diff --git a/inst/poped/ex.2.c.warfarin.ODE.compiled.babelmixr2.R b/inst/poped/ex.2.c.warfarin.ODE.compiled.babelmixr2.R
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+## Warfarin example from software comparison in:
+## Nyberg et al., "Methods and software tools for design evaluation
+##   for population pharmacokinetics-pharmacodynamics studies",
+##   Br. J. Clin. Pharm., 2014.
+
+## Optimization using an additive + proportional reidual error to
+##   avoid sample times at very low concentrations (time 0 or very late samoples).
+
+## Model described with an ODE
+library(PopED)
+library(babelmixr2)
+
+f <- function() {
+  ini({
+    tCl <- 0.15
+    tV <- 8
+    tKA <- 1.0
+    tFavail <- fix(1)
+    eta.cl ~ 0.07
+    eta.v ~ 0.02
+    eta.ka ~ 0.6
+    prop.sd <- sqrt(0.01) # nlmixr2 uses sd
+    add.sd <- sqrt(0.25)
+  })
+  model({
+    CL <- tCl*exp(eta.cl)
+    V <- tV*exp(eta.v)
+    KA <- tKA*exp(eta.ka)
+    Favail <- tFavail
+    d/dt(depot) <- -KA*depot
+    d/dt(central) <- KA*depot - (CL/V)*central
+    depot(0) <- Favail*DOSE
+    y <- central/V
+    y ~ prop(prop.sd) + add(add.sd)
+  })
+}
+
+## -- Define initial design  and design space
+e <-  et(c(0.5, 1,2,6,24,36,72,120)) %>%
+  as.data.frame()
+
+babel.db <- nlmixr2(f, e, "poped",
+                    popedControl(groupsize=32,
+                                 minxt=0,
+                                 maxxt=120,
+                                 a=70,
+                                 mina=0,
+                                 maxa=100))
+
+##  create plot of model without variability
+plot_model_prediction(babel.db)
+
+##  create plot of model with variability
+plot_model_prediction(babel.db,IPRED=T,DV=T)
+
+## evaluate initial design (much faster than pure R solution)
+tic(); design_ode_compiled <- evaluate_design(babel.db); toc()
+
+## making optimization times more resonable
+output <- poped_optim(babel.db, opt_xt =TRUE, parallel=TRUE, method = c("LS"))