diff --git a/kb07.qmd b/kb07.qmd
index be31a95..8fa9e14 100644
--- a/kb07.qmd
+++ b/kb07.qmd
@@ -10,7 +10,6 @@ Begin by loading the packages to be used.
 #| output: false
 using AlgebraOfGraphics
 using CairoMakie
-using DataFrameMacros
 using DataFrames
 using MixedModels
 using ProgressMeter
@@ -19,7 +18,6 @@ using SMLP2023: dataset
 
 CairoMakie.activate!(; type="svg")
 
-import ProgressMeter
 ProgressMeter.ijulia_behavior(:clear)
 ```
 
@@ -108,8 +106,7 @@ The pseudo-random number generator is initialized to a fixed value for reproduci
 
 ```{julia}
 Random.seed!(1234321)
-hide_progress = true
-kbm02samp = parametricbootstrap(2000, kbm02; hide_progress)
+kbm02samp = parametricbootstrap(2000, kbm02)
 kbm02tbl = kbm02samp.tbl
 ```
 
diff --git a/kkl15.qmd b/kkl15.qmd
index 2aeb0d9..1ae9e3a 100644
--- a/kkl15.qmd
+++ b/kkl15.qmd
@@ -38,6 +38,7 @@ Here we provide some of the corresponding analyses with _MixedModels.jl_ and a m
 #| output: false
 using Arrow
 using AlgebraOfGraphics
+using AlgebraOfGraphics: density
 using CairoMakie
 using CategoricalArrays
 using Chain
@@ -45,14 +46,11 @@ using DataFrameMacros
 using DataFrames
 using MixedModels
 using MixedModelsMakie
-using MixedModelsMakie: qqnorm
+using ProgressMeter
 using Random
 using SMLP2023: dataset
 using StatsBase
 
-using AlgebraOfGraphics: density
-
-import ProgressMeter
 ProgressMeter.ijulia_behavior(:clear)
 CairoMakie.activate!(; type="svg")
 ```
@@ -432,7 +430,7 @@ Interesting.
 Here we
 
   - generate a bootstrap sample
-  - compute shortest covergage intervals for the LMM parameters
+  - compute shortest coverage intervals for the LMM parameters
   - plot densities of bootstrapped parameter estimates for residual, fixed effects, variance components, and correlation parameters
 
 ## Generate a bootstrap sample
@@ -487,6 +485,7 @@ The shortest coverage interval for the `GM` ranges from 376 to 404 ms and the as
 #| code-fold: true
 #| label: fig-betadensitym1
 #| fig-cap: '  Kernel density estimate from bootstrap samples of the fixed effects for model  m1L'
+#=
 rn = renamer([
   "(Intercept)" => "GM",
   "CTR: sod" => "spatial effect",
@@ -499,6 +498,7 @@ rn = renamer([
   "CTR: sod, CTR: dod" => "spatial, attraction",
   "CTR: dos, CTR: dod" => "object, attraction",
 ])
+=#
 draw(
   data(tbl) *
   mapping(
diff --git a/kwdyz11.qmd b/kwdyz11.qmd
index 2fa66cb..9739993 100644
--- a/kwdyz11.qmd
+++ b/kwdyz11.qmd
@@ -1,5 +1,5 @@
 ---
-title: "RePsychLing Kliegl et al. (2010)"
+title: "RePsychLing Kliegl et al. (2011)"
 jupyter: julia-1.9
 author: "Reinhold Kliegl"
 ---
@@ -284,7 +284,7 @@ We generate 2500 samples for the 15 model parameters (4 fixed effect, 4 VCs, 6 C
 ```{julia}
 #| code-fold: true
 Random.seed!(1234321)
-samp = parametricbootstrap(2500, m1; hide_progress=true)
+samp = parametricbootstrap(2500, m1)
 tbl = samp.tbl
 ```
 
diff --git a/sleepstudy.qmd b/sleepstudy.qmd
index d2e652f..07e8ef7 100644
--- a/sleepstudy.qmd
+++ b/sleepstudy.qmd
@@ -216,7 +216,7 @@ Simulating and fitting a substantial number of model fits, 5000 in this case, ta
 
 ```{julia}
 rng = Random.seed!(42)    # initialize a random number generator
-m1bstp = parametricbootstrap(rng, 5000, m1; hide_progress=true)
+m1bstp = parametricbootstrap(rng, 5000, m1)
 tbl = m1bstp.tbl
 ```