Revert "Disable chunks"

inst/pages/differential_abundance.qmd

@@ -199,11 +199,6 @@ folder for some output files. Lastly, we prevent `Maaslin2()` to create certain
 plots. (Note that `Maaslin2()` prints a lot of some output
 which is suppressed here for convenience.)
 
-```{r}
-#| label: disable_chunks
-knitr::opts_chunk$set(eval = FALSE)
-```
-
 ```{r}
 #| label: run_daa_basic
 #| results: hide
@@ -558,11 +553,6 @@ cohort).
 Note that a similar plot could also be used to illustrate a possible confounding
 effect.
 
-```{r}
-#| label: enable_chunks
-knitr::opts_chunk$set(eval = TRUE)
-```
-
 ### Differential prevalence analysis (DPA) {#sec-DPA}
 
 We show here how to perform DPA with logistic regression. We show the case

inst/pages/integrated_learner.qmd

@@ -146,11 +146,6 @@ integrates the outputs from these individual models to make the final
 predictions. For "base learners" and "meta learner", we use _random forest_
 and _rank loss minimization_ models, respectively.
 
-```{r}
-#| label: disable_chunks
-knitr::opts_chunk$set(eval = FALSE)
-```
-
 ```{r}
 #| label: fit_model
 

inst/pages/mmuphin_meta_analysis.qmd

@@ -130,11 +130,6 @@ abundance file, which includes the study names and overall response rate
 (ORR) the response variable along with other metadata. By the end of
 this step, we obtain a batch-adjusted relative abundance matrix.
 
-```{r}
-#| label: disable_chunks
-knitr::opts_chunk$set(eval = FALSE)
-```
-
 ```{r}
 #| label: prepare_input_data
 

inst/pages/msea.qmd

@@ -98,11 +98,6 @@ microbial species abundance versus IBD diagnosis. The analysis method we use
 here is "LM", which is the default setting. We also adjust for age and
 antibiotic usage, following the original study.
 
-```{r}
-#| label: disable_chunks
-knitr::opts_chunk$set(eval = FALSE)
-```
-
 ```{r}
 #| label: fit_model_Maaslin2