diff --git a/DESCRIPTION b/DESCRIPTION
index c16aff98e..3e8db4117 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.0.17
+Version: 0.0.18
 Authors@R: c(
     person("Daniel", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
@@ -35,12 +35,10 @@ Imports:
     ggplot2,
     glue,
     hardhat (>= 1.3.0),
-    lubridate,
     magrittr,
     quantreg,
     recipes (>= 1.0.4),
     rlang (>= 1.0.0),
-    slider,
     smoothqr,
     stats,
     tibble,
@@ -55,6 +53,7 @@ Suggests:
     epidatr (>= 1.0.0),
     fs,
     knitr,
+    lubridate,
     poissonreg,
     purrr,
     ranger,
diff --git a/NEWS.md b/NEWS.md
index 4cb3e092a..d02e246f0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -52,4 +52,4 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   `...` args intended for `predict.model_fit()`
 - `bake.epi_recipe()` will now re-infer the geo and time type in case baking the
   steps has changed the appropriate values
-- Add a step to produce generic sliding computations over an `epi_df`
+- Add `step_epi_slide` to produce generic sliding computations over an `epi_df`
diff --git a/R/step_epi_slide.R b/R/step_epi_slide.R
index 25ef3973c..637d31a54 100644
--- a/R/step_epi_slide.R
+++ b/R/step_epi_slide.R
@@ -4,7 +4,6 @@
 #'   that will generate one or more new columns of derived data by "sliding"
 #'   a computation along existing data.
 #'
-#'
 #' @inheritParams step_epi_lag
 #' @param .f A function in one of the following formats:
 #'  1. An unquoted function name with no arguments, e.g., `mean`
@@ -20,27 +19,15 @@
 #'  argument must be named `.x`. A common, though very difficult to debug
 #'  error is using something like `function(x) mean`. This will not work
 #'  because it returns the function mean, rather than `mean(x)`
+#' @param before,after the size of the sliding window on the left and the right
+#'  of the center. Usually non-negative integers for data indexed by date, but
+#'  more restrictive in other cases (see [epiprocess::epi_slide()] for details).
+#' @param prefix A character string that will be prefixed to the new column.
 #' @param f_name a character string of at most 20 characters that describes
 #'   the function. This will be combined with `prefix` and the columns in `...`
 #'   to name the result using `{prefix}{f_name}_{column}`. By default it will be determined
 #'   automatically using `clean_f_name()`.
-#' @param before,after non-negative integers.
-#'   How far `before` and `after` each `time_value` should
-#'   the sliding window extend? Any value provided for either
-#'   argument must be a single, non-`NA`, non-negative,
-#'   [integer-compatible][vctrs::vec_cast] number of time steps. Endpoints of
-#'   the window are inclusive. Common settings:
-#'   * For trailing/right-aligned windows from `time_value - time_step(k)` to
-#'   `time_value`, use `before=k, after=0`. This is the most likely use case
-#'   for the purposes of forecasting.
-#'   * For center-aligned windows from `time_value - time_step(k)` to
-#'   `time_value + time_step(k)`, use `before=k, after=k`.
-#'   * For leading/left-aligned windows from `time_value` to
-#'   `time_value + time_step(k)`, use `after=k, after=0`.
 #'
-#'   You may also pass a [lubridate::period], like `lubridate::weeks(1)` or a
-#'   character string that is coercible to a [lubridate::period], like
-#'   `"2 weeks"`.
 #' @template step-return
 #'
 #' @export
@@ -69,9 +56,8 @@ step_epi_slide <-
       rlang::abort("This recipe step can only operate on an `epi_recipe`.")
     }
     .f <- validate_slide_fun(.f)
-    arg_is_scalar(before, after)
-    before <- try_period(before)
-    after <- try_period(after)
+    epiprocess:::validate_slide_window_arg(before, attributes(recipe$template)$metadata$time_type)
+    epiprocess:::validate_slide_window_arg(after, attributes(recipe$template)$metadata$time_type)
     arg_is_chr_scalar(role, prefix, id)
     arg_is_lgl_scalar(skip)
 
@@ -126,7 +112,6 @@ step_epi_slide_new <-
   }
 
 
-
 #' @export
 prep.step_epi_slide <- function(x, training, info = NULL, ...) {
   col_names <- recipes::recipes_eval_select(x$terms, data = training, info = info)
@@ -150,7 +135,6 @@ prep.step_epi_slide <- function(x, training, info = NULL, ...) {
 }
 
 
-
 #' @export
 bake.step_epi_slide <- function(object, new_data, ...) {
   recipes::check_new_data(names(object$columns), object, new_data)
@@ -170,12 +154,16 @@ bake.step_epi_slide <- function(object, new_data, ...) {
       class = "epipredict__step__name_collision_error"
     )
   }
-  if (any(vapply(c(mean, sum), \(x) identical(x, object$.f), logical(1L)))) {
-    cli_warn(
-      c("There is an optimized version of both mean and sum. See `step_epi_slide_mean`, `step_epi_slide_sum`, or `step_epi_slide_opt`."),
-      class = "epipredict__step_epi_slide__optimized_version"
-    )
-  }
+  # TODO: Uncomment this whenever we make the optimized versions available.
+  # if (any(vapply(c(mean, sum), \(x) identical(x, object$.f), logical(1L)))) {
+  #   cli_warn(
+  #     c(
+  #       "There is an optimized version of both mean and sum. See `step_epi_slide_mean`, `step_epi_slide_sum`,
+  #      or `step_epi_slide_opt`."
+  #     ),
+  #     class = "epipredict__step_epi_slide__optimized_version"
+  #   )
+  # }
   epi_slide_wrapper(
     new_data,
     object$before,
@@ -187,48 +175,51 @@ bake.step_epi_slide <- function(object, new_data, ...) {
     object$prefix
   )
 }
-#' wrapper to handle epi_slide particulars
+
+
+#' Wrapper to handle epi_slide particulars
+#'
 #' @description
 #' This should simplify somewhat in the future when we can run `epi_slide` on
 #'   columns. Surprisingly, lapply is several orders of magnitude faster than
 #'   using roughly equivalent tidy select style.
+#'
 #' @param fns vector of functions, even if it's length 1.
 #' @param group_keys the keys to group by. likely `epi_keys[-1]` (to remove time_value)
+#'
 #' @importFrom tidyr crossing
 #' @importFrom dplyr bind_cols group_by ungroup
 #' @importFrom epiprocess epi_slide
 #' @keywords internal
 epi_slide_wrapper <- function(new_data, before, after, columns, fns, fn_names, group_keys, name_prefix) {
   cols_fns <- tidyr::crossing(col_name = columns, fn_name = fn_names, fn = fns)
+  # Iterate over the rows of cols_fns. For each row number, we will output a
+  # transformed column. The first result returns all the original columns along
+  # with the new column. The rest just return the new column.
   seq_len(nrow(cols_fns)) %>%
-    lapply( # iterate over the rows of cols_fns
-      # takes in the row number, outputs the transformed column
-      function(comp_i) {
-        # extract values from the row
-        col_name <- cols_fns[[comp_i, "col_name"]]
-        fn_name <- cols_fns[[comp_i, "fn_name"]]
-        fn <- cols_fns[[comp_i, "fn"]][[1L]]
-        result_name <- paste(name_prefix, fn_name, col_name, sep = "_")
-        result <- new_data %>%
-          group_by(across(all_of(group_keys))) %>%
-          epi_slide(
-            before = before,
-            after = after,
-            new_col_name = result_name,
-            f = function(slice, geo_key, ref_time_value) {
-              fn(slice[[col_name]])
-            }
-          ) %>%
-          ungroup()
-        # the first result needs to include all of the original columns
-        if (comp_i == 1L) {
-          result
-        } else {
-          # everything else just needs that  column transformed
-          result[result_name]
-        }
+    lapply(function(comp_i) {
+      col_name <- cols_fns[[comp_i, "col_name"]]
+      fn_name <- cols_fns[[comp_i, "fn_name"]]
+      fn <- cols_fns[[comp_i, "fn"]][[1L]]
+      result_name <- paste(name_prefix, fn_name, col_name, sep = "_")
+      result <- new_data %>%
+        group_by(across(all_of(group_keys))) %>%
+        epi_slide(
+          before = before,
+          after = after,
+          new_col_name = result_name,
+          f = function(slice, geo_key, ref_time_value) {
+            fn(slice[[col_name]])
+          }
+        ) %>%
+        ungroup()
+
+      if (comp_i == 1L) {
+        result
+      } else {
+        result[result_name]
       }
-    ) %>%
+    }) %>%
     bind_cols()
 }
 
@@ -286,9 +277,7 @@ validate_slide_fun <- function(.f) {
     cli_abort("In, `step_epi_slide()`, `.f` may not be missing.")
   }
   if (rlang::is_formula(.f, scoped = TRUE)) {
-    if (!is.null(rlang::f_lhs(.f))) {
-      cli_abort("In, `step_epi_slide()`, `.f` must be a one-sided formula.")
-    }
+    cli_abort("In, `step_epi_slide()`, `.f` cannot be a formula.")
   } else if (rlang::is_character(.f)) {
     .f <- rlang::as_function(.f)
   } else if (!rlang::is_function(.f)) {
@@ -296,23 +285,3 @@ validate_slide_fun <- function(.f) {
   }
   .f
 }
-
-try_period <- function(x) {
-  err <- is.na(x)
-  if (!err) {
-    if (is.numeric(x)) {
-      err <- !rlang::is_integerish(x) || x < 0
-    } else {
-      x <- lubridate::as.period(x)
-      err <- is.na(x)
-    }
-  }
-  if (err) {
-    cli_abort(paste(
-      "The value supplied to `before` or `after` must be a non-negative integer",
-      "a {.cls lubridate::period} or a character scalar that can be coerced",
-      'as a {.cls lubridate::period}, e.g., `"1 week"`.'
-    ), )
-  }
-  x
-}
diff --git a/man/add_epi_recipe.Rd b/man/add_epi_recipe.Rd
index 10c282209..0da2d55b3 100644
--- a/man/add_epi_recipe.Rd
+++ b/man/add_epi_recipe.Rd
@@ -35,7 +35,7 @@ Add an \code{epi_recipe} to a workflow
 \details{
 \code{add_epi_recipe} has the same behaviour as
 \code{\link[workflows:add_recipe]{workflows::add_recipe()}} but sets a different
-default blueprint to automatically handle \link[epiprocess:as_epi_df]{epiprocess::epi_df} data.
+default blueprint to automatically handle \link[epiprocess:epi_df]{epiprocess::epi_df} data.
 }
 \examples{
 library(dplyr)
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index eb36f0872..350352ae9 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -44,7 +44,7 @@ workflow
 }
 \description{
 This is an autoregressive classification model for
-\link[epiprocess:as_epi_df]{epiprocess::epi_df} data. It does "direct" forecasting, meaning
+\link[epiprocess:epi_df]{epiprocess::epi_df} data. It does "direct" forecasting, meaning
 that it estimates a class at a particular target horizon.
 }
 \examples{
diff --git a/man/arx_forecaster.Rd b/man/arx_forecaster.Rd
index 2395f5659..af05c0682 100644
--- a/man/arx_forecaster.Rd
+++ b/man/arx_forecaster.Rd
@@ -37,7 +37,7 @@ workflow
 }
 \description{
 This is an autoregressive forecasting model for
-\link[epiprocess:as_epi_df]{epiprocess::epi_df} data. It does "direct" forecasting, meaning
+\link[epiprocess:epi_df]{epiprocess::epi_df} data. It does "direct" forecasting, meaning
 that it estimates a model for a particular target horizon.
 }
 \examples{
diff --git a/man/cdc_baseline_forecaster.Rd b/man/cdc_baseline_forecaster.Rd
index db7bbc41c..cd3c4ed67 100644
--- a/man/cdc_baseline_forecaster.Rd
+++ b/man/cdc_baseline_forecaster.Rd
@@ -11,7 +11,7 @@ cdc_baseline_forecaster(
 )
 }
 \arguments{
-\item{epi_data}{An \code{\link[epiprocess:as_epi_df]{epiprocess::epi_df}}}
+\item{epi_data}{An \code{\link[epiprocess:epi_df]{epiprocess::epi_df}}}
 
 \item{outcome}{A scalar character for the column name we wish to predict.}
 
@@ -24,7 +24,7 @@ horizons) for each unique combination of \code{key_vars}.
 }
 \description{
 This is a simple forecasting model for
-\link[epiprocess:as_epi_df]{epiprocess::epi_df} data. It uses the most recent observation as the
+\link[epiprocess:epi_df]{epiprocess::epi_df} data. It uses the most recent observation as the
 forecast for any future date, and produces intervals by shuffling the quantiles
 of the residuals of such a "flatline" forecast and incrementing these
 forward over all available training data.
diff --git a/man/epi_slide_wrapper.Rd b/man/epi_slide_wrapper.Rd
index e1afbf599..583d2eacf 100644
--- a/man/epi_slide_wrapper.Rd
+++ b/man/epi_slide_wrapper.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/step_epi_slide.R
 \name{epi_slide_wrapper}
 \alias{epi_slide_wrapper}
-\title{wrapper to handle epi_slide particulars}
+\title{Wrapper to handle epi_slide particulars}
 \usage{
 epi_slide_wrapper(
   new_data,
diff --git a/man/flatline_forecaster.Rd b/man/flatline_forecaster.Rd
index 9c37b4943..052dd6428 100644
--- a/man/flatline_forecaster.Rd
+++ b/man/flatline_forecaster.Rd
@@ -7,7 +7,7 @@
 flatline_forecaster(epi_data, outcome, args_list = flatline_args_list())
 }
 \arguments{
-\item{epi_data}{An \link[epiprocess:as_epi_df]{epiprocess::epi_df}}
+\item{epi_data}{An \link[epiprocess:epi_df]{epiprocess::epi_df}}
 
 \item{outcome}{A scalar character for the column name we wish to predict.}
 
@@ -20,7 +20,7 @@ ahead (unique horizon) for each unique combination of \code{key_vars}.
 }
 \description{
 This is a simple forecasting model for
-\link[epiprocess:as_epi_df]{epiprocess::epi_df} data. It uses the most recent observation as the
+\link[epiprocess:epi_df]{epiprocess::epi_df} data. It uses the most recent observation as the
 forcast for any future date, and produces intervals based on the quantiles
 of the residuals of such a "flatline" forecast over all available training
 data.
diff --git a/man/get_test_data.Rd b/man/get_test_data.Rd
index a88dcc4c7..b18685d89 100644
--- a/man/get_test_data.Rd
+++ b/man/get_test_data.Rd
@@ -37,7 +37,7 @@ keys, as well other variables in the original dataset.
 }
 \description{
 Based on the longest lag period in the recipe,
-\code{get_test_data()} creates an \link[epiprocess:as_epi_df]{epi_df}
+\code{get_test_data()} creates an \link[epiprocess:epi_df]{epi_df}
 with columns \code{geo_value}, \code{time_value}
 and other variables in the original dataset,
 which will be used to create features necessary to produce forecasts.
diff --git a/man/grad_employ_subset.Rd b/man/grad_employ_subset.Rd
index 8e7a230f7..46ba36913 100644
--- a/man/grad_employ_subset.Rd
+++ b/man/grad_employ_subset.Rd
@@ -5,7 +5,7 @@
 \alias{grad_employ_subset}
 \title{Subset of Statistics Canada median employment income for postsecondary graduates}
 \format{
-An \link[epiprocess:as_epi_df]{epiprocess::epi_df} with 10193 rows and 8 variables:
+An \link[epiprocess:epi_df]{epiprocess::epi_df} with 10193 rows and 8 variables:
 \describe{
 \item{geo_value}{The province in Canada associated with each
 row of measurements.}
diff --git a/man/predict-epi_workflow.Rd b/man/predict-epi_workflow.Rd
index 2ecdf0102..130279249 100644
--- a/man/predict-epi_workflow.Rd
+++ b/man/predict-epi_workflow.Rd
@@ -60,7 +60,7 @@ workflow was created and fit. This is accomplished using
 \code{\link[recipes:bake]{recipes::bake()}} if a recipe was supplied.
 \item Call \code{\link[parsnip:predict.model_fit]{parsnip::predict.model_fit()}} for you using the underlying fit
 parsnip model.
-\item Ensure that the returned object is an \link[epiprocess:as_epi_df]{epiprocess::epi_df} where
+\item Ensure that the returned object is an \link[epiprocess:epi_df]{epiprocess::epi_df} where
 possible. Specifically, the output will have \code{time_value} and
 \code{geo_value} columns as well as the prediction.
 }
diff --git a/man/step_epi_slide.Rd b/man/step_epi_slide.Rd
index fb83942ae..46bb386ad 100644
--- a/man/step_epi_slide.Rd
+++ b/man/step_epi_slide.Rd
@@ -41,30 +41,14 @@ argument must be named \code{.x}. A common, though very difficult to debug
 error is using something like \code{function(x) mean}. This will not work
 because it returns the function mean, rather than \code{mean(x)}}
 
-\item{before, after}{non-negative integers.
-How far \code{before} and \code{after} each \code{time_value} should
-the sliding window extend? Any value provided for either
-argument must be a single, non-\code{NA}, non-negative,
-\link[vctrs:vec_cast]{integer-compatible} number of time steps. Endpoints of
-the window are inclusive. Common settings:
-\itemize{
-\item For trailing/right-aligned windows from \code{time_value - time_step(k)} to
-\code{time_value}, use \verb{before=k, after=0}. This is the most likely use case
-for the purposes of forecasting.
-\item For center-aligned windows from \code{time_value - time_step(k)} to
-\code{time_value + time_step(k)}, use \verb{before=k, after=k}.
-\item For leading/left-aligned windows from \code{time_value} to
-\code{time_value + time_step(k)}, use \verb{after=k, after=0}.
-}
-
-You may also pass a \link[lubridate:period]{lubridate::period}, like \code{lubridate::weeks(1)} or a
-character string that is coercible to a \link[lubridate:period]{lubridate::period}, like
-\code{"2 weeks"}.}
+\item{before, after}{the size of the sliding window on the left and the right
+of the center. Usually non-negative integers for data indexed by date, but
+more restrictive in other cases (see \code{\link[epiprocess:epi_slide]{epiprocess::epi_slide()}} for details).}
 
 \item{role}{For model terms created by this step, what analysis role should
 they be assigned? \code{lag} is default a predictor while \code{ahead} is an outcome.}
 
-\item{prefix}{A prefix to indicate what type of variable this is}
+\item{prefix}{A character string that will be prefixed to the new column.}
 
 \item{f_name}{a character string of at most 20 characters that describes
 the function. This will be combined with \code{prefix} and the columns in \code{...}
diff --git a/tests/testthat/test-step_epi_slide.R b/tests/testthat/test-step_epi_slide.R
index 1f284b721..29e046eae 100644
--- a/tests/testthat/test-step_epi_slide.R
+++ b/tests/testthat/test-step_epi_slide.R
@@ -1,3 +1,5 @@
+library(dplyr)
+
 tt <- seq(as.Date("2022-01-01"), by = "1 day", length.out = 20)
 edf <- data.frame(
   time_value = c(tt, tt),
@@ -7,17 +9,14 @@ edf <- data.frame(
   as_epi_df()
 
 r <- epi_recipe(edf)
-
-test_that("try_period works", {
-  expect_error(try_period("1 jeff"))
-  expect_error(try_period(lubridate::period("1 jeff")))
-  expect_error(try_period(NA))
-  expect_error(try_period(1.5))
-  res <- lubridate::weeks(1)
-  expect_identical(try_period("1 week"), res)
-  expect_identical(try_period(lubridate::period("1 week")), res)
-  expect_identical(try_period(1L), 1L)
-})
+rolled_before <- edf %>%
+  group_by(geo_value) %>%
+  epi_slide(value = mean(value), before = 3L) %>%
+  pull(value)
+rolled_after <- edf %>%
+  group_by(geo_value) %>%
+  epi_slide(value = mean(value), after = 3L) %>%
+  pull(value)
 
 
 test_that("epi_slide errors when needed", {
@@ -43,92 +42,21 @@ test_that("epi_slide errors when needed", {
   expect_error(r %>% step_epi_slide(value, .f = 1))
 })
 
-library(dplyr)
-rolled_before <- edf %>%
-  group_by(geo_value) %>%
-  epi_slide(value = mean(value), before = 3L) %>%
-  pull(value)
-rolled_after <- edf %>%
-  group_by(geo_value) %>%
-  epi_slide(value = mean(value), after = 3L) %>%
-  pull(value)
-
-
-test_that("epi_slide handles classed before/after", {
-  expect_warning(
-    baseline <- r %>%
-      step_epi_slide(value, .f = mean, before = 3L) %>%
-      prep(edf) %>%
-      bake(new_data = NULL),
-    regexp = "There is an optimized version"
-  )
-  expect_equal(baseline[[4]], rolled_before)
-
-  expect_warning(
-    pbefore <- r %>%
-      step_epi_slide(value, .f = mean, before = lubridate::period("3 days")) %>%
-      prep(edf) %>%
-      bake(new_data = NULL),
-    regexp = "There is an optimized version"
-  )
-  expect_warning(
-    cbefore <- r %>%
-      step_epi_slide(value, .f = mean, before = "3 days") %>%
-      prep(edf) %>%
-      bake(new_data = NULL),
-    regexp = "There is an optimized version"
-  )
-  expect_equal(baseline, pbefore)
-  expect_equal(baseline, cbefore)
-
-  expect_warning(
-    baseline <- r %>%
-      step_epi_slide(value, .f = mean, after = 3L) %>%
-      prep(edf) %>%
-      bake(new_data = NULL),
-    regexp = "There is an optimized version"
-  )
-  expect_equal(baseline[[4]], rolled_after)
-  expect_warning(
-    pafter <- r %>%
-      step_epi_slide(value, .f = mean, after = lubridate::period("3 days")) %>%
-      prep(edf) %>%
-      bake(new_data = NULL),
-    regexp = "There is an optimized version"
-  )
-  expect_warning(
-    cafter <- r %>%
-      step_epi_slide(value, .f = mean, after = "3 days") %>%
-      prep(edf) %>%
-      bake(new_data = NULL),
-    regexp = "There is an optimized version"
-  )
-  expect_equal(baseline, pafter)
-  expect_equal(baseline, cafter)
-})
-
 
 test_that("epi_slide handles different function specs", {
-  expect_warning(
-    cfun <- r %>%
-      step_epi_slide(value, .f = "mean", before = 3L) %>%
-      prep(edf) %>%
-      bake(new_data = NULL),
-    regexp = "There is an optimized version"
-  )
-  expect_warning(
-    ffun <- r %>%
-      step_epi_slide(value, .f = mean, before = 3L) %>%
-      prep(edf) %>%
-      bake(new_data = NULL),
-    regexp = "There is an optimized version"
-  )
+  cfun <- r %>%
+    step_epi_slide(value, .f = "mean", before = 3L) %>%
+    prep(edf) %>%
+    bake(new_data = NULL)
+  ffun <- r %>%
+    step_epi_slide(value, .f = mean, before = 3L) %>%
+    prep(edf) %>%
+    bake(new_data = NULL)
   # formula NOT currently supported
   expect_error(
     lfun <- r %>%
-      step_epi_slide(value, .f = ~ mean(.x, na.rm = TRUE), before = 3L) %>%
-      prep(edf) %>%
-      bake(new_data = NULL)
+      step_epi_slide(value, .f = ~ mean(.x, na.rm = TRUE), before = 3L),
+    regexp = "cannot be a formula."
   )
   blfun <- r %>%
     step_epi_slide(value, .f = function(x) mean(x, na.rm = TRUE), before = 3L) %>%