diff --git a/DESCRIPTION b/DESCRIPTION
index 0e95dafafc..4b1a48486b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: tern
 Title: Create Common TLGs Used in Clinical Trials
-Version: 0.7.9.9004
-Date: 2022-08-30
+Version: 0.7.9.9005
+Date: 2022-09-09
 Authors@R: c(
     person("Joe", "Zhu", , "joe.zhu@roche.com", role = c("aut", "cre")),
     person("Daniel", "Sabanés Bové", , "daniel.sabanes_bove@roche.com", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 2df56724d2..cfb2ffa516 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# tern 0.7.9.9004
+# tern 0.7.9.9005
 
 ### Enhancements 
 * Added `DescTools` `BinomDiffCI` function within `tern`.
diff --git a/R/desctools_binom_diff.R b/R/desctools_binom_diff.R
index 26fa77601d..3a62249f37 100644
--- a/R/desctools_binom_diff.R
+++ b/R/desctools_binom_diff.R
@@ -39,7 +39,10 @@ h_recycle <- function(...) {
 #' rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20)
 #' grp <- factor(c(rep("A", 10), rep("B", 10)))
 #' tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))
-#' desctools_binom(tbl[1], sum(tbl[1], tbl[3]), tbl[2], sum(tbl[2], tbl[4]), conf.level = 0.90, method = "waldcc")
+#' desctools_binom(
+#'   tbl[1], sum(tbl[1], tbl[3]), tbl[2], sum(tbl[2], tbl[4]),
+#'   conf.level = 0.90, method = "waldcc"
+#' )
 #' }
 #'
 #' @keywords internal
diff --git a/R/g_lineplot.R b/R/g_lineplot.R
index aab6aa08e5..58511ff22f 100644
--- a/R/g_lineplot.R
+++ b/R/g_lineplot.R
@@ -81,8 +81,9 @@
 #' @examples
 #' library(nestcolor)
 #'
-#' adsl <- scda::synthetic_cdisc_data("latest")$adsl
-#' adlb <- scda::synthetic_cdisc_data("latest")$adlb
+#' cached_data <- scda::synthetic_cdisc_data("latest")
+#' adsl <- cached_data$adsl
+#' adlb <- cached_data$adlb
 #' adlb <- dplyr::filter(adlb, ANL01FL == "Y", PARAMCD == "ALT", AVISIT != "SCREENING")
 #' adlb$AVISIT <- droplevels(adlb$AVISIT)
 #' adlb <- dplyr::mutate(adlb, AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min))
diff --git a/R/summarize_ancova.R b/R/summarize_ancova.R
index da04a4c97d..857ac12f9a 100644
--- a/R/summarize_ancova.R
+++ b/R/summarize_ancova.R
@@ -118,7 +118,11 @@ h_ancova <- function(.var,
 #'
 #' # Internal function - s_ancova
 #' \dontrun{
-#' s_ancova(df, .var, .df_row, variables, .ref_group, .in_ref_col = FALSE, conf_level, interaction_y = FALSE, interaction_item = NULL)
+#' s_ancova(
+#'   df, .var, .df_row, variables, .ref_group,
+#'   .in_ref_col = FALSE,
+#'   conf_level, interaction_y = FALSE, interaction_item = NULL
+#' )
 #' }
 #'
 #' @keywords internal
@@ -213,7 +217,11 @@ s_ancova <- function(df,
 #' @examples
 #' # Internal function - a_ancova
 #' \dontrun{
-#' a_ancova(df, .var, .df_row, variables, .ref_group, .in_ref_col = FALSE, interaction_y = FALSE, interaction_item = NULL, conf_level)
+#' a_ancova(
+#'   df, .var, .df_row, variables, .ref_group,
+#'   .in_ref_col = FALSE,
+#'   interaction_y = FALSE, interaction_item = NULL, conf_level
+#' )
 #' }
 #'
 #' @keywords internal
@@ -240,8 +248,10 @@ a_ancova <- make_afun(
 #' library(rtables)
 #' library(dplyr)
 #'
-#' adsl <- synthetic_cdisc_data("rcd_2022_02_28")$adsl
-#' adqs <- synthetic_cdisc_data("rcd_2022_02_28")$adqs
+#' cached_data <- synthetic_cdisc_data("rcd_2022_02_28")
+#'
+#' adsl <- cached_data$adsl
+#' adqs <- cached_data$adqs
 #' adqs_single <- adqs %>%
 #'   filter(
 #'     AVISIT == "WEEK 1 DAY 8", # single time point
@@ -270,8 +280,9 @@ a_ancova <- make_afun(
 #'   build_table(adqs_single, alt_counts_df = adsl)
 #'
 #' # Another example: count the interaction between rows and columns into consideration
-#' adsl <- synthetic_cdisc_data("rcd_2022_02_28")$adsl
-#' adqs <- synthetic_cdisc_data("rcd_2022_02_28")$adqs
+#'
+#' adsl <- cached_data$adsl
+#' adqs <- cached_data$adqs
 #' adqs_single <- adqs %>%
 #'   filter(AVISIT %in% c("WEEK 1 DAY 8", "WEEK 2 DAY 15", "WEEK 5 DAY 36")) %>%
 #'   droplevels() %>%
diff --git a/man/desctools_binom.Rd b/man/desctools_binom.Rd
index c9663bd4db..eecc0b1539 100644
--- a/man/desctools_binom.Rd
+++ b/man/desctools_binom.Rd
@@ -84,7 +84,10 @@ set.seed(2)
 rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20)
 grp <- factor(c(rep("A", 10), rep("B", 10)))
 tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))
-desctools_binom(tbl[1], sum(tbl[1], tbl[3]), tbl[2], sum(tbl[2], tbl[4]), conf.level = 0.90, method = "waldcc")
+desctools_binom(
+  tbl[1], sum(tbl[1], tbl[3]), tbl[2], sum(tbl[2], tbl[4]),
+  conf.level = 0.90, method = "waldcc"
+)
 }
 
 }
diff --git a/man/g_lineplot.Rd b/man/g_lineplot.Rd
index 0f41764420..1046bc1496 100644
--- a/man/g_lineplot.Rd
+++ b/man/g_lineplot.Rd
@@ -144,8 +144,9 @@ Line plot with the optional table
 \examples{
 library(nestcolor)
 
-adsl <- scda::synthetic_cdisc_data("latest")$adsl
-adlb <- scda::synthetic_cdisc_data("latest")$adlb
+cached_data <- scda::synthetic_cdisc_data("latest")
+adsl <- cached_data$adsl
+adlb <- cached_data$adlb
 adlb <- dplyr::filter(adlb, ANL01FL == "Y", PARAMCD == "ALT", AVISIT != "SCREENING")
 adlb$AVISIT <- droplevels(adlb$AVISIT)
 adlb <- dplyr::mutate(adlb, AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min))
diff --git a/man/summarize_ancova.Rd b/man/summarize_ancova.Rd
index 8be3a481c3..bdb9225de0 100644
--- a/man/summarize_ancova.Rd
+++ b/man/summarize_ancova.Rd
@@ -164,12 +164,20 @@ conf_level <- 0.95
 
 # Internal function - s_ancova
 \dontrun{
-s_ancova(df, .var, .df_row, variables, .ref_group, .in_ref_col = FALSE, conf_level, interaction_y = FALSE, interaction_item = NULL)
+s_ancova(
+  df, .var, .df_row, variables, .ref_group,
+  .in_ref_col = FALSE,
+  conf_level, interaction_y = FALSE, interaction_item = NULL
+)
 }
 
 # Internal function - a_ancova
 \dontrun{
-a_ancova(df, .var, .df_row, variables, .ref_group, .in_ref_col = FALSE, interaction_y = FALSE, interaction_item = NULL, conf_level)
+a_ancova(
+  df, .var, .df_row, variables, .ref_group,
+  .in_ref_col = FALSE,
+  interaction_y = FALSE, interaction_item = NULL, conf_level
+)
 }
 
 
@@ -177,8 +185,10 @@ library(scda)
 library(rtables)
 library(dplyr)
 
-adsl <- synthetic_cdisc_data("rcd_2022_02_28")$adsl
-adqs <- synthetic_cdisc_data("rcd_2022_02_28")$adqs
+cached_data <- synthetic_cdisc_data("rcd_2022_02_28")
+
+adsl <- cached_data$adsl
+adqs <- cached_data$adqs
 adqs_single <- adqs \%>\%
   filter(
     AVISIT == "WEEK 1 DAY 8", # single time point
@@ -207,8 +217,9 @@ basic_table() \%>\%
   build_table(adqs_single, alt_counts_df = adsl)
 
 # Another example: count the interaction between rows and columns into consideration
-adsl <- synthetic_cdisc_data("rcd_2022_02_28")$adsl
-adqs <- synthetic_cdisc_data("rcd_2022_02_28")$adqs
+
+adsl <- cached_data$adsl
+adqs <- cached_data$adqs
 adqs_single <- adqs \%>\%
   filter(AVISIT \%in\% c("WEEK 1 DAY 8", "WEEK 2 DAY 15", "WEEK 5 DAY 36")) \%>\%
   droplevels() \%>\%
diff --git a/vignettes/tern.Rmd b/vignettes/tern.Rmd
index 2563b697a8..60dbd8effd 100644
--- a/vignettes/tern.Rmd
+++ b/vignettes/tern.Rmd
@@ -179,4 +179,4 @@ The [`teal` package](https://insightsengineering.github.io/teal/) is a shiny-bas
 
 In summary, `tern` contains many additional functions for creating tables, listing and graphs used in clinical trials and other statistical analyses. The design of the package gives users a lot of flexibility to meet the analysis needs in a regulatory or exploratory reporting context.
 
-**For more information please explore [the tern website](https://insightsengineering.github.io/tern).**
+**For more information please explore [the tern website](https://insightsengineering.github.io/tern/).**