diff --git a/DESCRIPTION b/DESCRIPTION
index 3c409c31..0c871dca 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: epiprocess
 Title: Tools for basic signal processing in epidemiology
-Version: 0.7.10
+Version: 0.7.11
 Authors@R: c(
     person("Jacob", "Bien", role = "ctb"),
     person("Logan", "Brooks", email = "lcbrooks@andrew.cmu.edu", role = c("aut", "cre")),
diff --git a/NEWS.md b/NEWS.md
index 1bfdd9c5..57256cd7 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -16,6 +16,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.x.y will indicat
 - Add new `epi_slide_opt` function to allow much faster rolling computations
   in some cases, using `data.table` and `slider` optimized rolling functions
   (#433).
+- Add tidyselect interface for `epi_slide_opt` and derivatives (#452).
 - regenerated the `jhu_csse_daily_subset` dataset with the latest versions of
   the data from the API
 - changed approach to versioning, see DEVELOPMENT.md for details
diff --git a/R/slide.R b/R/slide.R
index 416127ef..27a3135c 100644
--- a/R/slide.R
+++ b/R/slide.R
@@ -86,8 +86,8 @@
 #' @seealso [`epi_slide_opt`] [`epi_slide_mean`] [`epi_slide_sum`]
 #' @examples
 #' # slide a 7-day trailing average formula on cases
-#' # This and other simple sliding means are much faster to do using
-#' # the `epi_slide_mean` function instead.
+#' # Simple sliding means and sums are much faster to do using
+#' # the `epi_slide_mean` and `epi_slide_sum` functions instead.
 #' jhu_csse_daily_subset %>%
 #'   group_by(geo_value) %>%
 #'   epi_slide(cases_7dav = mean(cases), before = 6) %>%
@@ -377,7 +377,8 @@ epi_slide <- function(x, f, ..., before, after, ref_time_values,
 #'
 #' @importFrom dplyr bind_rows mutate %>% arrange tibble select
 #' @importFrom rlang enquo quo_get_expr as_label expr_label caller_arg
-#' @importFrom  purrr map map_lgl
+#' @importFrom tidyselect eval_select
+#' @importFrom purrr map map_lgl
 #' @importFrom data.table frollmean frollsum frollapply
 #' @importFrom lubridate as.period
 #' @importFrom checkmate assert_function
@@ -390,23 +391,23 @@ epi_slide <- function(x, f, ..., before, after, ref_time_values,
 #'   group_by(geo_value) %>%
 #'   epi_slide_opt(
 #'     cases,
-#'     f = data.table::frollmean, new_col_name = "cases_7dav", names_sep = NULL, before = 6
+#'     f = data.table::frollmean, before = 6
 #'   ) %>%
-#'   # Remove a nonessential var. to ensure new col is printed
-#'   dplyr::select(geo_value, time_value, cases, cases_7dav) %>%
+#'   # Remove a nonessential var. to ensure new col is printed, and rename new col
+#'   dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) %>%
 #'   ungroup()
 #'
 #' # slide a 7-day trailing average formula on cases. Adjust `frollmean` settings for speed
 #' # and accuracy, and to allow partially-missing windows.
 #' jhu_csse_daily_subset %>%
 #'   group_by(geo_value) %>%
-#'   epi_slide_opt(cases,
-#'     f = data.table::frollmean,
-#'     new_col_name = "cases_7dav", names_sep = NULL, before = 6,
+#'   epi_slide_opt(
+#'     cases,
+#'     f = data.table::frollmean, before = 6,
 #'     # `frollmean` options
 #'     na.rm = TRUE, algo = "exact", hasNA = TRUE
 #'   ) %>%
-#'   dplyr::select(geo_value, time_value, cases, cases_7dav) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) %>%
 #'   ungroup()
 #'
 #' # slide a 7-day leading average
@@ -414,10 +415,10 @@ epi_slide <- function(x, f, ..., before, after, ref_time_values,
 #'   group_by(geo_value) %>%
 #'   epi_slide_opt(
 #'     cases,
-#'     f = slider::slide_mean, new_col_name = "cases_7dav", names_sep = NULL, after = 6
+#'     f = slider::slide_mean, after = 6
 #'   ) %>%
 #'   # Remove a nonessential var. to ensure new col is printed
-#'   dplyr::select(geo_value, time_value, cases, cases_7dav) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) %>%
 #'   ungroup()
 #'
 #' # slide a 7-day centre-aligned sum. This can also be done with `epi_slide_sum`
@@ -425,15 +426,15 @@ epi_slide <- function(x, f, ..., before, after, ref_time_values,
 #'   group_by(geo_value) %>%
 #'   epi_slide_opt(
 #'     cases,
-#'     f = data.table::frollsum, new_col_name = "cases_7dav", names_sep = NULL, before = 3, after = 3
+#'     f = data.table::frollsum, before = 3, after = 3
 #'   ) %>%
 #'   # Remove a nonessential var. to ensure new col is printed
-#'   dplyr::select(geo_value, time_value, cases, cases_7dav) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) %>%
 #'   ungroup()
 epi_slide_opt <- function(x, col_names, f, ..., before, after, ref_time_values,
                           time_step,
-                          new_col_name = "slide_value", as_list_col = NULL,
-                          names_sep = "_", all_rows = FALSE) {
+                          new_col_name = NULL, as_list_col = NULL,
+                          names_sep = NULL, all_rows = FALSE) {
   assert_class(x, "epi_df")
 
   if (nrow(x) == 0L) {
@@ -443,15 +444,27 @@ epi_slide_opt <- function(x, col_names, f, ..., before, after, ref_time_values,
         "i" = "If this computation is occuring within an `epix_slide` call,
           check that `epix_slide` `ref_time_values` argument was set appropriately"
       ),
-      class = "epiprocess__epi_slide_mean__0_row_input",
+      class = "epiprocess__epi_slide_opt__0_row_input",
       epiprocess__x = x
     )
   }
 
   if (!is.null(as_list_col)) {
     cli_abort(
-      "`as_list_col` is not supported for `epi_slide_mean`",
-      class = "epiproces__epi_slide_mean__list_not_supported"
+      "`as_list_col` is not supported for `epi_slide_[opt/mean/sum]`",
+      class = "epiprocess__epi_slide_opt__list_not_supported"
+    )
+  }
+  if (!is.null(new_col_name)) {
+    cli_abort(
+      "`new_col_name` is not supported for `epi_slide_[opt/mean/sum]`",
+      class = "epiprocess__epi_slide_opt__new_name_not_supported"
+    )
+  }
+  if (!is.null(names_sep)) {
+    cli_abort(
+      "`names_sep` is not supported for `epi_slide_[opt/mean/sum]`",
+      class = "epiprocess__epi_slide_opt__name_sep_not_supported"
     )
   }
 
@@ -543,48 +556,16 @@ epi_slide_opt <- function(x, col_names, f, ..., before, after, ref_time_values,
   # `before` and `after` params.
   window_size <- before + after + 1L
 
-  col_names_quo <- enquo(col_names)
-  col_names_chr <- as.character(rlang::quo_get_expr(col_names_quo))
-  if (startsWith(rlang::as_label(col_names_quo), "c(")) {
-    # List or vector of col names. We need to drop the first element since it
-    # will be either "c" (if built as a vector) or "list" (if built as a
-    # list).
-    col_names_chr <- col_names_chr[-1]
-  } else if (startsWith(rlang::as_label(col_names_quo), "list(")) {
-    cli_abort(
-      "`col_names` must be a single tidy column name or a vector
-      (`c()`) of tidy column names",
-      class = "epiprocess__epi_slide_mean__col_names_in_list",
-      epiprocess__col_names = col_names_chr
-    )
-  }
-  # If single column name, do nothing.
-
-  if (is.null(names_sep)) {
-    if (length(new_col_name) != length(col_names_chr)) {
-      cli_abort(
-        c(
-          "`new_col_name` must be the same length as `col_names` when
-          `names_sep` is NULL to avoid duplicate output column names."
-        ),
-        class = "epiprocess__epi_slide_mean__col_names_length_mismatch",
-        epiprocess__new_col_name = new_col_name,
-        epiprocess__col_names = col_names_chr
-      )
-    }
-    result_col_names <- new_col_name
-  } else {
-    if (length(new_col_name) != 1L && length(new_col_name) != length(col_names_chr)) {
-      cli_abort(
-        "`new_col_name` must be either length 1 or the same length as `col_names`.",
-        class = "epiprocess__epi_slide_mean__col_names_length_mismatch_and_not_one",
-        epiprocess__new_col_name = new_col_name,
-        epiprocess__col_names = col_names_chr
-      )
-    }
-    result_col_names <- paste(new_col_name, col_names_chr, sep = names_sep)
-  }
-
+  # The position of a given column can be differ between input `x` and
+  # `.data_group` since the grouping step by default drops grouping columns.
+  # To avoid rerunning `eval_select` for every `.data_group`, convert
+  # positions of user-provided `col_names` into string column names. We avoid
+  # using `names(pos)` directly for robustness and in case we later want to
+  # allow users to rename fields via tidyselection.
+  pos <- eval_select(rlang::enquo(col_names), data = x, allow_rename = FALSE)
+  col_names_chr <- names(x)[pos]
+  # Always rename results to "slide_value_<original column name>".
+  result_col_names <- paste0("slide_value_", col_names_chr)
   slide_one_grp <- function(.data_group, .group_key, ...) {
     missing_times <- all_dates[!(all_dates %in% .data_group$time_value)]
 
@@ -600,19 +581,19 @@ epi_slide_opt <- function(x, col_names, f, ..., before, after, ref_time_values,
     # If a group contains duplicate time values, `frollmean` will still only
     # use the last `k` obs. It isn't looking at dates, it just goes in row
     # order. So if the computation is aggregating across multiple obs for the
-    # same date, `epi_slide_mean` will produce incorrect results; `epi_slide`
-    # should be used instead.
+    # same date, `epi_slide_opt` and derivates will produce incorrect
+    # results; `epi_slide` should be used instead.
     if (anyDuplicated(.data_group$time_value) != 0L) {
       cli_abort(
         c(
-          "group contains duplicate time values. Using `epi_slide_mean` on this
+          "group contains duplicate time values. Using `epi_slide_[opt/mean/sum]` on this
             group will result in incorrect results",
           "i" = "Please change the grouping structure of the input data so that
             each group has non-duplicate time values (e.g. `x %>% group_by(geo_value)
-            %>% epi_slide_mean`)",
+            %>% epi_slide_opt(f = frollmean)`)",
           "i" = "Use `epi_slide` to aggregate across groups"
         ),
-        class = "epiprocess__epi_slide_mean__duplicate_time_values",
+        class = "epiprocess__epi_slide_opt__duplicate_time_values",
         epiprocess__data_group = .data_group,
         epiprocess__group_key = .group_key
       )
@@ -624,7 +605,7 @@ epi_slide_opt <- function(x, col_names, f, ..., before, after, ref_time_values,
           "i" = c("Input data may contain `time_values` closer together than the
              expected `time_step` size")
         ),
-        class = "epiprocess__epi_slide_mean__unexpected_row_number",
+        class = "epiprocess__epi_slide_opt__unexpected_row_number",
         epiprocess__data_group = .data_group,
         epiprocess__group_key = .group_key
       )
@@ -669,7 +650,7 @@ epi_slide_opt <- function(x, col_names, f, ..., before, after, ref_time_values,
   }
 
   if (!is_epi_df(result)) {
-    # `all_rows`handling strip epi_df format and metadata.
+    # `all_rows`handling strips epi_df format and metadata.
     # Restore them.
     result <- reclass(result, attributes(x)$metadata)
   }
@@ -700,50 +681,51 @@ epi_slide_opt <- function(x, col_names, f, ..., before, after, ref_time_values,
 #' # slide a 7-day trailing average formula on cases
 #' jhu_csse_daily_subset %>%
 #'   group_by(geo_value) %>%
-#'   epi_slide_mean(cases, new_col_name = "cases_7dav", names_sep = NULL, before = 6) %>%
+#'   epi_slide_mean(cases, before = 6) %>%
 #'   # Remove a nonessential var. to ensure new col is printed
-#'   dplyr::select(geo_value, time_value, cases, cases_7dav) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) %>%
 #'   ungroup()
 #'
 #' # slide a 7-day trailing average formula on cases. Adjust `frollmean` settings for speed
 #' # and accuracy, and to allow partially-missing windows.
 #' jhu_csse_daily_subset %>%
 #'   group_by(geo_value) %>%
-#'   epi_slide_mean(cases,
-#'     new_col_name = "cases_7dav", names_sep = NULL, before = 6,
+#'   epi_slide_mean(
+#'     cases,
+#'     before = 6,
 #'     # `frollmean` options
 #'     na.rm = TRUE, algo = "exact", hasNA = TRUE
 #'   ) %>%
-#'   dplyr::select(geo_value, time_value, cases, cases_7dav) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) %>%
 #'   ungroup()
 #'
 #' # slide a 7-day leading average
 #' jhu_csse_daily_subset %>%
 #'   group_by(geo_value) %>%
-#'   epi_slide_mean(cases, new_col_name = "cases_7dav", names_sep = NULL, after = 6) %>%
+#'   epi_slide_mean(cases, after = 6) %>%
 #'   # Remove a nonessential var. to ensure new col is printed
-#'   dplyr::select(geo_value, time_value, cases, cases_7dav) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) %>%
 #'   ungroup()
 #'
 #' # slide a 7-day centre-aligned average
 #' jhu_csse_daily_subset %>%
 #'   group_by(geo_value) %>%
-#'   epi_slide_mean(cases, new_col_name = "cases_7dav", names_sep = NULL, before = 3, after = 3) %>%
+#'   epi_slide_mean(cases, before = 3, after = 3) %>%
 #'   # Remove a nonessential var. to ensure new col is printed
-#'   dplyr::select(geo_value, time_value, cases, cases_7dav) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) %>%
 #'   ungroup()
 #'
 #' # slide a 14-day centre-aligned average
 #' jhu_csse_daily_subset %>%
 #'   group_by(geo_value) %>%
-#'   epi_slide_mean(cases, new_col_name = "cases_14dav", names_sep = NULL, before = 6, after = 7) %>%
+#'   epi_slide_mean(cases, before = 6, after = 7) %>%
 #'   # Remove a nonessential var. to ensure new col is printed
-#'   dplyr::select(geo_value, time_value, cases, cases_14dav) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_14dav = slide_value_cases) %>%
 #'   ungroup()
 epi_slide_mean <- function(x, col_names, ..., before, after, ref_time_values,
                            time_step,
-                           new_col_name = "slide_value", as_list_col = NULL,
-                           names_sep = "_", all_rows = FALSE) {
+                           new_col_name = NULL, as_list_col = NULL,
+                           names_sep = NULL, all_rows = FALSE) {
   epi_slide_opt(
     x = x,
     col_names = {{ col_names }},
@@ -783,14 +765,14 @@ epi_slide_mean <- function(x, col_names, ..., before, after, ref_time_values,
 #' # slide a 7-day trailing sum formula on cases
 #' jhu_csse_daily_subset %>%
 #'   group_by(geo_value) %>%
-#'   epi_slide_sum(cases, new_col_name = "cases_7dsum", names_sep = NULL, before = 6) %>%
+#'   epi_slide_sum(cases, before = 6) %>%
 #'   # Remove a nonessential var. to ensure new col is printed
-#'   dplyr::select(geo_value, time_value, cases, cases_7dsum) %>%
+#'   dplyr::select(geo_value, time_value, cases, cases_7dsum = slide_value_cases) %>%
 #'   ungroup()
 epi_slide_sum <- function(x, col_names, ..., before, after, ref_time_values,
                           time_step,
-                          new_col_name = "slide_value", as_list_col = NULL,
-                          names_sep = "_", all_rows = FALSE) {
+                          new_col_name = NULL, as_list_col = NULL,
+                          names_sep = NULL, all_rows = FALSE) {
   epi_slide_opt(
     x = x,
     col_names = {{ col_names }},
@@ -859,7 +841,7 @@ full_date_seq <- function(x, before, after, time_step) {
           "i" = c("The input data's `time_type` was probably `custom` or `day-time`.
           These require also passing a `time_step` function.")
         ),
-        class = "epiprocess__epi_slide_mean__unmappable_time_type",
+        class = "epiprocess__full_date_seq__unmappable_time_type",
         epiprocess__time_type = ttype
       )
     }
diff --git a/man-roxygen/opt-slide-params.R b/man-roxygen/opt-slide-params.R
index a7d5b04a..d13921b2 100644
--- a/man-roxygen/opt-slide-params.R
+++ b/man-roxygen/opt-slide-params.R
@@ -1,5 +1,13 @@
-#' @param col_names A single tidyselection or a tidyselection vector of the
-#'  names of one or more columns for which to calculate the rolling mean.
+#' @param col_names <[`tidy-select`][dplyr_tidy_select]> An unquoted column
+#'  name(e.g., `cases`), multiple column names (e.g., `c(cases, deaths)`), or
+#'  [other tidy-select expression][tidyselect::language]. Variable names can
+#'  be used as if they were positions in the data frame, so expressions like
+#'  `x:y` can be used to select a range of variables. If you have the desired
+#'  column names stored in a vector `vars`, use `col_names = all_of(vars)`.
+#'
+#'   The tidy-selection renaming interface is not supported, and cannot be used
+#'   to provide output column names; if you want to customize the output column
+#'   names, use [`dplyr::rename`] after the slide.
 #' @param as_list_col Not supported. Included to match `epi_slide` interface.
 #' @param new_col_name Character vector indicating the name(s) of the new
 #'  column(s) that will contain the derivative values. Default
diff --git a/man/epi_slide.Rd b/man/epi_slide.Rd
index 0d0dfb55..a1319f99 100644
--- a/man/epi_slide.Rd
+++ b/man/epi_slide.Rd
@@ -160,8 +160,8 @@ through the \code{new_col_name} argument.
 }
 \examples{
 # slide a 7-day trailing average formula on cases
-# This and other simple sliding means are much faster to do using
-# the `epi_slide_mean` function instead.
+# Simple sliding means and sums are much faster to do using
+# the `epi_slide_mean` and `epi_slide_sum` functions instead.
 jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
   epi_slide(cases_7dav = mean(cases), before = 6) \%>\%
diff --git a/man/epi_slide_mean.Rd b/man/epi_slide_mean.Rd
index ee3e7838..850a45a1 100644
--- a/man/epi_slide_mean.Rd
+++ b/man/epi_slide_mean.Rd
@@ -12,9 +12,9 @@ epi_slide_mean(
   after,
   ref_time_values,
   time_step,
-  new_col_name = "slide_value",
+  new_col_name = NULL,
   as_list_col = NULL,
-  names_sep = "_",
+  names_sep = NULL,
   all_rows = FALSE
 )
 }
@@ -23,8 +23,16 @@ epi_slide_mean(
 or ungrouped. If ungrouped, all data in \code{x} will be treated as part of a
 single data group.}
 
-\item{col_names}{A single tidyselection or a tidyselection vector of the
-names of one or more columns for which to calculate the rolling mean.}
+\item{col_names}{<\code{\link[=dplyr_tidy_select]{tidy-select}}> An unquoted column
+name(e.g., \code{cases}), multiple column names (e.g., \code{c(cases, deaths)}), or
+\link[tidyselect:language]{other tidy-select expression}. Variable names can
+be used as if they were positions in the data frame, so expressions like
+\code{x:y} can be used to select a range of variables. If you have the desired
+column names stored in a vector \code{vars}, use \code{col_names = all_of(vars)}.
+
+The tidy-selection renaming interface is not supported, and cannot be used
+to provide output column names; if you want to customize the output column
+names, use \code{\link[dplyr:rename]{dplyr::rename}} after the slide.}
 
 \item{...}{Additional arguments to pass to \code{data.table::frollmean}, for
 example, \code{na.rm} and \code{algo}. \code{data.table::frollmean} is automatically
@@ -125,45 +133,46 @@ misspelled.)
 # slide a 7-day trailing average formula on cases
 jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
-  epi_slide_mean(cases, new_col_name = "cases_7dav", names_sep = NULL, before = 6) \%>\%
+  epi_slide_mean(cases, before = 6) \%>\%
   # Remove a nonessential var. to ensure new col is printed
-  dplyr::select(geo_value, time_value, cases, cases_7dav) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) \%>\%
   ungroup()
 
 # slide a 7-day trailing average formula on cases. Adjust `frollmean` settings for speed
 # and accuracy, and to allow partially-missing windows.
 jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
-  epi_slide_mean(cases,
-    new_col_name = "cases_7dav", names_sep = NULL, before = 6,
+  epi_slide_mean(
+    cases,
+    before = 6,
     # `frollmean` options
     na.rm = TRUE, algo = "exact", hasNA = TRUE
   ) \%>\%
-  dplyr::select(geo_value, time_value, cases, cases_7dav) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) \%>\%
   ungroup()
 
 # slide a 7-day leading average
 jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
-  epi_slide_mean(cases, new_col_name = "cases_7dav", names_sep = NULL, after = 6) \%>\%
+  epi_slide_mean(cases, after = 6) \%>\%
   # Remove a nonessential var. to ensure new col is printed
-  dplyr::select(geo_value, time_value, cases, cases_7dav) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) \%>\%
   ungroup()
 
 # slide a 7-day centre-aligned average
 jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
-  epi_slide_mean(cases, new_col_name = "cases_7dav", names_sep = NULL, before = 3, after = 3) \%>\%
+  epi_slide_mean(cases, before = 3, after = 3) \%>\%
   # Remove a nonessential var. to ensure new col is printed
-  dplyr::select(geo_value, time_value, cases, cases_7dav) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) \%>\%
   ungroup()
 
 # slide a 14-day centre-aligned average
 jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
-  epi_slide_mean(cases, new_col_name = "cases_14dav", names_sep = NULL, before = 6, after = 7) \%>\%
+  epi_slide_mean(cases, before = 6, after = 7) \%>\%
   # Remove a nonessential var. to ensure new col is printed
-  dplyr::select(geo_value, time_value, cases, cases_14dav) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_14dav = slide_value_cases) \%>\%
   ungroup()
 }
 \seealso{
diff --git a/man/epi_slide_opt.Rd b/man/epi_slide_opt.Rd
index 0772b431..4b011c16 100644
--- a/man/epi_slide_opt.Rd
+++ b/man/epi_slide_opt.Rd
@@ -13,9 +13,9 @@ epi_slide_opt(
   after,
   ref_time_values,
   time_step,
-  new_col_name = "slide_value",
+  new_col_name = NULL,
   as_list_col = NULL,
-  names_sep = "_",
+  names_sep = NULL,
   all_rows = FALSE
 )
 }
@@ -24,8 +24,16 @@ epi_slide_opt(
 or ungrouped. If ungrouped, all data in \code{x} will be treated as part of a
 single data group.}
 
-\item{col_names}{A single tidyselection or a tidyselection vector of the
-names of one or more columns for which to calculate the rolling mean.}
+\item{col_names}{<\code{\link[=dplyr_tidy_select]{tidy-select}}> An unquoted column
+name(e.g., \code{cases}), multiple column names (e.g., \code{c(cases, deaths)}), or
+\link[tidyselect:language]{other tidy-select expression}. Variable names can
+be used as if they were positions in the data frame, so expressions like
+\code{x:y} can be used to select a range of variables. If you have the desired
+column names stored in a vector \code{vars}, use \code{col_names = all_of(vars)}.
+
+The tidy-selection renaming interface is not supported, and cannot be used
+to provide output column names; if you want to customize the output column
+names, use \code{\link[dplyr:rename]{dplyr::rename}} after the slide.}
 
 \item{f}{Function; together with \code{...} specifies the computation to slide.
 \code{f} must be one of \code{data.table}'s rolling functions
@@ -148,23 +156,23 @@ jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
   epi_slide_opt(
     cases,
-    f = data.table::frollmean, new_col_name = "cases_7dav", names_sep = NULL, before = 6
+    f = data.table::frollmean, before = 6
   ) \%>\%
-  # Remove a nonessential var. to ensure new col is printed
-  dplyr::select(geo_value, time_value, cases, cases_7dav) \%>\%
+  # Remove a nonessential var. to ensure new col is printed, and rename new col
+  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) \%>\%
   ungroup()
 
 # slide a 7-day trailing average formula on cases. Adjust `frollmean` settings for speed
 # and accuracy, and to allow partially-missing windows.
 jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
-  epi_slide_opt(cases,
-    f = data.table::frollmean,
-    new_col_name = "cases_7dav", names_sep = NULL, before = 6,
+  epi_slide_opt(
+    cases,
+    f = data.table::frollmean, before = 6,
     # `frollmean` options
     na.rm = TRUE, algo = "exact", hasNA = TRUE
   ) \%>\%
-  dplyr::select(geo_value, time_value, cases, cases_7dav) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) \%>\%
   ungroup()
 
 # slide a 7-day leading average
@@ -172,10 +180,10 @@ jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
   epi_slide_opt(
     cases,
-    f = slider::slide_mean, new_col_name = "cases_7dav", names_sep = NULL, after = 6
+    f = slider::slide_mean, after = 6
   ) \%>\%
   # Remove a nonessential var. to ensure new col is printed
-  dplyr::select(geo_value, time_value, cases, cases_7dav) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) \%>\%
   ungroup()
 
 # slide a 7-day centre-aligned sum. This can also be done with `epi_slide_sum`
@@ -183,10 +191,10 @@ jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
   epi_slide_opt(
     cases,
-    f = data.table::frollsum, new_col_name = "cases_7dav", names_sep = NULL, before = 3, after = 3
+    f = data.table::frollsum, before = 3, after = 3
   ) \%>\%
   # Remove a nonessential var. to ensure new col is printed
-  dplyr::select(geo_value, time_value, cases, cases_7dav) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_7dav = slide_value_cases) \%>\%
   ungroup()
 }
 \seealso{
diff --git a/man/epi_slide_sum.Rd b/man/epi_slide_sum.Rd
index d5961f27..8c835bdb 100644
--- a/man/epi_slide_sum.Rd
+++ b/man/epi_slide_sum.Rd
@@ -12,9 +12,9 @@ epi_slide_sum(
   after,
   ref_time_values,
   time_step,
-  new_col_name = "slide_value",
+  new_col_name = NULL,
   as_list_col = NULL,
-  names_sep = "_",
+  names_sep = NULL,
   all_rows = FALSE
 )
 }
@@ -23,8 +23,16 @@ epi_slide_sum(
 or ungrouped. If ungrouped, all data in \code{x} will be treated as part of a
 single data group.}
 
-\item{col_names}{A single tidyselection or a tidyselection vector of the
-names of one or more columns for which to calculate the rolling mean.}
+\item{col_names}{<\code{\link[=dplyr_tidy_select]{tidy-select}}> An unquoted column
+name(e.g., \code{cases}), multiple column names (e.g., \code{c(cases, deaths)}), or
+\link[tidyselect:language]{other tidy-select expression}. Variable names can
+be used as if they were positions in the data frame, so expressions like
+\code{x:y} can be used to select a range of variables. If you have the desired
+column names stored in a vector \code{vars}, use \code{col_names = all_of(vars)}.
+
+The tidy-selection renaming interface is not supported, and cannot be used
+to provide output column names; if you want to customize the output column
+names, use \code{\link[dplyr:rename]{dplyr::rename}} after the slide.}
 
 \item{...}{Additional arguments to pass to \code{data.table::frollsum}, for
 example, \code{na.rm} and \code{algo}. \code{data.table::frollsum} is automatically
@@ -125,9 +133,9 @@ misspelled.)
 # slide a 7-day trailing sum formula on cases
 jhu_csse_daily_subset \%>\%
   group_by(geo_value) \%>\%
-  epi_slide_sum(cases, new_col_name = "cases_7dsum", names_sep = NULL, before = 6) \%>\%
+  epi_slide_sum(cases, before = 6) \%>\%
   # Remove a nonessential var. to ensure new col is printed
-  dplyr::select(geo_value, time_value, cases, cases_7dsum) \%>\%
+  dplyr::select(geo_value, time_value, cases, cases_7dsum = slide_value_cases) \%>\%
   ungroup()
 }
 \seealso{
diff --git a/tests/testthat/test-epi_slide.R b/tests/testthat/test-epi_slide.R
index 6d66e0c4..8765d50c 100644
--- a/tests/testthat/test-epi_slide.R
+++ b/tests/testthat/test-epi_slide.R
@@ -29,7 +29,7 @@ toy_edf <- tibble::tribble(
   as_epi_df(as_of = 100)
 
 # nolint start: line_length_linter.
-basic_result_from_size1_sum <- tibble::tribble(
+basic_sum_result <- tibble::tribble(
   ~geo_value, ~time_value, ~value, ~slide_value,
   "a", 1:10, 2L^(1:10), data.table::frollsum(2L^(1:10) + 2L^(11:20), c(1:7, rep(7L, 3L)), adaptive = TRUE, na.rm = TRUE),
   "b", 1:10, 2L^(11:20), data.table::frollsum(2L^(1:10) + 2L^(11:20), c(1:7, rep(7L, 3L)), adaptive = TRUE, na.rm = TRUE),
@@ -38,7 +38,7 @@ basic_result_from_size1_sum <- tibble::tribble(
   dplyr::arrange(time_value) %>%
   as_epi_df(as_of = 100)
 
-basic_result_from_size1_mean <- tibble::tribble(
+basic_mean_result <- tibble::tribble(
   ~geo_value, ~time_value, ~value, ~slide_value,
   "a", 1:10, 2L^(1:10), data.table::frollmean(2L^(1:10), c(1:7, rep(7L, 3L)), adaptive = TRUE, na.rm = TRUE),
 ) %>%
@@ -315,27 +315,29 @@ test_that(
 )
 
 test_that("computation output formats x as_list_col", {
-  # See `toy_edf` and `basic_result_from_size1_sum` definitions at top of file.
+  # See `toy_edf` and `basic_sum_result` definitions at top of file.
   # We'll try 7d sum with a few formats.
   expect_identical(
     toy_edf %>% epi_slide(before = 6L, ~ sum(.x$value)),
-    basic_result_from_size1_sum
+    basic_sum_result
   )
   expect_identical(
     toy_edf %>% epi_slide(before = 6L, ~ sum(.x$value), as_list_col = TRUE),
-    basic_result_from_size1_sum %>% dplyr::mutate(slide_value = as.list(slide_value))
+    basic_sum_result %>% dplyr::mutate(slide_value = as.list(slide_value))
   )
   expect_identical(
     toy_edf %>% epi_slide(before = 6L, ~ data.frame(value = sum(.x$value))),
-    basic_result_from_size1_sum %>% rename(slide_value_value = slide_value)
+    basic_sum_result %>% rename(slide_value_value = slide_value)
   )
   expect_identical(
     toy_edf %>% epi_slide(before = 6L, ~ data.frame(value = sum(.x$value)), as_list_col = TRUE),
-    basic_result_from_size1_sum %>%
+    basic_sum_result %>%
       mutate(slide_value = purrr::map(slide_value, ~ data.frame(value = .x)))
   )
+})
 
-  # See `toy_edf` and `basic_result_from_size1_mean` definitions at top of file.
+test_that("epi_slide_mean errors when `as_list_col` non-NULL", {
+  # See `toy_edf` and `basic_mean_result` definitions at top of file.
   # We'll try 7d avg with a few formats.
   # Warning: not exactly the same naming behavior as `epi_slide`.
   expect_identical(
@@ -347,7 +349,7 @@ test_that("computation output formats x as_list_col", {
         value,
         before = 6L, na.rm = TRUE
       ),
-    basic_result_from_size1_mean %>% dplyr::mutate(
+    basic_mean_result %>% dplyr::mutate(
       slide_value_value = slide_value
     ) %>%
       select(-slide_value)
@@ -361,7 +363,7 @@ test_that("computation output formats x as_list_col", {
         value,
         before = 6L, as_list_col = TRUE, na.rm = TRUE
       ),
-    class = "epiproces__epi_slide_mean__list_not_supported"
+    class = "epiprocess__epi_slide_opt__list_not_supported"
   )
   # `epi_slide_mean` doesn't return dataframe columns
 })
@@ -373,7 +375,7 @@ test_that("nested dataframe output names are controllable", {
         before = 6L, ~ data.frame(value = sum(.x$value)),
         new_col_name = "result"
       ),
-    basic_result_from_size1_sum %>% rename(result_value = slide_value)
+    basic_sum_result %>% rename(result_value = slide_value)
   )
   expect_identical(
     toy_edf %>%
@@ -381,17 +383,7 @@ test_that("nested dataframe output names are controllable", {
         before = 6L, ~ data.frame(value_sum = sum(.x$value)),
         names_sep = NULL
       ),
-    basic_result_from_size1_sum %>% rename(value_sum = slide_value)
-  )
-  expect_identical(
-    toy_edf %>% filter(
-      geo_value == "a"
-    ) %>%
-      epi_slide_mean(
-        value,
-        before = 6L, names_sep = NULL, na.rm = TRUE
-      ),
-    basic_result_from_size1_mean
+    basic_sum_result %>% rename(value_sum = slide_value)
   )
 })
 
@@ -482,7 +474,8 @@ test_that("`ref_time_values` + `all_rows = TRUE` works", {
         value,
         before = 6L, names_sep = NULL, na.rm = TRUE
       ),
-    basic_result_from_size1_mean
+    basic_mean_result %>%
+      rename(slide_value_value = slide_value)
   )
   expect_identical(
     toy_edf %>% filter(
@@ -493,7 +486,8 @@ test_that("`ref_time_values` + `all_rows = TRUE` works", {
         before = 6L, ref_time_values = c(2L, 8L),
         names_sep = NULL, na.rm = TRUE
       ),
-    filter(basic_result_from_size1_mean, time_value %in% c(2L, 8L))
+    filter(basic_mean_result, time_value %in% c(2L, 8L)) %>%
+      rename(slide_value_value = slide_value)
   )
   expect_identical(
     toy_edf %>% filter(
@@ -504,10 +498,11 @@ test_that("`ref_time_values` + `all_rows = TRUE` works", {
         before = 6L, ref_time_values = c(2L, 8L), all_rows = TRUE,
         names_sep = NULL, na.rm = TRUE
       ),
-    basic_result_from_size1_mean %>%
-      dplyr::mutate(slide_value = dplyr::if_else(time_value %in% c(2L, 8L),
+    basic_mean_result %>%
+      dplyr::mutate(slide_value_value = dplyr::if_else(time_value %in% c(2L, 8L),
         slide_value, NA_integer_
-      ))
+      )) %>%
+      select(-slide_value)
   )
 
   # slide computations returning data frames:
@@ -662,7 +657,7 @@ test_that("basic grouped epi_slide_mean computation produces expected output", {
     as_epi_df(as_of = d + 6)
 
   result1 <- epi_slide_mean(small_x, value, before = 50, names_sep = NULL, na.rm = TRUE)
-  expect_identical(result1, expected_output)
+  expect_identical(result1, expected_output %>% rename(slide_value_value = slide_value))
 })
 
 test_that("ungrouped epi_slide computation completes successfully", {
@@ -722,14 +717,14 @@ test_that("basic ungrouped epi_slide_mean computation produces expected output",
     ungroup() %>%
     filter(geo_value == "ak") %>%
     epi_slide_mean(value, before = 50, names_sep = NULL, na.rm = TRUE)
-  expect_identical(result1, expected_output)
+  expect_identical(result1, expected_output %>% rename(slide_value_value = slide_value))
 
   # Ungrouped with multiple geos
   # epi_slide_mean fails when input data groups contain duplicate time_values,
   # e.g. aggregating across geos
   expect_error(
     small_x %>% ungroup() %>% epi_slide_mean(value, before = 6L),
-    class = "epiprocess__epi_slide_mean__duplicate_time_values"
+    class = "epiprocess__epi_slide_opt__duplicate_time_values"
   )
 })
 
@@ -928,7 +923,7 @@ test_that("basic slide behavior is correct when groups have non-overlapping date
   expect_identical(result1, expected_output)
 
   result2 <- epi_slide_mean(small_x_misaligned_dates, value, before = 50, names_sep = NULL, na.rm = TRUE)
-  expect_identical(result2, expected_output)
+  expect_identical(result2, expected_output %>% rename(slide_value_value = slide_value))
 })
 
 
@@ -1152,7 +1147,7 @@ test_that("special time_types without time_step fail in epi_slide_mean", {
         col_names = a,
         before = before, after = after
       ),
-      class = "epiprocess__epi_slide_mean__unmappable_time_type"
+      class = "epiprocess__full_date_seq__unmappable_time_type"
     )
   }
 
@@ -1376,14 +1371,7 @@ test_that("`epi_slide_mean` errors when passed `time_values` with closer than ex
     as_epi_df()
   expect_error(
     epi_slide_mean(time_df, value, before = 6L, time_step = lubridate::seconds),
-    class = "epiprocess__epi_slide_mean__unexpected_row_number"
-  )
-})
-
-test_that("`epi_slide_mean` errors when passed `col_names` as list", {
-  expect_error(
-    epi_slide_mean(grouped, col_names = list(value), before = 1L, after = 0L, ref_time_values = d + 1),
-    class = "epiprocess__epi_slide_mean__col_names_in_list"
+    class = "epiprocess__epi_slide_opt__unexpected_row_number"
   )
 })