diff --git a/inst/poped/ex.1.a.PK.1.comp.oral.md.intro.babelmixr2.R b/inst/poped/ex.1.a.PK.1.comp.oral.md.intro.babelmixr2.R
index 46caf211..b35491b6 100644
--- a/inst/poped/ex.1.a.PK.1.comp.oral.md.intro.babelmixr2.R
+++ b/inst/poped/ex.1.a.PK.1.comp.oral.md.intro.babelmixr2.R
@@ -66,7 +66,7 @@ evaluate_design(babel.db)
 shrinkage(babel.db)
 
 # Optimization of sample times
-output <- poped_optim(babel.db, opt_xt =TRUE, parallel=TRUE)
+output <- poped_optim(babel.db, opt_xt =TRUE)
 
 # Evaluate optimization results
 summary(output)
@@ -77,7 +77,7 @@ plot_model_prediction(output$poped.db)
 
 
 # Optimization of sample times and doses
-output_2 <- poped_optim(output$poped.db, opt_xt =TRUE, opt_a = TRUE, parallel = TRUE)
+output_2 <- poped_optim(output$poped.db, opt_xt =TRUE, opt_a = TRUE)
 
 summary(output_2)
 
@@ -90,7 +90,7 @@ plot_model_prediction(output_2$poped.db)
 # faster than continuous optimization in this case
 babel.db.discrete <- create.poped.database(babel.db,discrete_xt = list(0:248))
 
-output_discrete <- poped_optim(babel.db.discrete, opt_xt=T, parallel = TRUE)
+output_discrete <- poped_optim(babel.db.discrete, opt_xt=T)
 
 
 summary(output_discrete)
diff --git a/inst/poped/ex.1.b.PK.1.comp.oral.md.re-parameterize.babelmixr2.R b/inst/poped/ex.1.b.PK.1.comp.oral.md.re-parameterize.babelmixr2.R
new file mode 100644
index 00000000..0205b1ec
--- /dev/null
+++ b/inst/poped/ex.1.b.PK.1.comp.oral.md.re-parameterize.babelmixr2.R
@@ -0,0 +1,99 @@
+## using libary models and reparameterizing the problen to KA, KE and V
+## optimization of dose and dose interval
+
+library(babelmixr2)
+
+library(PopED)
+
+f <- function() {
+  ini({
+    tV <- 72.8
+    tKa <- 0.25
+    tKe <- 3.75/72.8
+    tFavail <- fix(0.9)
+
+    eta.v ~ 0.09
+    eta.ka ~ 0.09
+    eta.ke ~ 0.25^2
+
+    prop.sd <- fix(sqrt(0.04))
+    add.sd <- fix(sqrt(5e-6))
+  })
+  model({
+    V <- tV*exp(eta.v)
+    KA <- tKa*exp(eta.ka)
+    KE <- tKe*exp(eta.ke)
+    Favail <- tFavail
+    N <- floor(time/TAU)+1
+    y <- (DOSE*Favail/V)*(KA/(KA - KE)) *
+      (exp(-KE * (time - (N - 1) * TAU)) * (1 - exp(-N * KE * TAU))/(1 - exp(-KE * TAU)) -
+         exp(-KA * (time - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))
+
+    y ~ prop(prop.sd) + add(add.sd)
+  })
+}
+
+# minxt, maxxt
+e <- et(list(c(0, 10),
+             c(0, 10),
+             c(0, 10),
+             c(240, 248),
+             c(240, 248))) %>%
+  as.data.frame()
+
+#xt
+e$time <-  c(1,2,8,240,245)
+
+
+babel.db <- nlmixr2(f, e, "poped",
+                    popedControl(groupsize=20,
+                                 bUseGrouped_xt=TRUE,
+                                 a=list(c(DOSE=20,TAU=24),
+                                        c(DOSE=40, TAU=24)),
+                                 maxa=c(DOSE=200,TAU=24),
+                                 mina=c(DOSE=0,TAU=24)))
+
+
+##  create plot of model without variability
+plot_model_prediction(babel.db)
+
+##  create plot of model with variability
+plot_model_prediction(babel.db,IPRED=T,DV=T,separate.groups=T)
+
+## evaluate initial design
+evaluate_design(babel.db)
+
+shrinkage(babel.db)
+
+# Optimization of sample times
+output <- poped_optim(babel.db, opt_xt =TRUE, parallel=TRUE)
+
+# Evaluate optimization results
+summary(output)
+
+get_rse(output$FIM,output$poped.db)
+
+plot_model_prediction(output$poped.db)
+
+# Optimization of sample times, doses and dose intervals
+output_2 <- poped_optim(output$poped.db, opt_xt =TRUE, opt_a = TRUE, parallel = TRUE)
+
+summary(output_2)
+get_rse(output_2$FIM,output_2$poped.db)
+plot_model_prediction(output_2$poped.db)
+
+# Optimization of sample times with only integer time points in design space
+# faster than continuous optimization in this case
+babel.db.discrete <- create.poped.database(babel.db,discrete_xt = list(0:248))
+
+output_discrete <- poped_optim(babel.db.discrete, opt_xt=T, parallel = TRUE)
+
+summary(output_discrete)
+
+get_rse(output_discrete$FIM,output_discrete$poped.db)
+
+plot_model_prediction(output_discrete$poped.db)
+
+
+# Efficiency of sampling windows
+plot_efficiency_of_windows(output_discrete$poped.db, xt_windows=1)
diff --git a/inst/poped/ex.1.c.PK.1.comp.oral.md.ODE.compiled.babelmixr2.R b/inst/poped/ex.1.c.PK.1.comp.oral.md.ODE.compiled.babelmixr2.R
new file mode 100644
index 00000000..611da739
--- /dev/null
+++ b/inst/poped/ex.1.c.PK.1.comp.oral.md.ODE.compiled.babelmixr2.R
@@ -0,0 +1,102 @@
+library(babelmixr2)
+library(PopED)
+
+
+## define the ODE
+f <- function() {
+  ini({
+    tV <- 72.8
+    tKa <- 0.25
+    tCl <- 3.75
+    tF <- fix(0.9)
+
+    eta.v ~ 0.09
+    eta.ka ~ 0.09
+    eta.cl ~0.25^2
+
+    prop.sd <- fix(sqrt(0.04))
+    add.sd <- fix(sqrt(5e-6))
+
+  })
+  model({
+    V<-tV*exp(eta.v)
+    KA<-tKa*exp(eta.ka)
+    CL<-tCl*exp(eta.cl)
+    Favail <- tF
+    d/dt(depot) <- -KA*depot
+    d/dt(central) <- KA*depot - (CL/V)*central
+    f(depot) <- Favail*DOSE
+    y <- central/V
+    y ~ prop(prop.sd) + add(add.sd)
+  })
+}
+
+# minxt, maxxt
+e <- et(list(c(0, 10),
+             c(0, 10),
+             c(0, 10),
+             c(240, 248),
+             c(240, 248))) %>%
+  et(amt=1000, ii=24, until=248,cmt="depot") %>%
+  as.data.frame()
+
+#xt
+e$time <-  c(0, 1,2,8,240,245)
+
+
+babel.db <- nlmixr2(f, e, "poped",
+                    popedControl(groupsize=20,
+                                 bUseGrouped_xt=TRUE,
+                                 a=list(c(DOSE=20,TAU=24),
+                                        c(DOSE=40, TAU=24)),
+                                 maxa=c(DOSE=200,TAU=24),
+                                 mina=c(DOSE=0,TAU=24)))
+
+##  create plot of model without variability
+plot_model_prediction(babel.db, model_num_points = 300)
+
+##  create plot of model with variability
+plot_model_prediction(babel.db, IPRED=T, DV=T, separate.groups=T, model_num_points = 300)
+
+## evaluate initial design
+evaluate_design(babel.db)
+
+shrinkage(babel.db)
+
+# Optimization of sample times
+output <- poped_optim(babel.db, opt_xt =TRUE)
+
+# Evaluate optimization results
+summary(output)
+
+get_rse(output$FIM,output$poped.db)
+
+plot_model_prediction(output$poped.db)
+
+
+# Optimization of sample times and doses
+output_2 <- poped_optim(output$poped.db, opt_xt =TRUE, opt_a = TRUE)
+
+summary(output_2)
+
+get_rse(output_2$FIM,output_2$poped.db)
+
+plot_model_prediction(output_2$poped.db)
+
+
+# Optimization of sample times with only integer time points in design space
+# faster than continuous optimization in this case
+babel.db.discrete <- create.poped.database(babel.db,discrete_xt = list(0:248))
+
+output_discrete <- poped_optim(babel.db.discrete, opt_xt=T)
+
+
+summary(output_discrete)
+
+get_rse(output_discrete$FIM,output_discrete$poped.db)
+
+plot_model_prediction(output_discrete$poped.db)
+
+
+# Efficiency of sampling windows
+plot_efficiency_of_windows(output_discrete$poped.db, xt_windows=1)