Felix review

generation/index.qmd

@@ -8,7 +8,16 @@ Throughout, we will use the `synthpop` package [@synthpop], which is a powerful
 Other alternatives to create synthetic data are, for example, the `R`-package `mice` [@mice; @volker_anonymiced_2021] or the stand-alone software `IVEware` [@iveware].
 Additionally, we will use the package `densityratio` [@densityratio] to evaluate the utility of synthetic data.
 
-Make sure to load all of the required packages, and in case you haven't installed them already, install them first, using `install.packages("package_name")`.
+Make sure to load all of the required packages, and in case you haven't installed them already, install them first:
+
+```{r}
+#| label: install-packages
+#| eval: false
+
+install.packages("synthpop")
+install.packages("densityratio")
+install.packages("mvtnorm")
+```
 
 ---
 
@@ -99,7 +108,9 @@ __3. Use the `summary()` function to get an overview of the data.__
 summary(data)
 ```
 
-You may notice a couple of things. First, the data seems to be sorted by age. You may verify this by running `!is.unsorted(data$age)`. Second, you may notice that most variables are non-negative, which might be something you want to take into account when modelling the data (but perhaps, this is not so relevant for the analysis at hand; for now, we assume it is).
+You may notice a couple of things. First, the data seems to be sorted by age. You may verify this by running `!is.unsorted(data$age)`. 
+<!-- Why is this relevant? -->
+Second, you may notice that most variables are non-negative, which might be something you want to take into account when modelling the data (but perhaps, this is not so relevant for the analysis at hand; for now, we assume it is).
 Third, you may notice that the variable `bmi` is mathematically linked to the variables `hgt` and `wgt`. 
 We want to take this into account when modelling the data.
 Finally, note that the data consist of a mix of continuous and categorical variables. 
@@ -117,7 +128,7 @@ The other arguments allow to specify various modelling choices, for example whic
 
 ::: {.callout-tip title = "Modelling choices in synthpop"}
 
-Some of the most important arguments of the `syn()` function are the following (you can use `?synthpop::syn()` for a more exaustive list). The `syn()` function will take these modelling choices into account when modelling each synthetic variable, and the resulting synthetic data, called `syn` in the output list, adheres to these specifications. 
+Some of the most important arguments of the `syn()` function are the following (you can use `?synthpop::syn()` for a more exhaustive list). The `syn()` function will take these modelling choices into account when modelling each synthetic variable, and the resulting synthetic data, called `syn` in the output list, adheres to these specifications. 
 
 ### `method`
 

index.qmd

@@ -9,7 +9,7 @@ This **self-paced tutorial** will introduce you to the generation and evaluation
 Synthetic data is generated data that can be used as an alternative to privacy-sensitive data, for example to enhance open science practices. 
 Advantages of open (synthetic) data are numerous: other researchers can re-run analyses with data that is close to the actual data, which allows them to verify the main results. 
 Additionally, open (synthetic) data allows researchers to perform exploratory analyses that may lead to novel hypotheses, and in quite some instances performing such analyses with synthetic data yields rather accurate results.
-Moreover, realistic synthetic can be used in teaching, or for starting with model building when access to the real data is currently still prohibited. 
+Moreover, realistic synthetic data can be used in teaching, or for starting with model building when access to the real data is currently still prohibited. 
 All in all, synthetic data makes open science practices easier and might spark collaborations with potential data users.