Error(s) in re-building vignettes:
--- re-building ‘running_nlmixr.Rmd’ using rmarkdown
Loading required package: nlmixr2data
ℹ parameter labels from comments will be replaced by 'label()'
ℹ parameter labels from comments will be replaced by 'label()'
→ loading into symengine environment...
→ pruning branches (`if`/`else`) of full model...
✔ done
→ calculate jacobian
→ calculate ∂(f)/∂(η)
→ calculate ∂(R²)/∂(η)
→ finding duplicate expressions in inner model...
→ optimizing duplicate expressions in inner model...
→ finding duplicate expressions in EBE model...
→ optimizing duplicate expressions in EBE model...
→ compiling inner model...
✔ done
→ finding duplicate expressions in FD model...
→ compiling EBE model...
✔ done
→ compiling events FD model...
✔ done
rxode2 2.0.8 using 2 threads (see ?getRxThreads)
  no cache: create with `rxCreateCache()`

 *** caught segfault ***
address 0x18, cause 'memory not mapped'

Traceback:
 1: objective(.par, ...)
 2: stats::nlminb(start = par, objective = fn, gradient = gr, hessian = NULL,     control = .ctl, lower = lower, upper = upper)
 3: (function (par, fn, gr, lower = -Inf, upper = Inf, control = list(),     ...) {    .ctl <- control    .ctl <- .ctl[names(.ctl) %in% c("eval.max", "iter.max", "trace",         "abs.tol", "rel.tol", "x.tol", "xf.tol", "step.min",         "step.max", "sing.tol", "scale.inti", "diff.g")]    .ctl$trace <- 0    .ret <- stats::nlminb(start = par, objective = fn, gradient = gr,         hessian = NULL, control = .ctl, lower = lower, upper = upper)    .ret$x <- .ret$par    return(.ret)})(par = c(-1, -0.637832151326478, 1, -0.833333333333333, -0.542520422355001, -0.39919989679531, -0.114480393307385), fn = function (theta) {    .Call(`_nlmixr2est_foceiOuterF`, theta)}, gr = function (theta) {    .Call(`_nlmixr2est_foceiOuterG`, theta)}, lower = c(-Inf, -Inf, -Inf, -2.83333163034348, -0.873779194190821, -0.730458752094879, -0.44573935454913), upper = c(Inf, 2.97408411280245, Inf, Inf, 1.61975587753649, 1.76307631963243, 2.04779571717818), control = list(maxOuterIterations = 5000L, maxInnerIterations = 1000L,     n1qn1nsim = 10001L, print = 0L, lbfgsLmm = 7L, lbfgsPgtol = 0,     lbfgsFactr = 450359962737.05, scaleTo = 1, epsilon = 1e-04,     derivEps = c(2.98023223876953e-07, 2.98023223876953e-07),     derivMethod = 3L, covDerivMethod = 1L, covMethod = 1L, centralDerivEps = c(2.98023223876953e-07,     2.98023223876953e-07), eigen = 1L, addPosthoc = 1L, diagXform = "sqrt",     sumProd = FALSE, optExpression = TRUE, outerOpt = -1L, ci = 0.95,     sigdig = 3, sigdigTable = 3, scaleObjective = 0, useColor = FALSE,     boundTol = 0.05, calcTables = TRUE, printNcol = 4L, noAbort = 1L,     interaction = 1L, cholSEtol = 6.05545445239334e-06, hessEps = 6.05545445239334e-06,     cholAccept = 0.001, resetEtaSize = 1.43953147093846, resetThetaSize = 1.95996398454005,     resetThetaFinalSize = 1.43953147093846, diagOmegaBoundUpper = 5,     diagOmegaBoundLower = 100, cholSEOpt = 0L, cholSECov = 0L,     fo = 0L, covTryHarder = 0L, outerOptFun = function (par,         fn, gr, lower = -Inf, upper = Inf, control = list(),         ...)     {        .ctl <- control        .ctl <- .ctl[names(.ctl) %in% c("eval.max", "iter.max",             "trace", "abs.tol", "rel.tol", "x.tol", "xf.tol",             "step.min", "step.max", "sing.tol", "scale.inti",             "diff.g")]        .ctl$trace <- 0        .ret <- stats::nlminb(start = par, objective = fn, gradient = gr,             hessian = NULL, control = .ctl, lower = lower, upper = upper)        .ret$x <- .ret$par        return(.ret)    }, rhobeg = 0.2, rhoend = 1e-04, npt = NULL, rel.tol = 1e-04,     x.tol = 1e-04, eval.max = 4000, iter.max = 2000, innerOpt = 1L,     abstol = 1e-04, reltol = 1e-04, derivSwitchTol = 2e-04, resetHessianAndEta = 0L,     stateTrim = Inf, gillK = 10L, gillKcov = 10L, gillRtol = 1.49011611938477e-08,     gillStep = 4, gillStepCov = 2, scaleType = 2L, normType = 1L,     scaleC = NULL, scaleCmin = 1e-05, scaleCmax = 1e+05, scaleC0 = 1e+05,     outerOptTxt = "nlminb", rmatNorm = 1L, smatNorm = 1L, covGillF = 1L,     optGillF = 1L, gillFtol = 0, gillFtolCov = 0, covSmall = 1e-05,     adjLik = TRUE, gradTrim = Inf, gradCalcCentralSmall = 1e-04,     gradCalcCentralLarge = 10000, etaNudge = 1.13158573407617,     etaNudge2 = 1.51818157425799, maxOdeRecalc = 5L, odeRecalcFactor = 3.16227766016838,     nRetries = 3, seed = 42, resetThetaCheckPer = 0.1, etaMat = NULL,     repeatGillMax = 3L, stickyRecalcN = 5L, eventFD = 1.49011611938477e-08,     eventType = 1L, gradProgressOfvTime = 10, addProp = "combined2",     badSolveObjfAdj = 100, compress = TRUE, rxControl = list(        scale = NULL, method = c(liblsoda = 2L), atol = 5e-06,         rtol = 5e-06, maxsteps = 500000L, hmin = 0, hmax = NA_real_,         hini = 0, maxordn = 12L, maxords = 5L, covsInterpolation = c(locf = 1L),         addCov = TRUE, returnType = c(rxSolve = 0L), sigma = NULL,         sigmaDf = NULL, nCoresRV = 1L, sigmaIsChol = FALSE, sigmaSeparation = "auto",         sigmaXform = c(identity = 4L), nDisplayProgress = 10000L,         amountUnits = NA_character_, timeUnits = "hours", addDosing = FALSE,         stateTrim = Inf, updateObject = FALSE, omega = NULL,         omegaDf = NULL, omegaIsChol = FALSE, omegaSeparation = "auto",         omegaXform = c(variance = 6L), nSub = 1L, thetaMat = NULL,         thetaDf = NULL, thetaIsChol = FALSE, nStud = 1L, dfSub = 0,         dfObs = 0, seed = NULL, nsim = NULL, minSS = 10L, maxSS = 1000L,         strictSS = 1L, infSSstep = 12, istateReset = TRUE, subsetNonmem = TRUE,         hmaxSd = 0, maxAtolRtolFactor = 0.1, from = NULL, to = NULL,         by = NULL, length.out = NULL, iCov = NULL, keep = NULL,         keepF = character(0), drop = NULL, warnDrop = TRUE, omegaLower = -Inf,         omegaUpper = Inf, sigmaLower = -Inf, sigmaUpper = Inf,         thetaLower = -Inf, thetaUpper = Inf, indLinPhiM = 0L,         indLinPhiTol = 1e-07, indLinMatExpType = c(expokit = 2L),         indLinMatExpOrder = 6L, idFactor = TRUE, mxhnil = 0L,         hmxi = 0, warnIdSort = TRUE, ssAtol = 5e-04, ssRtol = 5e-04,         safeZero = 1L, sumType = c(pairwise = 1L), prodType = c(`long double` = 1L),         sensType = c(advan = 4L), linDiff = c(tlag = 1.5e-05,         f = 1.5e-05, rate = 1.5e-05, dur = 1.5e-05, tlag2 = 1.5e-05,         f2 = 1.5e-05, rate2 = 1.5e-05, dur2 = 1.5e-05), linDiffCentral = c(tlag = TRUE,         f = TRUE, rate = TRUE, dur = TRUE, tlag2 = TRUE, f2 = TRUE,         rate2 = TRUE, dur2 = TRUE), resample = NULL, resampleID = TRUE,         maxwhile = 100000L, cores = 1L, atolSens = 1.58113883008419e-05,         rtolSens = 1.58113883008419e-05, ssAtolSens = 0.00210848251714291,         ssRtolSens = 0.00210848251714291, simVariability = NA,         nLlikAlloc = NULL, useStdPow = 0L), genRxControl = TRUE,     skipCov = NULL, fallbackFD = FALSE, foceiMuRef = c(0L, 0L,     0L), predNeq = 0L, nfixed = 0L, nomega = 3L, neta = 3L, ntheta = 4L,     nF = 0, printTop = TRUE))
 4: evalq((function (par, fn, gr, lower = -Inf, upper = Inf, control = list(),     ...) {    .ctl <- control    .ctl <- .ctl[names(.ctl) %in% c("eval.max", "iter.max", "trace",         "abs.tol", "rel.tol", "x.tol", "xf.tol", "step.min",         "step.max", "sing.tol", "scale.inti", "diff.g")]    .ctl$trace <- 0    .ret <- stats::nlminb(start = par, objective = fn, gradient = gr,         hessian = NULL, control = .ctl, lower = lower, upper = upper)    .ret$x <- .ret$par    return(.ret)})(par = c(-1, -0.637832151326478, 1, -0.833333333333333, -0.542520422355001, -0.39919989679531, -0.114480393307385), fn = function (theta) {    .Call(`_nlmixr2est_foceiOuterF`, theta)}, gr = function (theta) {    .Call(`_nlmixr2est_foceiOuterG`, theta)}, lower = c(-Inf, -Inf, -Inf, -2.83333163034348, -0.873779194190821, -0.730458752094879, -0.44573935454913), upper = c(Inf, 2.97408411280245, Inf, Inf, 1.61975587753649, 1.76307631963243, 2.04779571717818), control = list(maxOuterIterations = 5000L, maxInnerIterations = 1000L,     n1qn1nsim = 10001L, print = 0L, lbfgsLmm = 7L, lbfgsPgtol = 0,     lbfgsFactr = 450359962737.05, scaleTo = 1, epsilon = 1e-04,     derivEps = c(2.98023223876953e-07, 2.98023223876953e-07),     derivMethod = 3L, covDerivMethod = 1L, covMethod = 1L, centralDerivEps = c(2.98023223876953e-07,     2.98023223876953e-07), eigen = 1L, addPosthoc = 1L, diagXform = "sqrt",     sumProd = FALSE, optExpression = TRUE, outerOpt = -1L, ci = 0.95,     sigdig = 3, sigdigTable = 3, scaleObjective = 0, useColor = FALSE,     boundTol = 0.05, calcTables = TRUE, printNcol = 4L, noAbort = 1L,     interaction = 1L, cholSEtol = 6.05545445239334e-06, hessEps = 6.05545445239334e-06,     cholAccept = 0.001, resetEtaSize = 1.43953147093846, resetThetaSize = 1.95996398454005,     resetThetaFinalSize = 1.43953147093846, diagOmegaBoundUpper = 5,     diagOmegaBoundLower = 100, cholSEOpt = 0L, cholSECov = 0L,     fo = 0L, covTryHarder = 0L, outerOptFun = function (par,         fn, gr, lower = -Inf, upper = Inf, control = list(),         ...)     {        .ctl <- control        .ctl <- .ctl[names(.ctl) %in% c("eval.max", "iter.max",             "trace", "abs.tol", "rel.tol", "x.tol", "xf.tol",             "step.min", "step.max", "sing.tol", "scale.inti",             "diff.g")]        .ctl$trace <- 0        .ret <- stats::nlminb(start = par, objective = fn, gradient = gr,             hessian = NULL, control = .ctl, lower = lower, upper = upper)        .ret$x <- .ret$par        return(.ret)    }, rhobeg = 0.2, rhoend = 1e-04, npt = NULL, rel.tol = 1e-04,     x.tol = 1e-04, eval.max = 4000, iter.max = 2000, innerOpt = 1L,     abstol = 1e-04, reltol = 1e-04, derivSwitchTol = 2e-04, resetHessianAndEta = 0L,     stateTrim = Inf, gillK = 10L, gillKcov = 10L, gillRtol = 1.49011611938477e-08,     gillStep = 4, gillStepCov = 2, scaleType = 2L, normType = 1L,     scaleC = NULL, scaleCmin = 1e-05, scaleCmax = 1e+05, scaleC0 = 1e+05,     outerOptTxt = "nlminb", rmatNorm = 1L, smatNorm = 1L, covGillF = 1L,     optGillF = 1L, gillFtol = 0, gillFtolCov = 0, covSmall = 1e-05,     adjLik = TRUE, gradTrim = Inf, gradCalcCentralSmall = 1e-04,     gradCalcCentralLarge = 10000, etaNudge = 1.13158573407617,     etaNudge2 = 1.51818157425799, maxOdeRecalc = 5L, odeRecalcFactor = 3.16227766016838,     nRetries = 3, seed = 42, resetThetaCheckPer = 0.1, etaMat = NULL,     repeatGillMax = 3L, stickyRecalcN = 5L, eventFD = 1.49011611938477e-08,     eventType = 1L, gradProgressOfvTime = 10, addProp = "combined2",     badSolveObjfAdj = 100, compress = TRUE, rxControl = list(        scale = NULL, method = c(liblsoda = 2L), atol = 5e-06,         rtol = 5e-06, maxsteps = 500000L, hmin = 0, hmax = NA_real_,         hini = 0, maxordn = 12L, maxords = 5L, covsInterpolation = c(locf = 1L),         addCov = TRUE, returnType = c(rxSolve = 0L), sigma = NULL,         sigmaDf = NULL, nCoresRV = 1L, sigmaIsChol = FALSE, sigmaSeparation = "auto",         sigmaXform = c(identity = 4L), nDisplayProgress = 10000L,         amountUnits = NA_character_, timeUnits = "hours", addDosing = FALSE,         stateTrim = Inf, updateObject = FALSE, omega = NULL,         omegaDf = NULL, omegaIsChol = FALSE, omegaSeparation = "auto",         omegaXform = c(variance = 6L), nSub = 1L, thetaMat = NULL,         thetaDf = NULL, thetaIsChol = FALSE, nStud = 1L, dfSub = 0,         dfObs = 0, seed = NULL, nsim = NULL, minSS = 10L, maxSS = 1000L,         strictSS = 1L, infSSstep = 12, istateReset = TRUE, subsetNonmem = TRUE,         hmaxSd = 0, maxAtolRtolFactor = 0.1, from = NULL, to = NULL,         by = NULL, length.out = NULL, iCov = NULL, keep = NULL,         keepF = character(0), drop = NULL, warnDrop = TRUE, omegaLower = -Inf,         omegaUpper = Inf, sigmaLower = -Inf, sigmaUpper = Inf,         thetaLower = -Inf, thetaUpper = Inf, indLinPhiM = 0L,         indLinPhiTol = 1e-07, indLinMatExpType = c(expokit = 2L),         indLinMatExpOrder = 6L, idFactor = TRUE, mxhnil = 0L,         hmxi = 0, warnIdSort = TRUE, ssAtol = 5e-04, ssRtol = 5e-04,         safeZero = 1L, sumType = c(pairwise = 1L), prodType = c(`long double` = 1L),         sensType = c(advan = 4L), linDiff = c(tlag = 1.5e-05,         f = 1.5e-05, rate = 1.5e-05, dur = 1.5e-05, tlag2 = 1.5e-05,         f2 = 1.5e-05, rate2 = 1.5e-05, dur2 = 1.5e-05), linDiffCentral = c(tlag = TRUE,         f = TRUE, rate = TRUE, dur = TRUE, tlag2 = TRUE, f2 = TRUE,         rate2 = TRUE, dur2 = TRUE), resample = NULL, resampleID = TRUE,         maxwhile = 100000L, cores = 1L, atolSens = 1.58113883008419e-05,         rtolSens = 1.58113883008419e-05, ssAtolSens = 0.00210848251714291,         ssRtolSens = 0.00210848251714291, simVariability = NA,         nLlikAlloc = NULL, useStdPow = 0L), genRxControl = TRUE,     skipCov = NULL, fallbackFD = FALSE, foceiMuRef = c(0L, 0L,     0L), predNeq = 0L, nfixed = 0L, nomega = 3L, neta = 3L, ntheta = 4L,     nF = 0, printTop = TRUE)), <environment>)
 5: evalq((function (par, fn, gr, lower = -Inf, upper = Inf, control = list(),     ...) {    .ctl <- control    .ctl <- .ctl[names(.ctl) %in% c("eval.max", "iter.max", "trace",         "abs.tol", "rel.tol", "x.tol", "xf.tol", "step.min",         "step.max", "sing.tol", "scale.inti", "diff.g")]    .ctl$trace <- 0    .ret <- stats::nlminb(start = par, objective = fn, gradient = gr,         hessian = NULL, control = .ctl, lower = lower, upper = upper)    .ret$x <- .ret$par    return(.ret)})(par = c(-1, -0.637832151326478, 1, -0.833333333333333, -0.542520422355001, -0.39919989679531, -0.114480393307385), fn = function (theta) {    .Call(`_nlmixr2est_foceiOuterF`, theta)}, gr = function (theta) {    .Call(`_nlmixr2est_foceiOuterG`, theta)}, lower = c(-Inf, -Inf, -Inf, -2.83333163034348, -0.873779194190821, -0.730458752094879, -0.44573935454913), upper = c(Inf, 2.97408411280245, Inf, Inf, 1.61975587753649, 1.76307631963243, 2.04779571717818), control = list(maxOuterIterations = 5000L, maxInnerIterations = 1000L,     n1qn1nsim = 10001L, print = 0L, lbfgsLmm = 7L, lbfgsPgtol = 0,     lbfgsFactr = 450359962737.05, scaleTo = 1, epsilon = 1e-04,     derivEps = c(2.98023223876953e-07, 2.98023223876953e-07),     derivMethod = 3L, covDerivMethod = 1L, covMethod = 1L, centralDerivEps = c(2.98023223876953e-07,     2.98023223876953e-07), eigen = 1L, addPosthoc = 1L, diagXform = "sqrt",     sumProd = FALSE, optExpression = TRUE, outerOpt = -1L, ci = 0.95,     sigdig = 3, sigdigTable = 3, scaleObjective = 0, useColor = FALSE,     boundTol = 0.05, calcTables = TRUE, printNcol = 4L, noAbort = 1L,     interaction = 1L, cholSEtol = 6.05545445239334e-06, hessEps = 6.05545445239334e-06,     cholAccept = 0.001, resetEtaSize = 1.43953147093846, resetThetaSize = 1.95996398454005,     resetThetaFinalSize = 1.43953147093846, diagOmegaBoundUpper = 5,     diagOmegaBoundLower = 100, cholSEOpt = 0L, cholSECov = 0L,     fo = 0L, covTryHarder = 0L, outerOptFun = function (par,         fn, gr, lower = -Inf, upper = Inf, control = list(),         ...)     {        .ctl <- control        .ctl <- .ctl[names(.ctl) %in% c("eval.max", "iter.max",             "trace", "abs.tol", "rel.tol", "x.tol", "xf.tol",             "step.min", "step.max", "sing.tol", "scale.inti",             "diff.g")]        .ctl$trace <- 0        .ret <- stats::nlminb(start = par, objective = fn, gradient = gr,             hessian = NULL, control = .ctl, lower = lower, upper = upper)        .ret$x <- .ret$par        return(.ret)    }, rhobeg = 0.2, rhoend = 1e-04, npt = NULL, rel.tol = 1e-04,     x.tol = 1e-04, eval.max = 4000, iter.max = 2000, innerOpt = 1L,     abstol = 1e-04, reltol = 1e-04, derivSwitchTol = 2e-04, resetHessianAndEta = 0L,     stateTrim = Inf, gillK = 10L, gillKcov = 10L, gillRtol = 1.49011611938477e-08,     gillStep = 4, gillStepCov = 2, scaleType = 2L, normType = 1L,     scaleC = NULL, scaleCmin = 1e-05, scaleCmax = 1e+05, scaleC0 = 1e+05,     outerOptTxt = "nlminb", rmatNorm = 1L, smatNorm = 1L, covGillF = 1L,     optGillF = 1L, gillFtol = 0, gillFtolCov = 0, covSmall = 1e-05,     adjLik = TRUE, gradTrim = Inf, gradCalcCentralSmall = 1e-04,     gradCalcCentralLarge = 10000, etaNudge = 1.13158573407617,     etaNudge2 = 1.51818157425799, maxOdeRecalc = 5L, odeRecalcFactor = 3.16227766016838,     nRetries = 3, seed = 42, resetThetaCheckPer = 0.1, etaMat = NULL,     repeatGillMax = 3L, stickyRecalcN = 5L, eventFD = 1.49011611938477e-08,     eventType = 1L, gradProgressOfvTime = 10, addProp = "combined2",     badSolveObjfAdj = 100, compress = TRUE, rxControl = list(        scale = NULL, method = c(liblsoda = 2L), atol = 5e-06,         rtol = 5e-06, maxsteps = 500000L, hmin = 0, hmax = NA_real_,         hini = 0, maxordn = 12L, maxords = 5L, covsInterpolation = c(locf = 1L),         addCov = TRUE, returnType = c(rxSolve = 0L), sigma = NULL,         sigmaDf = NULL, nCoresRV = 1L, sigmaIsChol = FALSE, sigmaSeparation = "auto",         sigmaXform = c(identity = 4L), nDisplayProgress = 10000L,         amountUnits = NA_character_, timeUnits = "hours", addDosing = FALSE,         stateTrim = Inf, updateObject = FALSE, omega = NULL,         omegaDf = NULL, omegaIsChol = FALSE, omegaSeparation = "auto",         omegaXform = c(variance = 6L), nSub = 1L, thetaMat = NULL,         thetaDf = NULL, thetaIsChol = FALSE, nStud = 1L, dfSub = 0,         dfObs = 0, seed = NULL, nsim = NULL, minSS = 10L, maxSS = 1000L,         strictSS = 1L, infSSstep = 12, istateReset = TRUE, subsetNonmem = TRUE,         hmaxSd = 0, maxAtolRtolFactor = 0.1, from = NULL, to = NULL,         by = NULL, length.out = NULL, iCov = NULL, keep = NULL,         keepF = character(0), drop = NULL, warnDrop = TRUE, omegaLower = -Inf,         omegaUpper = Inf, sigmaLower = -Inf, sigmaUpper = Inf,         thetaLower = -Inf, thetaUpper = Inf, indLinPhiM = 0L,         indLinPhiTol = 1e-07, indLinMatExpType = c(expokit = 2L),         indLinMatExpOrder = 6L, idFactor = TRUE, mxhnil = 0L,         hmxi = 0, warnIdSort = TRUE, ssAtol = 5e-04, ssRtol = 5e-04,         safeZero = 1L, sumType = c(pairwise = 1L), prodType = c(`long double` = 1L),         sensType = c(advan = 4L), linDiff = c(tlag = 1.5e-05,         f = 1.5e-05, rate = 1.5e-05, dur = 1.5e-05, tlag2 = 1.5e-05,         f2 = 1.5e-05, rate2 = 1.5e-05, dur2 = 1.5e-05), linDiffCentral = c(tlag = TRUE,         f = TRUE, rate = TRUE, dur = TRUE, tlag2 = TRUE, f2 = TRUE,         rate2 = TRUE, dur2 = TRUE), resample = NULL, resampleID = TRUE,         maxwhile = 100000L, cores = 1L, atolSens = 1.58113883008419e-05,         rtolSens = 1.58113883008419e-05, ssAtolSens = 0.00210848251714291,         ssRtolSens = 0.00210848251714291, simVariability = NA,         nLlikAlloc = NULL, useStdPow = 0L), genRxControl = TRUE,     skipCov = NULL, fallbackFD = FALSE, foceiMuRef = c(0L, 0L,     0L), predNeq = 0L, nfixed = 0L, nomega = 3L, neta = 3L, ntheta = 4L,     nF = 0, printTop = TRUE)), <environment>)
 6: doTryCatch(return(expr), name, parentenv, handler)
 7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
 8: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
 9: doTryCatch(return(expr), name, parentenv, handler)
10: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),     names[nh], parentenv, handlers[[nh]])
11: tryCatchList(expr, classes, parentenv, handlers)
12: tryCatch(evalq((function (par, fn, gr, lower = -Inf, upper = Inf,     control = list(), ...) {    .ctl <- control    .ctl <- .ctl[names(.ctl) %in% c("eval.max", "iter.max", "trace",         "abs.tol", "rel.tol", "x.tol", "xf.tol", "step.min",         "step.max", "sing.tol", "scale.inti", "diff.g")]    .ctl$trace <- 0    .ret <- stats::nlminb(start = par, objective = fn, gradient = gr,         hessian = NULL, control = .ctl, lower = lower, upper = upper)    .ret$x <- .ret$par    return(.ret)})(par = c(-1, -0.637832151326478, 1, -0.833333333333333, -0.542520422355001, -0.39919989679531, -0.114480393307385), fn = function (theta) {    .Call(`_nlmixr2est_foceiOuterF`, theta)}, gr = function (theta) {    .Call(`_nlmixr2est_foceiOuterG`, theta)}, lower = c(-Inf, -Inf, -Inf, -2.83333163034348, -0.873779194190821, -0.730458752094879, -0.44573935454913), upper = c(Inf, 2.97408411280245, Inf, Inf, 1.61975587753649, 1.76307631963243, 2.04779571717818), control = list(maxOuterIterations = 5000L, maxInnerIterations = 1000L,     n1qn1nsim = 10001L, print = 0L, lbfgsLmm = 7L, lbfgsPgtol = 0,     lbfgsFactr = 450359962737.05, scaleTo = 1, epsilon = 1e-04,     derivEps = c(2.98023223876953e-07, 2.98023223876953e-07),     derivMethod = 3L, covDerivMethod = 1L, covMethod = 1L, centralDerivEps = c(2.98023223876953e-07,     2.98023223876953e-07), eigen = 1L, addPosthoc = 1L, diagXform = "sqrt",     sumProd = FALSE, optExpression = TRUE, outerOpt = -1L, ci = 0.95,     sigdig = 3, sigdigTable = 3, scaleObjective = 0, useColor = FALSE,     boundTol = 0.05, calcTables = TRUE, printNcol = 4L, noAbort = 1L,     interaction = 1L, cholSEtol = 6.05545445239334e-06, hessEps = 6.05545445239334e-06,     cholAccept = 0.001, resetEtaSize = 1.43953147093846, resetThetaSize = 1.95996398454005,     resetThetaFinalSize = 1.43953147093846, diagOmegaBoundUpper = 5,     diagOmegaBoundLower = 100, cholSEOpt = 0L, cholSECov = 0L,     fo = 0L, covTryHarder = 0L, outerOptFun = function (par,         fn, gr, lower = -Inf, upper = Inf, control = list(),         ...)     {        .ctl <- control        .ctl <- .ctl[names(.ctl) %in% c("eval.max", "iter.max",             "trace", "abs.tol", "rel.tol", "x.tol", "xf.tol",             "step.min", "step.max", "sing.tol", "scale.inti",             "diff.g")]        .ctl$trace <- 0        .ret <- stats::nlminb(start = par, objective = fn, gradient = gr,             hessian = NULL, control = .ctl, lower = lower, upper = upper)        .ret$x <- .ret$par        return(.ret)    }, rhobeg = 0.2, rhoend = 1e-04, npt = NULL, rel.tol = 1e-04,     x.tol = 1e-04, eval.max = 4000, iter.max = 2000, innerOpt = 1L,     abstol = 1e-04, reltol = 1e-04, derivSwitchTol = 2e-04, resetHessianAndEta = 0L,     stateTrim = Inf, gillK = 10L, gillKcov = 10L, gillRtol = 1.49011611938477e-08,     gillStep = 4, gillStepCov = 2, scaleType = 2L, normType = 1L,     scaleC = NULL, scaleCmin = 1e-05, scaleCmax = 1e+05, scaleC0 = 1e+05,     outerOptTxt = "nlminb", rmatNorm = 1L, smatNorm = 1L, covGillF = 1L,     optGillF = 1L, gillFtol = 0, gillFtolCov = 0, covSmall = 1e-05,     adjLik = TRUE, gradTrim = Inf, gradCalcCentralSmall = 1e-04,     gradCalcCentralLarge = 10000, etaNudge = 1.13158573407617,     etaNudge2 = 1.51818157425799, maxOdeRecalc = 5L, odeRecalcFactor = 3.16227766016838,     nRetries = 3, seed = 42, resetThetaCheckPer = 0.1, etaMat = NULL,     repeatGillMax = 3L, stickyRecalcN = 5L, eventFD = 1.49011611938477e-08,     eventType = 1L, gradProgressOfvTime = 10, addProp = "combined2",     badSolveObjfAdj = 100, compress = TRUE, rxControl = list(        scale = NULL, method = c(liblsoda = 2L), atol = 5e-06,         rtol = 5e-06, maxsteps = 500000L, hmin = 0, hmax = NA_real_,         hini = 0, maxordn = 12L, maxords = 5L, covsInterpolation = c(locf = 1L),         addCov = TRUE, returnType = c(rxSolve = 0L), sigma = NULL,         sigmaDf = NULL, nCoresRV = 1L, sigmaIsChol = FALSE, sigmaSeparation = "auto",         sigmaXform = c(identity = 4L), nDisplayProgress = 10000L,         amountUnits = NA_character_, timeUnits = "hours", addDosing = FALSE,         stateTrim = Inf, updateObject = FALSE, omega = NULL,         omegaDf = NULL, omegaIsChol = FALSE, omegaSeparation = "auto",         omegaXform = c(variance = 6L), nSub = 1L, thetaMat = NULL,         thetaDf = NULL, thetaIsChol = FALSE, nStud = 1L, dfSub = 0,         dfObs = 0, seed = NULL, nsim = NULL, minSS = 10L, maxSS = 1000L,         strictSS = 1L, infSSstep = 12, istateReset = TRUE, subsetNonmem = TRUE,         hmaxSd = 0, maxAtolRtolFactor = 0.1, from = NULL, to = NULL,         by = NULL, length.out = NULL, iCov = NULL, keep = NULL,         keepF = character(0), drop = NULL, warnDrop = TRUE, omegaLower = -Inf,         omegaUpper = Inf, sigmaLower = -Inf, sigmaUpper = Inf,         thetaLower = -Inf, thetaUpper = Inf, indLinPhiM = 0L,         indLinPhiTol = 1e-07, indLinMatExpType = c(expokit = 2L),         indLinMatExpOrder = 6L, idFactor = TRUE, mxhnil = 0L,         hmxi = 0, warnIdSort = TRUE, ssAtol = 5e-04, ssRtol = 5e-04,         safeZero = 1L, sumType = c(pairwise = 1L), prodType = c(`long double` = 1L),         sensType = c(advan = 4L), linDiff = c(tlag = 1.5e-05,         f = 1.5e-05, rate = 1.5e-05, dur = 1.5e-05, tlag2 = 1.5e-05,         f2 = 1.5e-05, rate2 = 1.5e-05, dur2 = 1.5e-05), linDiffCentral = c(tlag = TRUE,         f = TRUE, rate = TRUE, dur = TRUE, tlag2 = TRUE, f2 = TRUE,         rate2 = TRUE, dur2 = TRUE), resample = NULL, resampleID = TRUE,         maxwhile = 100000L, cores = 1L, atolSens = 1.58113883008419e-05,         rtolSens = 1.58113883008419e-05, ssAtolSens = 0.00210848251714291,         ssRtolSens = 0.00210848251714291, simVariability = NA,         nLlikAlloc = NULL, useStdPow = 0L), genRxControl = TRUE,     skipCov = NULL, fallbackFD = FALSE, foceiMuRef = c(0L, 0L,     0L), predNeq = 0L, nfixed = 0L, nomega = 3L, neta = 3L, ntheta = 4L,     nF = 0, printTop = TRUE)), <environment>), error = function (x) x, interrupt = function (x) x)
13: foceiFitCpp_(.ret)
14: doTryCatch(return(expr), name, parentenv, handler)
15: tryCatchOne(expr, names, parentenv, handlers[[1L]])
16: tryCatchList(expr, classes, parentenv, handlers)
17: tryCatch({    foceiFitCpp_(.ret)}, error = function(e) {    if (regexpr("theta reset", e$message) != -1) {        assign("zeroOuter", FALSE, this.env)        assign("zeroGrad", FALSE, this.env)        if (regexpr("theta reset0", e$message) != -1) {            assign("zeroGrad", TRUE, this.env)        }        else if (regexpr("theta resetZ", e$message) != -1) {            assign("zeroOuter", TRUE, this.env)        }        assign("err", "theta reset", this.env)    }    else {        assign("err", e$message, this.env)    }})
18: .foceiFitInternal(.env)
19: doTryCatch(return(expr), name, parentenv, handler)
20: tryCatchOne(expr, names, parentenv, handlers[[1L]])
21: tryCatchList(expr, classes, parentenv, handlers)
22: tryCatch(expr, error = function(e) {    call <- conditionCall(e)    if (!is.null(call)) {        if (identical(call[[1L]], quote(doTryCatch)))             call <- sys.call(-4L)        dcall <- deparse(call, nlines = 1L)        prefix <- paste("Error in", dcall, ": ")        LONG <- 75L        sm <- strsplit(conditionMessage(e), "\n")[[1L]]        w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")        if (is.na(w))             w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],                 type = "b")        if (w > LONG)             prefix <- paste0(prefix, "\n  ")    }    else prefix <- "Error : "    msg <- paste0(prefix, conditionMessage(e), "\n")    .Internal(seterrmessage(msg[1L]))    if (!silent && isTRUE(getOption("show.error.messages"))) {        cat(msg, file = outFile)        .Internal(printDeferredWarnings())    }    invisible(structure(msg, class = "try-error", condition = e))})
23: try(.foceiFitInternal(.env))
24: .foceiFamilyReturn(env, .ui, ..., est = "focei")
25: nlmixr2Est.focei(env, ...)
26: nlmixr2Est(env, ...)
27: withCallingHandlers(expr, warning = function(w) {    assign("ws", unique(c(w$message, ws)), this.env)})
28: withCallingHandlers(expr, warning = function(w) if (inherits(w,     classes)) tryInvokeRestart("muffleWarning"))
29: suppressWarnings(withCallingHandlers(expr, warning = function(w) {    assign("ws", unique(c(w$message, ws)), this.env)}))
30: .collectWarnings(nlmixr2Est(env, ...), lst = TRUE)
31: doTryCatch(return(expr), name, parentenv, handler)
32: tryCatchOne(expr, names, parentenv, handlers[[1L]])
33: tryCatchList(expr, classes, parentenv, handlers)
34: tryCatch(expr, error = function(e) {    call <- conditionCall(e)    if (!is.null(call)) {        if (identical(call[[1L]], quote(doTryCatch)))             call <- sys.call(-4L)        dcall <- deparse(call, nlines = 1L)        prefix <- paste("Error in", dcall, ": ")        LONG <- 75L        sm <- strsplit(conditionMessage(e), "\n")[[1L]]        w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")        if (is.na(w))             w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],                 type = "b")        if (w > LONG)             prefix <- paste0(prefix, "\n  ")    }    else prefix <- "Error : "    msg <- paste0(prefix, conditionMessage(e), "\n")    .Internal(seterrmessage(msg[1L]))    if (!silent && isTRUE(getOption("show.error.messages"))) {        cat(msg, file = outFile)        .Internal(printDeferredWarnings())    }    invisible(structure(msg, class = "try-error", condition = e))})
35: try(.collectWarnings(nlmixr2Est(env, ...), lst = TRUE))
36: assign("ret", try(.collectWarnings(nlmixr2Est(env, ...), lst = TRUE)),     envir = .envReset)
37: nlmixr2Est0(.env)
38: nlmixr2.function(one.cmt, theo_sd, est = "focei", control = list(print = 0))
39: nlmixr(one.cmt, theo_sd, est = "focei", control = list(print = 0))
40: eval(expr, envir, enclos)
41: eval(expr, envir, enclos)
42: eval_with_user_handlers(expr, envir, enclos, user_handlers)
43: withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers))
44: withCallingHandlers(withVisible(eval_with_user_handlers(expr,     envir, enclos, user_handlers)), warning = wHandler, error = eHandler,     message = mHandler)
45: handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr,     envir, enclos, user_handlers)), warning = wHandler, error = eHandler,     message = mHandler))
46: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr,     envir, enclos, user_handlers)), warning = wHandler, error = eHandler,     message = mHandler)))
47: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,     debug = debug, last = i == length(out), use_try = stop_on_error !=         2L, keep_warning = keep_warning, keep_message = keep_message,     output_handler = output_handler, include_timing = include_timing)
48: evaluate::evaluate(...)
49: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),     keep_message = !isFALSE(options$message), stop_on_error = if (is.numeric(options$error)) options$error else {        if (options$error && options$include)             0L        else 2L    }, output_handler = knit_handlers(options$render, options))
50: in_dir(input_dir(), expr)
51: in_input_dir(evaluate(code, envir = env, new_device = FALSE,     keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),     stop_on_error = if (is.numeric(options$error)) options$error else {        if (options$error && options$include)             0L        else 2L    }, output_handler = knit_handlers(options$render, options)))
52: eng_r(options)
53: block_exec(params)
54: call_block(x)
55: process_group.block(group)
56: process_group(group)
57: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),     error = function(e) {        setwd(wd)        cat(res, sep = "\n", file = output %n% "")        message("Quitting from lines ", paste(current_lines(i),             collapse = "-"), " (", knit_concord$get("infile"),             ") ")    })
58: process_file(text, output)
59: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
60: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),     output_dir = getwd(), ...)
61: vweave_rmarkdown(...)
62: engine$weave(file, quiet = quiet, encoding = enc)
63: doTryCatch(return(expr), name, parentenv, handler)
64: tryCatchOne(expr, names, parentenv, handlers[[1L]])
65: tryCatchList(expr, classes, parentenv, handlers)
66: tryCatch({    engine$weave(file, quiet = quiet, encoding = enc)    setwd(startdir)    output <- find_vignette_product(name, by = "weave", engine = engine)    if (!have.makefile && vignette_is_tex(output)) {        texi2pdf(file = output, clean = FALSE, quiet = quiet)        output <- find_vignette_product(name, by = "texi2pdf",             engine = engine)    }    outputs <- c(outputs, output)}, error = function(e) {    thisOK <<- FALSE    fails <<- c(fails, file)    message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",         file, conditionMessage(e)))})
67: tools:::buildVignettes(dir = "/data/gannet/ripley/R/packages/tests-clang/nlmixr2.Rcheck/vign_test/nlmixr2")
An irrecoverable exception occurred. R is aborting now ...