounds passed to the optimizer. 'NA' transforms for optimization but skips the final back-transform.

An 'rxode2'/'nlmixr2' UI model (a model function or its compiled UI).

The estimation data (a data frame with the usual nlmixr2 columns).

The individual likelihood type: '"focei"' (FOCE with interaction), '"focep"' (FOCE+, interaction off with the residual variance at the conditional eta) or '"foce"' (NONMEM-style FOCE, residual variance frozen at eta=0).

An [rxode2::rxControl()] object for the ODE solving options.

Additional solving/model options passed to '.foceiLikControl' (e.g. 'optExpression', 'addProp', 'eventSens').

The estimation-scale parameter vector (length 'handle$npars'), matching the FOCEi optimizer parameterization: population thetas followed by the estimated Omega elements. 'handle$initPar' from [foceiLikLoad()] is a ready starting value.

A 'nid' by 'neta' matrix of random effects (one row per subject, in the loaded system's subject order).

'"joint"' (default) returns the individual joint log density 'log p(y_i, eta_i)'; '"cond"' returns the conditional data log-likelihood 'log p(y_i | eta_i)' alone. See Details.

Number of threads for the parallel per-subject evaluation.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Interaction term for the model, in this case the default is 'FALSE'; it cannot be changed, use 'focei' instead

FOCE residual-variance mode; for 'focepControl()' this is always '"foce+"' and cannot be changed – use 'foceControl()' for '"nonmem"'

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiConrol()'

Logical indicating if the estimation method should calculate 'foce' posthoc predicted parameters.

Interaction term for the model; ignored by fo

Logical indicating if the estimation method is FO (first order), but this is controlled by the estimation method so this is ignored.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiConrol()'

Logical indicating if the estimation method should calculate 'foce' posthoc predicted parameters.

Interaction term for the model; ignored by fo

Logical indicating if the estimation method is FO (first order), but this is controlled by the estimation method so this is ignored.

The numeric vector to convert

The number of significant digits to show

The value to return if 'is.na(x)'

nlmixr control object

Estimation routine

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Number of Gauss-Hermite adaptive quadrature points. '0' disables AGQ; '1' is equivalent to Laplace. Cost grows quickly with ETAs: once the EBE is found, expect 'nAGQ^neta' (even 'nAGQ') or '(nAGQ^neta)-1' (odd 'nAGQ') additional evaluations per subject.

Parameters used in the default 'foceiControl()'

boolean, Interaction term for the model, in this case the default is 'TRUE'; For adaptive quadrature, with normal distribution the Hessian is calculated with the foce(i) approximation

The lower bound for adaptive quadrature log-likelihood. By default this is -Inf; in the original nlmixr's gnlmm it was -700.

The upper bound for adaptive quadrature log-likelihood. By default this is Inf; in the original nlmixr's gnlmm was 400.

Selects the regression variant; for 'iagqControl()' this is always '"irls"' and cannot be changed – use 'magqControl()' for the closed-form OLS variant.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Interaction term for the model, in this case the default is 'FALSE'; it cannot be changed, use 'ifocei' instead

Selects the regression variant; for 'ifoceControl()' this is always '"irls"' and cannot be changed – use 'mfoceControl()' for the closed-form OLS variant.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Selects the regression variant; for 'ifoceiControl()' this is always '"irls"' and cannot be changed – use 'mfoceiControl()' for the closed-form OLS variant.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Interaction term for the model, in this case the default is 'FALSE'; it cannot be changed, use 'ifocei' instead

Selects the regression variant; for 'ifocepControl()' this is always '"irls"' and cannot be changed – use 'mfocepControl()' for the closed-form OLS variant.

FOCE residual-variance mode; for 'ifocepControl()' this is always '"foce+"' and cannot be changed – use 'ifoceControl()' for '"nonmem"'

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Number of Gauss-Hermite adaptive quadrature points. '0' disables AGQ; '1' is equivalent to Laplace. Cost grows quickly with ETAs: once the EBE is found, expect 'nAGQ^neta' (even 'nAGQ') or '(nAGQ^neta)-1' (odd 'nAGQ') additional evaluations per subject.

Selects the regression variant; for 'ilaplaceControl()' this is always '"irls"' and cannot be changed – use 'mlaplaceControl()' for the closed-form OLS variant.

Parameters passed to [impmapControl()].

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Number of importance samples drawn per subject per iteration (NONMEM ISAMPLE).

Maximum number of importance-sampling EM iterations.

Number of MAP re-centering iterations per EM step; '> 0' re-centers the proposal at the MAP mode each iteration.

Initial proposal-variance inflation factor (NONMEM ISCALE); the proposal covariance is 'gamma' times the inverse of the inner information matrix at the mode.

Lower/upper bounds for the adapted 'gamma' (NONMEM ISCALE_MIN / ISCALE_MAX).

Minimum importance-sampling effective-sample fraction (NONMEM IACCEPT). The proposal scale 'gamma' is kept at its efficient starting value while the achieved fraction stays at or above 'iaccept', and is inflated (toward 'iscaleMax') only when it drops below this floor.

Convergence tolerance on the windowed objective-function change; 'NULL' derives it from 'sigdig'.

Length of the trailing iteration window used to average the objective-function change for convergence (NONMEM-style CTYPE).

Base seed for the per-subject thread-safe (threefry) RNG streams; results are reproducible and independent of the thread count.

Covariance method. '"imp"' (default) computes the Monte-Carlo importance-sampling observed-information covariance for the estimated thetas and Omega parameters (a finite-difference Hessian of the importance-sampling objective over fixed common-random-number samples), stashed as '$impCov' / '$impSe' and installed as the fit covariance; the theta standard errors match the Hessian-based FOCEI covariance, though the variance of a tightly-determined random effect (an Omega diagonal) can be over-estimated because the fixed samples barely span its prior variation. '"analytic"', '"r,s"', '"r"', '"s"' instead compute the FOCEI covariance post-fit at the converged estimates (see [foceiControl()]); '""' skips the covariance step.

When 'TRUE', draw quasi-random (Sobol low-discrepancy) importance samples instead of pseudo-random Gaussian samples (QRPEM, Leary & Dunlavey PAGE 2012); the E-step integrals converge at O(1/N) instead of O(1/sqrt(N)).

Only used with 'qr=TRUE'. When 'TRUE' each (iteration, subject) applies a random Cranley-Patterson shift to the Sobol points (seeded, thread-count independent); 'FALSE' reuses one fixed Sobol point set everywhere (fully deterministic E-step, no RNG in the draw).

Only used with 'qr=TRUE' and 'qrShift=TRUE'. When 'TRUE' the shift is redrawn each iteration so residual quasi-random error averages out over the EM; 'FALSE' draws one shift per subject at the fit start, making each EM iteration a deterministic map (smoothest objective trace).

When 'TRUE', accelerate the non-mu / residual-error M-step by SIR (sampling-importance-resampling): the theta-sensitivity Newton step uses 'sirSample' equal-weight resampled points per subject instead of all 'isample' weighted samples.

Number of SIR resampled points per subject; 'NULL' uses 'max(25, ceiling(isample/10))'. Must be at most 'isample'.

Mu-referencing variant for the MAP inner problem; for 'impmapControl()' this is always '"lin"' and cannot be changed.

Integer. Print one iteration row every 'every' parameter evaluations; '0' suppresses output. Defaults to '1L'.

Integer or 'NULL'. Parameter columns per row before wrapping. 'NULL' (default) uses 'floor((getOption("width") - 23) / 12)'.

Integer or 'NULL'. Re-emit the column header every 'headerEvery' parameter-print events; '0' prints it once at fit start. 'NULL' (default) uses '10L'.

Logical (or 'NULL') emit ANSI bold/color escapes in the iteration print. 'NULL' (default) defers to [crayon::has_color()].

Logical. When 'TRUE', print a single row per iteration, suppressing the unscaled ('U') / back-transformed ('X') rows. Defaults to 'FALSE'.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Number of Gauss-Hermite adaptive quadrature points. '0' disables AGQ; '1' is equivalent to Laplace. Cost grows quickly with ETAs: once the EBE is found, expect 'nAGQ^neta' (even 'nAGQ') or '(nAGQ^neta)-1' (odd 'nAGQ') additional evaluations per subject.

If positive, print tracing information; higher values give more detail (see source for "L-BFGS-B" trace levels).

Convergence tolerance factor for "L-BFGS-B"; converges when the objective reduction is within this factor of machine tolerance (default 1e7, i.e. ~1e-8).

Tolerance on the projected gradient for "L-BFGS-B"; 0 (default) suppresses the check.

Absolute x-value tolerance for "L-BFGS-B"; 0 (default) suppresses the check.

Relative x-value tolerance for "L-BFGS-B"; 0 (default) suppresses the check.

Number of BFGS updates retained in "L-BFGS-B" (default 5).

maximum number of iterations.

return the lbfgsb3c output instead of the nlmixr2 fit

The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.

Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.

The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced

When 'TRUE' (default), only subjects whose ODE solve produced NaN/Inf have their tolerances relaxed, and the relaxed tolerance persists across optimizer calls (sticky). When 'FALSE', all subjects have their tolerances relaxed on each retry and tolerances are reset afterward.

Logical (or 'NULL') emit ANSI bold/color escapes in the iteration print. 'NULL' (default) defers to [crayon::has_color()].

Integer (or 'NULL') parameter columns per row before wrapping. 'NULL' (default) uses 'floor((getOption("width") - 23) / 12)'.

Either a scalar print-frequency ('0' = suppress, '1' (default) = every evaluation, 'N' = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to 'iterPrintControl(every = print, ncol = printNcol, useColor = useColor)'.

Parameter normalization/scaling used to get scaled initial values for scaleType, of the form Vscaled = (Vunscaled-C1)/C2 (see Feature Scaling; rescale2 follows the OptdesX manual): "rescale2" scales all parameters to (-1, 1); "rescale" (min-max) scales to (0, 1); "mean" centers on the mean with range (0, 1); "std" standardizes by mean/sd; "len" scales to unit (Euclidean) length; "constant" performs no normalization (C1=0, C2=1).

The scaling scheme for nlmixr2: "nlmixr2" (default) scales as (current-init)*scaleC[i] + scaleTo, with scaleTo from normType and scales from scaleC; "norm" uses the simple scaling from normType; "mult" scales multiplicatively as current/init*scaleTo; "multAdd" scales linearly ((current-init)+scaleTo) for parameters in an exponential block (e.g. exp(theta)) and multiplicatively otherwise.

Maximum value of the scaleC to prevent overflow.

Minimum value of the scaleC to prevent underflow.

Scaling constant used with scaleType="nlmixr2"; when not specified, chosen by parameter type to keep gradient sizes similar on a log scale: '1' for exp()-transformed/power/boxCox/ yeoJohnson parameters, '0.5*abs(est)' for additive/proportional/ lognormal error parameters, 'abs(1/digamma(est+1))' for factorials, and 'log(abs(est))*abs(est)' for log-scale parameters. May be set explicitly per parameter if these defaults don't apply well.

Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed.

this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2".

'rxode2' ODE solving options during fitting, created with 'rxControl()'

Optimize the rxode2 expression to speed up calculation. By default this is turned on.

Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

Type of additive-plus-proportional error: '"combined1"', where standard deviations add:

$y = f + (a + b\times f^c) \times \varepsilon$

; or '"combined2"', where variances add:

$y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon$

. Here y = observed, f = predicted, a = additive sd, b = proportional/power sd, c = power exponent (1 in the proportional case).

Controls how dosing/event-parameter ('alag', 'F', 'rate', 'dur') sensitivities are computed for THETA/ETA gradients: ‘"jump"' (default) uses rxode2’s analytic event sensitivities; '"fd"' uses the legacy finite-difference behavior.

Method used to compute the ODE parameter sensitivities: '"default"' (the default) defers to the global option 'getOption("nlmixr2est.adjoint")' (itself '"forward"' by default); '"forward"' uses the classic variational (forward) sensitivity ODEs; '"adjoint"' uses the in-engine discrete adjoint with the matching adjoint ('s') method.

This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE

Should the object have compressed items

Method for calculating the covariance. "r" (the default) uses nlmixr2's nlmixr2Hess() Hessian; "" skips the covariance step.

is a boolean to indicate if the objective function should be adjusted to be closer to NONMEM's default objective function. By default this is TRUE

Confidence level for some tables. By default this is 0.95 or 95% confidence.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3.

Ignored parameters

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Number of Gauss-Hermite adaptive quadrature points. '0' disables AGQ; '1' is equivalent to Laplace. Cost grows quickly with ETAs: once the EBE is found, expect 'nAGQ^neta' (even 'nAGQ') or '(nAGQ^neta)-1' (odd 'nAGQ') additional evaluations per subject.

Parameters used in the default 'foceiControl()'

boolean, Interaction term for the model, in this case the default is 'TRUE'; For adaptive quadrature, with normal distribution the Hessian is calculated with the foce(i) approximation

The lower bound for adaptive quadrature log-likelihood. By default this is -Inf; in the original nlmixr's gnlmm it was -700.

The upper bound for adaptive quadrature log-likelihood. By default this is Inf; in the original nlmixr's gnlmm was 400.

Selects the regression variant; for 'magqControl()' this is always '"lin"' and cannot be changed – use 'iagqControl()' for the IRLS variant.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Interaction term for the model, in this case the default is 'FALSE'; it cannot be changed, use 'mfocei' instead

Selects the regression variant; for 'mfoceControl()' this is always '"lin"' and cannot be changed – use 'ifoceControl()' for the IRLS variant.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Selects the regression variant; for 'mfoceiControl()' this is always '"lin"' and cannot be changed – use 'ifoceiControl()' for the IRLS variant.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Interaction term for the model, in this case the default is 'FALSE'; it cannot be changed, use 'mfocei' instead

Selects the regression variant; for 'mfocepControl()' this is always '"lin"' and cannot be changed – use 'ifocepControl()' for the IRLS variant.

FOCE residual-variance mode; for 'mfocepControl()' this is always '"foce+"' and cannot be changed – use 'mfoceControl()' for '"nonmem"'

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Parameters used in the default 'foceiControl()'

Number of Gauss-Hermite adaptive quadrature points. '0' disables AGQ; '1' is equivalent to Laplace. Cost grows quickly with ETAs: once the EBE is found, expect 'nAGQ^neta' (even 'nAGQ') or '(nAGQ^neta)-1' (odd 'nAGQ') additional evaluations per subject.

Selects the regression variant; for 'mlaplaceControl()' this is always '"lin"' and cannot be changed – use 'ilaplaceControl()' for the IRLS variant.

Precision of estimate for n1qn1 optimization.

Number of iterations

Number of function evaluations

Verbosity of messages.

Boolean to control if the function value and parameter estimates are echoed every time a function is called.

return the n1qn1 output instead of the nlmixr2 fit

The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.

Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.

The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced

When 'TRUE' (default), only subjects whose ODE solve produced NaN/Inf have their tolerances relaxed, and the relaxed tolerance persists across optimizer calls (sticky). When 'FALSE', all subjects have their tolerances relaxed on each retry and tolerances are reset afterward.

Logical (or 'NULL') emit ANSI bold/color escapes in the iteration print. 'NULL' (default) defers to [crayon::has_color()].

Integer (or 'NULL') parameter columns per row before wrapping. 'NULL' (default) uses 'floor((getOption("width") - 23) / 12)'.

Either a scalar print-frequency ('0' = suppress, '1' (default) = every evaluation, 'N' = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to 'iterPrintControl(every = print, ncol = printNcol, useColor = useColor)'.

Parameter normalization/scaling used to get scaled initial values for scaleType, of the form Vscaled = (Vunscaled-C1)/C2 (see Feature Scaling; rescale2 follows the OptdesX manual): "rescale2" scales all parameters to (-1, 1); "rescale" (min-max) scales to (0, 1); "mean" centers on the mean with range (0, 1); "std" standardizes by mean/sd; "len" scales to unit (Euclidean) length; "constant" performs no normalization (C1=0, C2=1).

The scaling scheme for nlmixr2: "nlmixr2" (default) scales as (current-init)*scaleC[i] + scaleTo, with scaleTo from normType and scales from scaleC; "norm" uses the simple scaling from normType; "mult" scales multiplicatively as current/init*scaleTo; "multAdd" scales linearly ((current-init)+scaleTo) for parameters in an exponential block (e.g. exp(theta)) and multiplicatively otherwise.

Maximum value of the scaleC to prevent overflow.

Minimum value of the scaleC to prevent underflow.

Scaling constant used with scaleType="nlmixr2"; when not specified, chosen by parameter type to keep gradient sizes similar on a log scale: '1' for exp()-transformed/power/boxCox/ yeoJohnson parameters, '0.5*abs(est)' for additive/proportional/ lognormal error parameters, 'abs(1/digamma(est+1))' for factorials, and 'log(abs(est))*abs(est)' for log-scale parameters. May be set explicitly per parameter if these defaults don't apply well.

Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed.

this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2".

'rxode2' ODE solving options during fitting, created with 'rxControl()'

Optimize the rxode2 expression to speed up calculation. By default this is turned on.

Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

Type of additive-plus-proportional error: '"combined1"', where standard deviations add:

$y = f + (a + b\times f^c) \times \varepsilon$

; or '"combined2"', where variances add:

$y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon$

. Here y = observed, f = predicted, a = additive sd, b = proportional/power sd, c = power exponent (1 in the proportional case).

Controls how dosing/event-parameter ('alag', 'F', 'rate', 'dur') sensitivities are computed for THETA/ETA gradients: ‘"jump"' (default) uses rxode2’s analytic event sensitivities; '"fd"' uses the legacy finite-difference behavior.

Method used to compute the ODE parameter sensitivities: '"default"' (the default) defers to the global option 'getOption("nlmixr2est.adjoint")' (itself '"forward"' by default); '"forward"' uses the classic variational (forward) sensitivity ODEs; '"adjoint"' uses the in-engine discrete adjoint with the matching adjoint ('s') method.

This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE

Should the object have compressed items

Method for calculating the covariance. "r" (the default) uses nlmixr2's nlmixr2Hess() Hessian; "n1qn1" uses the optimizer's own Hessian; "" skips the covariance step.

is a boolean to indicate if the objective function should be adjusted to be closer to NONMEM's default objective function. By default this is TRUE

Confidence level for some tables. By default this is 0.95 or 95% confidence.

Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 4 this is atol = 1e-7, rtol = 1e-4.

Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3.

When 'TRUE' (default), bounded parameters are transformed for unbounded optimization methods and back-transformed for final estimates. 'FALSE' optimizes on the original scale with bounds passed to the optimizer. 'NA' transforms for optimization but skips the final back-transform.

Ignored parameters

Number of points for bobyqa's quadratic approximation to the objective; must be in '[n+2, (n+1)(n+2)/2]'. Defaults to '2*n + 1'. (bobyqa)

Initial trust region radius for the bobyqa outer optimizer (with 'rhoend', must satisfy '0 < rhoend < rhobeg'). Default '0.2' (20 'abs(upper-lower)/2'. (bobyqa)

Final trust region radius. If not defined, '10^(-sigdig)' is used. (bobyqa)

Controls amount of printing ('0'=none, '1'=start/end only, '2'=each new rho, '3'=every function evaluation, '>3'=every 'iprint' evaluations). Default '0'.

The maximum allowed number of function evaluations. If this is exceeded, the method will terminate.

return the newuoa output instead of the nlmixr2 fit

The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.

Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.

The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced

When 'TRUE' (default), only subjects whose ODE solve produced NaN/Inf have their tolerances relaxed, and the relaxed tolerance persists across optimizer calls (sticky). When 'FALSE', all subjects have their tolerances relaxed on each retry and tolerances are reset afterward.

Logical (or 'NULL') emit ANSI bold/color escapes in the iteration print. 'NULL' (default) defers to [crayon::has_color()].

Integer (or 'NULL') parameter columns per row before wrapping. 'NULL' (default) uses 'floor((getOption("width") - 23) / 12)'.

Either a scalar print-frequency ('0' = suppress, '1' (default) = every evaluation, 'N' = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to 'iterPrintControl(every = print, ncol = printNcol, useColor = useColor)'.

Parameter normalization/scaling used to get scaled initial values for scaleType, of the form Vscaled = (Vunscaled-C1)/C2 (see Feature Scaling; rescale2 follows the OptdesX manual): "rescale2" scales all parameters to (-1, 1); "rescale" (min-max) scales to (0, 1); "mean" centers on the mean with range (0, 1); "std" standardizes by mean/sd; "len" scales to unit (Euclidean) length; "constant" performs no normalization (C1=0, C2=1).

The scaling scheme for nlmixr2: "nlmixr2" (default) scales as (current-init)*scaleC[i] + scaleTo, with scaleTo from normType and scales from scaleC; "norm" uses the simple scaling from normType; "mult" scales multiplicatively as current/init*scaleTo; "multAdd" scales linearly ((current-init)+scaleTo) for parameters in an exponential block (e.g. exp(theta)) and multiplicatively otherwise.

Maximum value of the scaleC to prevent overflow.

Minimum value of the scaleC to prevent underflow.

Scaling constant used with scaleType="nlmixr2"; when not specified, chosen by parameter type to keep gradient sizes similar on a log scale: '1' for exp()-transformed/power/boxCox/ yeoJohnson parameters, '0.5*abs(est)' for additive/proportional/ lognormal error parameters, 'abs(1/digamma(est+1))' for factorials, and 'log(abs(est))*abs(est)' for log-scale parameters. May be set explicitly per parameter if these defaults don't apply well.

Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed.

'rxode2' ODE solving options during fitting, created with 'rxControl()'

Optimize the rxode2 expression to speed up calculation. By default this is turned on.

Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.