diff --git a/DESCRIPTION b/DESCRIPTION
index 593f1038..1994f2cc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.9
+Version: 0.1.10
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
@@ -25,7 +25,7 @@ URL: https://github.com/cmu-delphi/epipredict/,
 BugReports: https://github.com/cmu-delphi/epipredict/issues/
 Depends:
     epidatasets,
-    epiprocess (>= 0.9.0),
+    epiprocess (>= 0.10.4),
     parsnip (>= 1.0.0),
     R (>= 3.5.0)
 Imports:
@@ -73,7 +73,6 @@ Remotes:
     cmu-delphi/epidatasets,
     cmu-delphi/epidatr,
     cmu-delphi/epiprocess,
-    cmu-delphi/epidatasets,
     dajmcdon/smoothqr
 Config/Needs/website: cmu-delphi/delphidocs
 Config/testthat/edition: 3
diff --git a/NEWS.md b/NEWS.md
index 6b98d4d5..28f5bb99 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -11,6 +11,9 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   `data(<dataset name>)`, but can be accessed with
   `data(<dataset name>, package = "epidatasets")`, `epidatasets::<dataset name>`
   or, after loading the package, the name of the dataset alone (#382).
+- Addresses upstream breaking changes from cmu-delphi/epiprocess#595 (`growth_rate()`). 
+  `step_growth_rate()` has lost its `additional_gr_args_list` argument and now
+  has an `na_rm` argument.
 
 ## Improvements
 
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 240bc69e..1c4f35ef 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -27,8 +27,9 @@
 #'
 #' @examples
 #' library(dplyr)
+#' tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 #' jhu <- covid_case_death_rates %>%
-#'   filter(time_value >= as.Date("2021-11-01"))
+#'   filter(time_value >= as.Date("2021-11-01"), !(geo_value %in% tiny_geos))
 #'
 #' out <- arx_classifier(jhu, "death_rate", c("case_rate", "death_rate"))
 #'
@@ -58,7 +59,10 @@ arx_classifier <- function(
   if (args_list$adjust_latency == "none") {
     forecast_date_default <- max(epi_data$time_value)
     if (!is.null(args_list$forecast_date) && args_list$forecast_date != forecast_date_default) {
-      cli_warn("The specified forecast date {args_list$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+      cli_warn(
+        "The specified forecast date {args_list$forecast_date} doesn't match the
+        date from which the forecast is occurring {forecast_date}."
+      )
     }
   } else {
     forecast_date_default <- attributes(epi_data)$metadata$as_of
@@ -101,7 +105,7 @@ arx_classifier <- function(
 #'
 #' @return An unfit `epi_workflow`.
 #' @export
-#' @seealso [arx_classifier()]
+#' @seealso [arx_classifier()] [arx_class_args_list()]
 #' @examples
 #' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
@@ -154,12 +158,13 @@ arx_class_epi_workflow <- function(
       role = "grp",
       horizon = args_list$horizon,
       method = args_list$method,
-      log_scale = args_list$log_scale,
-      additional_gr_args_list = args_list$additional_gr_args
+      log_scale = args_list$log_scale
     )
   for (l in seq_along(lags)) {
     pred_names <- predictors[l]
-    pred_names <- as.character(glue::glue_data(args_list, "gr_{horizon}_{method}_{pred_names}"))
+    pred_names <- as.character(glue::glue_data(
+      args_list, "gr_{horizon}_{method}_{pred_names}"
+    ))
     r <- step_epi_lag(r, !!pred_names, lag = lags[[l]])
   }
   # ------- outcome
@@ -185,8 +190,7 @@ arx_class_epi_workflow <- function(
           role = "pre-outcome",
           horizon = args_list$horizon,
           method = args_list$method,
-          log_scale = args_list$log_scale,
-          additional_gr_args_list = args_list$additional_gr_args
+          log_scale = args_list$log_scale
         )
     }
   }
@@ -270,9 +274,6 @@ arx_class_epi_workflow <- function(
 #' @param method Character. Options available for growth rate calculation.
 #' @param log_scale Scalar logical. Whether to compute growth rates on the
 #'   log scale.
-#' @param additional_gr_args List. Optional arguments controlling growth rate
-#'   calculation. See [epiprocess::growth_rate()] and the related Vignette for
-#'   more details.
 #' @param check_enough_data_n Integer. A lower limit for the number of rows per
 #'   epi_key that are required for training. If `NULL`, this check is ignored.
 #' @param check_enough_data_epi_keys Character vector. A character vector of
@@ -301,7 +302,6 @@ arx_class_args_list <- function(
     horizon = 7L,
     method = c("rel_change", "linear_reg"),
     log_scale = FALSE,
-    additional_gr_args = list(),
     check_enough_data_n = NULL,
     check_enough_data_epi_keys = NULL,
     ...) {
@@ -320,23 +320,14 @@ arx_class_args_list <- function(
   arg_is_lgl(log_scale)
   arg_is_pos(n_training)
   if (is.finite(n_training)) arg_is_pos_int(n_training)
-  if (!is.list(additional_gr_args)) {
-    cli_abort(c(
-      "`additional_gr_args` must be a {.cls list}.",
-      "!" = "This is a {.cls {class(additional_gr_args)}}.",
-      i = "See `?epiprocess::growth_rate` for available arguments."
-    ))
-  }
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
       cli_warn(
-        paste0(
-          "`forecast_date`  {.val {forecast_date}} +",
-          " `ahead` {.val {ahead}} must equal `target_date` {.val {target_date}}."
-        ),
+        "`forecast_date` {.val {forecast_date}} +
+        `ahead` {.val {ahead}} must equal `target_date` {.val {target_date}}.",
         class = "epipredict__arx_args__inconsistent_target_ahead_forecaste_date"
       )
     }
@@ -362,7 +353,6 @@ arx_class_args_list <- function(
       horizon,
       method,
       log_scale,
-      additional_gr_args,
       check_enough_data_n,
       check_enough_data_epi_keys
     ),
diff --git a/R/step_growth_rate.R b/R/step_growth_rate.R
index b3a71231..159c2439 100644
--- a/R/step_growth_rate.R
+++ b/R/step_growth_rate.R
@@ -22,9 +22,7 @@
 #'   being removed from the data. Alternatively, you could specify arbitrary
 #'   large values, or perhaps zero. Setting this argument to `NULL` will result
 #'   in no replacement.
-#' @param additional_gr_args_list A list of additional arguments used by
-#'   [epiprocess::growth_rate()]. All `...` arguments may be passed here along
-#'   with `dup_rm` and `na_rm`.
+#' @inheritParams epiprocess::growth_rate
 #' @template step-return
 #'
 #'
@@ -32,12 +30,17 @@
 #' @importFrom epiprocess growth_rate
 #' @export
 #' @examples
-#' r <- epi_recipe(covid_case_death_rates) %>%
+#' library(dplyr)
+#' tiny_geos <- c("as", "mp", "vi", "gu", "pr")
+#' rates <- covid_case_death_rates %>%
+#'   filter(time_value >= as.Date("2021-11-01"), !(geo_value %in% tiny_geos))
+#'
+#' r <- epi_recipe(rates) %>%
 #'   step_growth_rate(case_rate, death_rate)
 #' r
 #'
 #' r %>%
-#'   prep(covid_case_death_rates) %>%
+#'   prep(rates) %>%
 #'   bake(new_data = NULL)
 step_growth_rate <-
   function(recipe,
@@ -46,11 +49,11 @@ step_growth_rate <-
            horizon = 7,
            method = c("rel_change", "linear_reg"),
            log_scale = FALSE,
+           na_rm = TRUE,
            replace_Inf = NA,
            prefix = "gr_",
            skip = FALSE,
-           id = rand_id("growth_rate"),
-           additional_gr_args_list = list()) {
+           id = rand_id("growth_rate")) {
     if (!is_epi_recipe(recipe)) {
       cli_abort("This recipe step can only operate on an {.cls epi_recipe}.")
     }
@@ -63,15 +66,7 @@ step_growth_rate <-
     }
     arg_is_chr(role)
     arg_is_chr_scalar(prefix, id)
-    arg_is_lgl_scalar(log_scale, skip)
-
-
-    if (!is.list(additional_gr_args_list)) {
-      cli_abort(c(
-        "`additional_gr_args_list` must be a {.cls list}.",
-        i = "See `?epiprocess::growth_rate` for available options."
-      ))
-    }
+    arg_is_lgl_scalar(log_scale, skip, na_rm)
 
     recipes::add_step(
       recipe,
@@ -82,13 +77,13 @@ step_growth_rate <-
         horizon = horizon,
         method = method,
         log_scale = log_scale,
+        na_rm = na_rm,
         replace_Inf = replace_Inf,
         prefix = prefix,
         keys = key_colnames(recipe),
         columns = NULL,
         skip = skip,
-        id = id,
-        additional_gr_args_list = additional_gr_args_list
+        id = id
       )
     )
   }
@@ -101,13 +96,13 @@ step_growth_rate_new <-
            horizon,
            method,
            log_scale,
+           na_rm,
            replace_Inf,
            prefix,
            keys,
            columns,
            skip,
-           id,
-           additional_gr_args_list) {
+           id) {
     recipes::step(
       subclass = "growth_rate",
       terms = terms,
@@ -116,13 +111,13 @@ step_growth_rate_new <-
       horizon = horizon,
       method = method,
       log_scale = log_scale,
+      na_rm = na_rm,
       replace_Inf = replace_Inf,
       prefix = prefix,
       keys = keys,
       columns = columns,
       skip = skip,
-      id = id,
-      additional_gr_args_list = additional_gr_args_list
+      id = id
     )
   }
 
@@ -137,13 +132,13 @@ prep.step_growth_rate <- function(x, training, info = NULL, ...) {
     horizon = x$horizon,
     method = x$method,
     log_scale = x$log_scale,
+    na_rm = x$na_rm,
     replace_Inf = x$replace_Inf,
     prefix = x$prefix,
     keys = x$keys,
     columns = recipes::recipes_eval_select(x$terms, training, info),
     skip = x$skip,
-    id = x$id,
-    additional_gr_args_list = x$additional_gr_args_list
+    id = x$id
   )
 }
 
@@ -177,10 +172,12 @@ bake.step_growth_rate <- function(object, new_data, ...) {
       across(
         all_of(object$columns),
         ~ epiprocess::growth_rate(
-          time_value, .x,
+          .x,
+          x = time_value,
           method = object$method,
-          h = object$horizon, log_scale = object$log_scale,
-          !!!object$additional_gr_args_list
+          h = object$horizon,
+          log_scale = object$log_scale,
+          na_rm = object$na_rm
         ),
         .names = "{object$prefix}{object$horizon}_{object$method}_{.col}"
       )
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index dbf27535..40bb48ca 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -17,7 +17,6 @@ arx_class_args_list(
   horizon = 7L,
   method = c("rel_change", "linear_reg"),
   log_scale = FALSE,
-  additional_gr_args = list(),
   check_enough_data_n = NULL,
   check_enough_data_epi_keys = NULL,
   ...
@@ -96,10 +95,6 @@ calculate the growth rate.}
 \item{log_scale}{Scalar logical. Whether to compute growth rates on the
 log scale.}
 
-\item{additional_gr_args}{List. Optional arguments controlling growth rate
-calculation. See \code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()}} and the related Vignette for
-more details.}
-
 \item{check_enough_data_n}{Integer. A lower limit for the number of rows per
 epi_key that are required for training. If \code{NULL}, this check is ignored.}
 
diff --git a/man/arx_class_epi_workflow.Rd b/man/arx_class_epi_workflow.Rd
index 9f0aae6a..7497fe95 100644
--- a/man/arx_class_epi_workflow.Rd
+++ b/man/arx_class_epi_workflow.Rd
@@ -65,5 +65,5 @@ arx_class_epi_workflow(
 )
 }
 \seealso{
-\code{\link[=arx_classifier]{arx_classifier()}}
+\code{\link[=arx_classifier]{arx_classifier()}} \code{\link[=arx_class_args_list]{arx_class_args_list()}}
 }
diff --git a/man/arx_classifier.Rd b/man/arx_classifier.Rd
index 94503f3d..22edc98e 100644
--- a/man/arx_classifier.Rd
+++ b/man/arx_classifier.Rd
@@ -49,8 +49,9 @@ that it estimates a class at a particular target horizon.
 }
 \examples{
 library(dplyr)
+tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 jhu <- covid_case_death_rates \%>\%
-  filter(time_value >= as.Date("2021-11-01"))
+  filter(time_value >= as.Date("2021-11-01"), !(geo_value \%in\% tiny_geos))
 
 out <- arx_classifier(jhu, "death_rate", c("case_rate", "death_rate"))
 
diff --git a/man/epi_recipe.Rd b/man/epi_recipe.Rd
index 98775eae..b93e4bcf 100644
--- a/man/epi_recipe.Rd
+++ b/man/epi_recipe.Rd
@@ -56,17 +56,17 @@ anything but common roles are \code{"outcome"}, \code{"predictor"},
 }
 \value{
 An object of class \code{recipe} with sub-objects:
-\item{var_info}{A tibble containing information about the original data
-set columns}
+\item{var_info}{A tibble containing information about the original data set
+columns.}
 \item{term_info}{A tibble that contains the current set of terms in the
 data set. This initially defaults to the same data contained in
 \code{var_info}.}
-\item{steps}{A list of \code{step}  or \code{check} objects that define the sequence of
-preprocessing operations that will be applied to data. The default value is
-\code{NULL}}
-\item{template}{A tibble of the data. This is initialized to be the same
-as the data given in the \code{data} argument but can be different after
-the recipe is trained.}
+\item{steps}{A list of \code{step} or \code{check} objects that define the sequence
+of preprocessing operations that will be applied to data. The default value
+is \code{NULL}.}
+\item{template}{A tibble of the data. This is initialized to be the same as
+the data given in the \code{data} argument but can be different after the recipe
+is trained.}
 }
 \description{
 A recipe is a description of the steps to be applied to a data set in
diff --git a/man/step_growth_rate.Rd b/man/step_growth_rate.Rd
index 12963f8d..2c98e74e 100644
--- a/man/step_growth_rate.Rd
+++ b/man/step_growth_rate.Rd
@@ -11,11 +11,11 @@ step_growth_rate(
   horizon = 7,
   method = c("rel_change", "linear_reg"),
   log_scale = FALSE,
+  na_rm = TRUE,
   replace_Inf = NA,
   prefix = "gr_",
   skip = FALSE,
-  id = rand_id("growth_rate"),
-  additional_gr_args_list = list()
+  id = rand_id("growth_rate")
 )
 }
 \arguments{
@@ -41,6 +41,9 @@ growth rates). See \code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()
 \item{log_scale}{Should growth rates be estimated using the parameterization
 on the log scale? See details for an explanation. Default is \code{FALSE}.}
 
+\item{na_rm}{Should missing values be removed before the computation? Default
+is \code{FALSE}.}
+
 \item{replace_Inf}{Sometimes, the growth rate calculation can result in
 infinite values (if the denominator is zero, for example). In this case,
 most prediction methods will fail. This argument specifies potential
@@ -59,10 +62,6 @@ Care should be taken when using \code{skip = TRUE} as it may affect
 the computations for subsequent operations.}
 
 \item{id}{A unique identifier for the step}
-
-\item{additional_gr_args_list}{A list of additional arguments used by
-\code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()}}. All \code{...} arguments may be passed here along
-with \code{dup_rm} and \code{na_rm}.}
 }
 \value{
 An updated version of \code{recipe} with the new step added to the
@@ -73,12 +72,17 @@ sequence of any existing operations.
 that will generate one or more new columns of derived data.
 }
 \examples{
-r <- epi_recipe(covid_case_death_rates) \%>\%
+library(dplyr)
+tiny_geos <- c("as", "mp", "vi", "gu", "pr")
+rates <- covid_case_death_rates \%>\%
+  filter(time_value >= as.Date("2021-11-01"), !(geo_value \%in\% tiny_geos))
+
+r <- epi_recipe(rates) \%>\%
   step_growth_rate(case_rate, death_rate)
 r
 
 r \%>\%
-  prep(covid_case_death_rates) \%>\%
+  prep(rates) \%>\%
   bake(new_data = NULL)
 }
 \seealso{
diff --git a/tests/testthat/_snaps/layers.md b/tests/testthat/_snaps/layers.md
index a0474eab..7f208f2e 100644
--- a/tests/testthat/_snaps/layers.md
+++ b/tests/testthat/_snaps/layers.md
@@ -3,7 +3,7 @@
     Code
       update(f$layers[[1]], lower = 100)
     Condition
-      Error in `recipes:::update_fields()`:
+      Error in `update()`:
       ! The step you are trying to update, `layer_predict()`, does not have the lower field.
 
 ---
@@ -19,6 +19,6 @@
     Code
       update(f$layers[[2]], bad_param = 100)
     Condition
-      Error in `recipes:::update_fields()`:
+      Error in `update()`:
       ! The step you are trying to update, `layer_threshold()`, does not have the bad_param field.
 
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 17191e04..d1cf2df7 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -1237,54 +1237,52 @@
 # arx_classifier snapshots
 
     structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co", 
-    "ct", "dc", "de", "fl", "ga", "gu", "hi", "ia", "id", "il", "in", 
-    "ks", "ky", "la", "ma", "me", "mi", "mn", "mo", "mp", "ms", "mt", 
-    "nc", "nd", "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", 
-    "pa", "pr", "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", 
-    "wi", "wv", "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 
+    "ct", "dc", "de", "fl", "ga", "hi", "ia", "id", "il", "in", "ks", 
+    "ky", "la", "ma", "me", "mi", "mn", "mo", "ms", "mt", "nc", "nd", 
+    "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", "pa", "pr", 
+    "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", "wi", "wv", 
+    "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
-    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
-    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
-    1L), levels = c("(-Inf,0.25]", "(0.25, Inf]"), class = "factor"), 
-        forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
-        18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
-        18999, 18999, 18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
-    -53L), class = c("tbl_df", "tbl", "data.frame"))
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("(-Inf,0.25]", 
+    "(0.25, Inf]"), class = "factor"), forecast_date = structure(c(18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 
+    18992, 18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 
+    18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
+    -51L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
 
     structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co", 
-    "ct", "dc", "de", "fl", "ga", "gu", "hi", "ia", "id", "il", "in", 
-    "ks", "ky", "la", "ma", "me", "mi", "mn", "mo", "mp", "ms", "mt", 
-    "nc", "nd", "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", 
-    "pa", "pr", "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", 
-    "wi", "wv", "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 
-    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
+    "ct", "dc", "de", "fl", "ga", "hi", "ia", "id", "il", "in", "ks", 
+    "ky", "la", "ma", "me", "mi", "mn", "mo", "ms", "mt", "nc", "nd", 
+    "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", "pa", "pr", 
+    "ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", "wi", "wv", 
+    "wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
-    1L), levels = c("(-Inf,0.25]", "(0.25, Inf]"), class = "factor"), 
-        forecast_date = structure(c(18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
-        18994, 18994, 18994), class = "Date"), target_date = structure(c(19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
-        19001, 19001, 19001, 19001, 19001, 19001, 19001), class = "Date")), row.names = c(NA, 
-    -53L), class = c("tbl_df", "tbl", "data.frame"))
+    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("(-Inf,0.25]", 
+    "(0.25, Inf]"), class = "factor"), forecast_date = structure(c(18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 
+    18994, 18994, 18994, 18994, 18994), class = "Date"), target_date = structure(c(19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 
+    19001, 19001, 19001, 19001, 19001), class = "Date")), row.names = c(NA, 
+    -51L), class = c("tbl_df", "tbl", "data.frame"))
 
diff --git a/tests/testthat/_snaps/step_growth_rate.md b/tests/testthat/_snaps/step_growth_rate.md
index 0912977e..409aa197 100644
--- a/tests/testthat/_snaps/step_growth_rate.md
+++ b/tests/testthat/_snaps/step_growth_rate.md
@@ -89,19 +89,18 @@
 ---
 
     Code
-      step_growth_rate(r, value, skip = 1)
+      step_growth_rate(r, value, na_rm = 1)
     Condition
       Error in `step_growth_rate()`:
-      ! `skip` must be a scalar of type <logical>.
+      ! `na_rm` must be a scalar of type <logical>.
 
 ---
 
     Code
-      step_growth_rate(r, value, additional_gr_args_list = 1:5)
+      step_growth_rate(r, value, skip = 1)
     Condition
       Error in `step_growth_rate()`:
-      ! `additional_gr_args_list` must be a <list>.
-      i See `?epiprocess::growth_rate` for available options.
+      ! `skip` must be a scalar of type <logical>.
 
 ---
 
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index 3aecfd6b..8b2ae838 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -146,16 +146,18 @@ test_that("arx_forecaster output format snapshots", {
 })
 
 test_that("arx_classifier snapshots", {
-  arc1 <- arx_classifier(
-    covid_case_death_rates %>%
+  train <- covid_case_death_rates %>%
+    filter(geo_value %nin% c("as", "gu", "mp", "vi"))
+  expect_warning(arc1 <- arx_classifier(
+    train %>%
       dplyr::filter(time_value >= as.Date("2021-11-01")),
     "death_rate",
     c("case_rate", "death_rate")
-  )
+  ), "fitted probabilities numerically")
   expect_snapshot_tibble(arc1$predictions)
-  max_date <- covid_case_death_rates$time_value %>% max()
+  max_date <- train$time_value %>% max()
   arc2 <- arx_classifier(
-    covid_case_death_rates %>%
+    train %>%
       dplyr::filter(time_value >= as.Date("2021-11-01")),
     "death_rate",
     c("case_rate", "death_rate"),
@@ -164,7 +166,7 @@ test_that("arx_classifier snapshots", {
   expect_snapshot_tibble(arc2$predictions)
   expect_error(
     arc3 <- arx_classifier(
-      covid_case_death_rates %>%
+      train %>%
         dplyr::filter(time_value >= as.Date("2021-11-01")),
       "death_rate",
       c("case_rate", "death_rate"),
@@ -174,7 +176,7 @@ test_that("arx_classifier snapshots", {
   )
   expect_error(
     arc4 <- arx_classifier(
-      covid_case_death_rates %>%
+      train %>%
         dplyr::filter(time_value >= as.Date("2021-11-01")),
       "death_rate",
       c("case_rate", "death_rate"),
diff --git a/tests/testthat/test-step_growth_rate.R b/tests/testthat/test-step_growth_rate.R
index f2845d81..dc8f04c9 100644
--- a/tests/testthat/test-step_growth_rate.R
+++ b/tests/testthat/test-step_growth_rate.R
@@ -16,8 +16,8 @@ test_that("step_growth_rate validates arguments", {
   expect_snapshot(error = TRUE, step_growth_rate(r, value, prefix = 1))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, id = 1))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, log_scale = 1))
+  expect_snapshot(error = TRUE, step_growth_rate(r, value, na_rm = 1))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, skip = 1))
-  expect_snapshot(error = TRUE, step_growth_rate(r, value, additional_gr_args_list = 1:5))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, replace_Inf = "c"))
   expect_snapshot(error = TRUE, step_growth_rate(r, value, replace_Inf = c(1, 2)))
   expect_silent(step_growth_rate(r, value, replace_Inf = NULL))