diff --git a/R/slide.R b/R/slide.R
index 5a6d8264..f3d27b21 100644
--- a/R/slide.R
+++ b/R/slide.R
@@ -70,6 +70,8 @@
 #' @export
 #' @seealso [`epi_slide_opt`] for optimized slide functions
 #' @examples
+#' library(dplyr)
+#'
 #' # Get the 7-day trailing standard deviation of cases and the 7-day trailing mean of cases
 #' cases_deaths_subset %>%
 #'   epi_slide(
@@ -77,45 +79,72 @@
 #'     cases_7dav = mean(cases, na.rm = TRUE),
 #'     .window_size = 7
 #'   ) %>%
-#'   dplyr::select(geo_value, time_value, cases, cases_7sd, cases_7dav)
+#'   select(geo_value, time_value, cases, cases_7sd, cases_7dav)
+#' # Note that epi_slide_mean could be used to more quickly calculate cases_7dav.
+#'
+#' # In addition to the [`dplyr::mutate`]-like syntax, you can feed in a function or
+#' # formula in a way similar to [`dplyr::group_modify`]:
+#' my_summarizer <- function(window_data) {
+#'   window_data %>%
+#'     summarize(
+#'       cases_7sd = sd(cases, na.rm = TRUE),
+#'       cases_7dav = mean(cases, na.rm = TRUE)
+#'     )
+#' }
+#' cases_deaths_subset %>%
+#'   epi_slide(
+#'     ~ my_summarizer(.x),
+#'     .window_size = 7
+#'   ) %>%
+#'   select(geo_value, time_value, cases, cases_7sd, cases_7dav)
+#'
+#'
 #'
-#' # The same as above, but unpacking using an unnamed data.frame with a formula
+#'
+#'
+#' #### Advanced: ####
+#'
+#' # The tidyverse supports ["packing"][tidyr::pack] multiple columns into a
+#' # single tibble-type column contained within some larger tibble. Like dplyr,
+#' # we normally don't pack output columns together, but will if you provide a
+#' # name for a tibble-type output:
 #' cases_deaths_subset %>%
 #'   epi_slide(
-#'     ~ data.frame(
+#'     slide_packed = tibble(
 #'       cases_7sd = sd(.x$cases, na.rm = TRUE),
 #'       cases_7dav = mean(.x$cases, na.rm = TRUE)
 #'     ),
 #'     .window_size = 7
 #'   ) %>%
-#'   dplyr::select(geo_value, time_value, cases, cases_7sd, cases_7dav)
-#'
-#' # The same as above, but packing using a named data.frame with a tidy evaluation
-#' # expression
+#'   select(geo_value, time_value, cases, slide_packed)
 #' cases_deaths_subset %>%
 #'   epi_slide(
-#'     slide_packed = data.frame(
+#'     ~ tibble(
 #'       cases_7sd = sd(.x$cases, na.rm = TRUE),
 #'       cases_7dav = mean(.x$cases, na.rm = TRUE)
 #'     ),
+#'     .new_col_name = "slide_packed",
 #'     .window_size = 7
 #'   ) %>%
-#'   dplyr::select(geo_value, time_value, cases, slide_packed)
+#'   select(geo_value, time_value, cases, slide_packed)
 #'
-#' # nested new columns
+#' # You can also get ["nested"][tidyr::nest] format by wrapping your results in
+#' # a list:
 #' cases_deaths_subset %>%
 #'   group_by(geo_value) %>%
 #'   epi_slide(
 #'     function(x, g, t) {
-#'       data.frame(
+#'       list(tibble(
 #'         cases_7sd = sd(x$cases, na.rm = TRUE),
 #'         cases_7dav = mean(x$cases, na.rm = TRUE)
-#'       )
+#'       ))
 #'     },
 #'     .window_size = 7
 #'   ) %>%
 #'   ungroup() %>%
-#'   dplyr::select(geo_value, time_value, cases, cases_7sd, cases_7dav)
+#'   select(geo_value, time_value, slide_value)
+#'
+#'
 #'
 #' # Use the geo_value or the ref_time_value in the slide computation
 #' cases_deaths_subset %>%
@@ -943,10 +972,8 @@ epi_slide_opt <- function(
 #' # output column names:
 #' covid_case_death_rates_extended %>%
 #'   group_by(geo_value) %>%
-#'   epi_slide_mean(c(case_rate, death_rate),
-#'     .window_size = 7,
-#'     .new_col_names = c("smoothed_case_rate", "smoothed_death_rate")
-#'   ) %>%
+#'   epi_slide_mean(c(case_rate, death_rate), .window_size = 7,
+#'                  .new_col_names = c("smoothed_case_rate", "smoothed_death_rate")) %>%
 #'   ungroup()
 #' covid_case_death_rates_extended %>%
 #'   group_by(geo_value) %>%
diff --git a/man/epi_slide.Rd b/man/epi_slide.Rd
index 511ba8a8..c497b5d3 100644
--- a/man/epi_slide.Rd
+++ b/man/epi_slide.Rd
@@ -128,6 +128,8 @@ determined the time window for the current computation.
 }
 }
 \examples{
+library(dplyr)
+
 # Get the 7-day trailing standard deviation of cases and the 7-day trailing mean of cases
 cases_deaths_subset \%>\%
   epi_slide(
@@ -135,45 +137,72 @@ cases_deaths_subset \%>\%
     cases_7dav = mean(cases, na.rm = TRUE),
     .window_size = 7
   ) \%>\%
-  dplyr::select(geo_value, time_value, cases, cases_7sd, cases_7dav)
+  select(geo_value, time_value, cases, cases_7sd, cases_7dav)
+# Note that epi_slide_mean could be used to more quickly calculate cases_7dav.
+
+# In addition to the [`dplyr::mutate`]-like syntax, you can feed in a function or
+# formula in a way similar to [`dplyr::group_modify`]:
+my_summarizer <- function(window_data) {
+  window_data \%>\%
+    summarize(
+      cases_7sd = sd(cases, na.rm = TRUE),
+      cases_7dav = mean(cases, na.rm = TRUE)
+    )
+}
+cases_deaths_subset \%>\%
+  epi_slide(
+    ~ my_summarizer(.x),
+    .window_size = 7
+  ) \%>\%
+  select(geo_value, time_value, cases, cases_7sd, cases_7dav)
+
 
-# The same as above, but unpacking using an unnamed data.frame with a formula
+
+
+
+#### Advanced: ####
+
+# The tidyverse supports ["packing"][tidyr::pack] multiple columns into a
+# single tibble-type column contained within some larger tibble. Like dplyr,
+# we normally don't pack output columns together, but will if you provide a
+# name for a tibble-type output:
 cases_deaths_subset \%>\%
   epi_slide(
-    ~ data.frame(
+    slide_packed = tibble(
       cases_7sd = sd(.x$cases, na.rm = TRUE),
       cases_7dav = mean(.x$cases, na.rm = TRUE)
     ),
     .window_size = 7
   ) \%>\%
-  dplyr::select(geo_value, time_value, cases, cases_7sd, cases_7dav)
-
-# The same as above, but packing using a named data.frame with a tidy evaluation
-# expression
+  select(geo_value, time_value, cases, slide_packed)
 cases_deaths_subset \%>\%
   epi_slide(
-    slide_packed = data.frame(
+    ~ tibble(
       cases_7sd = sd(.x$cases, na.rm = TRUE),
       cases_7dav = mean(.x$cases, na.rm = TRUE)
     ),
+    .new_col_name = "slide_packed",
     .window_size = 7
   ) \%>\%
-  dplyr::select(geo_value, time_value, cases, slide_packed)
+  select(geo_value, time_value, cases, slide_packed)
 
-# nested new columns
+# You can also get ["nested"][tidyr::nest] format by wrapping your results in
+# a list:
 cases_deaths_subset \%>\%
   group_by(geo_value) \%>\%
   epi_slide(
     function(x, g, t) {
-      data.frame(
+      list(tibble(
         cases_7sd = sd(x$cases, na.rm = TRUE),
         cases_7dav = mean(x$cases, na.rm = TRUE)
-      )
+      ))
     },
     .window_size = 7
   ) \%>\%
   ungroup() \%>\%
-  dplyr::select(geo_value, time_value, cases, cases_7sd, cases_7dav)
+  select(geo_value, time_value, slide_value)
+
+
 
 # Use the geo_value or the ref_time_value in the slide computation
 cases_deaths_subset \%>\%