diff --git a/R/cdc_baseline_forecaster.R b/R/cdc_baseline_forecaster.R
index e39ad9aea..f1e0e7099 100644
--- a/R/cdc_baseline_forecaster.R
+++ b/R/cdc_baseline_forecaster.R
@@ -161,11 +161,11 @@ cdc_baseline_forecaster <- function(
 #' cdc_baseline_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
 cdc_baseline_args_list <- function(
     data_frequency = "1 week",
-    aheads = 1:4,
+    aheads = 1:5,
     n_training = Inf,
     forecast_date = NULL,
     quantile_levels = c(.01, .025, 1:19 / 20, .975, .99),
-    nsims = 1e3L,
+    nsims = 1e5L,
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = "geo_value",
diff --git a/R/flusight_hub_formatter.R b/R/flusight_hub_formatter.R
index 170483575..0dbd1a954 100644
--- a/R/flusight_hub_formatter.R
+++ b/R/flusight_hub_formatter.R
@@ -1,19 +1,28 @@
-abbr_to_fips <- function(abbr) {
-  fi <- dplyr::left_join(
-    tibble::tibble(abbr = tolower(abbr)),
-    state_census,
-    by = "abbr"
-  ) %>%
-    dplyr::mutate(fips = as.character(fips), fips = case_when(
-      fips == "0" ~ "US",
-      nchar(fips) < 2L ~ paste0("0", fips),
-      TRUE ~ fips
-    )) %>%
-    pull(.data$fips)
-  names(fi) <- NULL
-  fi
+location_to_abbr <- function(location) {
+  dictionary <-
+    state_census %>%
+    dplyr::mutate(fips = sprintf("%02d", fips)) %>%
+    dplyr::transmute(
+      location = dplyr::case_match(fips, "00" ~ "US", .default = fips),
+      abbr
+    )
+  dictionary$abbr[match(location, dictionary$location)]
 }
 
+abbr_to_location <- function(abbr) {
+  dictionary <-
+    state_census %>%
+    dplyr::mutate(fips = sprintf("%02d", fips)) %>%
+    dplyr::transmute(
+      location = dplyr::case_match(fips, "00" ~ "US", .default = fips),
+      abbr
+    )
+  dictionary$location[match(abbr, dictionary$abbr)]
+}
+
+
+
+
 #' Format predictions for submission to FluSight forecast Hub
 #'
 #' This function converts predictions from any of the included forecasters into
@@ -47,22 +56,23 @@ abbr_to_fips <- function(abbr) {
 #' @export
 #'
 #' @examples
-#' library(dplyr)
-#' weekly_deaths <- case_death_rate_subset %>%
-#'   select(geo_value, time_value, death_rate) %>%
-#'   left_join(state_census %>% select(pop, abbr), by = c("geo_value" = "abbr")) %>%
-#'   mutate(deaths = pmax(death_rate / 1e5 * pop * 7, 0)) %>%
-#'   select(-pop, -death_rate) %>%
-#'   group_by(geo_value) %>%
-#'   epi_slide(~ sum(.$deaths), before = 6, new_col_name = "deaths") %>%
-#'   ungroup() %>%
-#'   filter(weekdays(time_value) == "Saturday")
+#' if (require(dplyr)) {
+#'   weekly_deaths <- case_death_rate_subset %>%
+#'     select(geo_value, time_value, death_rate) %>%
+#'     left_join(state_census %>% select(pop, abbr), by = c("geo_value" = "abbr")) %>%
+#'     mutate(deaths = pmax(death_rate / 1e5 * pop * 7, 0)) %>%
+#'     select(-pop, -death_rate) %>%
+#'     group_by(geo_value) %>%
+#'     epi_slide(~ sum(.$deaths), before = 6, new_col_name = "deaths") %>%
+#'     ungroup() %>%
+#'     filter(weekdays(time_value) == "Saturday")
 #'
-#' cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths")
-#' flusight_hub_formatter(cdc)
-#' flusight_hub_formatter(cdc, target = "wk inc covid deaths")
-#' flusight_hub_formatter(cdc, target = paste(horizon, "wk inc covid deaths"))
-#' flusight_hub_formatter(cdc, target = "wk inc covid deaths", output_type = "quantile")
+#'   cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths")
+#'   flusight_hub_formatter(cdc)
+#'   flusight_hub_formatter(cdc, target = "wk inc covid deaths")
+#'   flusight_hub_formatter(cdc, target = paste(horizon, "wk inc covid deaths"))
+#'   flusight_hub_formatter(cdc, target = "wk inc covid deaths", output_type = "quantile")
+#' }
 flusight_hub_formatter <- function(
     object, ...,
     .fcast_period = c("daily", "weekly")) {
@@ -93,11 +103,6 @@ flusight_hub_formatter.data.frame <- function(
   object <- object %>%
     # combine the predictions and the distribution
     dplyr::mutate(.pred_distn = nested_quantiles(.pred_distn)) %>%
-    dplyr::rowwise() %>%
-    dplyr::mutate(
-      .pred_distn = list(add_row(.pred_distn, values = .pred, quantile_levels = NA)),
-      .pred = NULL
-    ) %>%
     tidyr::unnest(.pred_distn) %>%
     # now we create the correct column names
     dplyr::rename(
@@ -106,7 +111,7 @@ flusight_hub_formatter.data.frame <- function(
       reference_date = forecast_date
     ) %>%
     # convert to fips codes, and add any constant cols passed in ...
-    dplyr::mutate(location = abbr_to_fips(tolower(geo_value)), geo_value = NULL)
+    dplyr::mutate(location = abbr_to_location(tolower(geo_value)), geo_value = NULL)
 
   # create target_end_date / horizon, depending on what is available
   pp <- ifelse(match.arg(.fcast_period) == "daily", 1L, 7L)
diff --git a/man/cdc_baseline_args_list.Rd b/man/cdc_baseline_args_list.Rd
index 2f6546f74..e89c35cfc 100644
--- a/man/cdc_baseline_args_list.Rd
+++ b/man/cdc_baseline_args_list.Rd
@@ -6,11 +6,11 @@
 \usage{
 cdc_baseline_args_list(
   data_frequency = "1 week",
-  aheads = 1:4,
+  aheads = 1:5,
   n_training = Inf,
   forecast_date = NULL,
   quantile_levels = c(0.01, 0.025, 1:19/20, 0.975, 0.99),
-  nsims = 1000L,
+  nsims = 100000L,
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = "geo_value",
diff --git a/man/flusight_hub_formatter.Rd b/man/flusight_hub_formatter.Rd
index d8a4571f4..8f3604756 100644
--- a/man/flusight_hub_formatter.Rd
+++ b/man/flusight_hub_formatter.Rd
@@ -41,20 +41,21 @@ be done via the \code{...} argument. See the examples below. The specific requir
 format for this forecast task is \href{https://github.com/cdcepi/FluSight-forecast-hub/blob/main/model-output/README.md}{here}.
 }
 \examples{
-library(dplyr)
-weekly_deaths <- case_death_rate_subset \%>\%
-  select(geo_value, time_value, death_rate) \%>\%
-  left_join(state_census \%>\% select(pop, abbr), by = c("geo_value" = "abbr")) \%>\%
-  mutate(deaths = pmax(death_rate / 1e5 * pop * 7, 0)) \%>\%
-  select(-pop, -death_rate) \%>\%
-  group_by(geo_value) \%>\%
-  epi_slide(~ sum(.$deaths), before = 6, new_col_name = "deaths") \%>\%
-  ungroup() \%>\%
-  filter(weekdays(time_value) == "Saturday")
+if (require(dplyr)) {
+  weekly_deaths <- case_death_rate_subset \%>\%
+    select(geo_value, time_value, death_rate) \%>\%
+    left_join(state_census \%>\% select(pop, abbr), by = c("geo_value" = "abbr")) \%>\%
+    mutate(deaths = pmax(death_rate / 1e5 * pop * 7, 0)) \%>\%
+    select(-pop, -death_rate) \%>\%
+    group_by(geo_value) \%>\%
+    epi_slide(~ sum(.$deaths), before = 6, new_col_name = "deaths") \%>\%
+    ungroup() \%>\%
+    filter(weekdays(time_value) == "Saturday")
 
-cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths")
-flusight_hub_formatter(cdc)
-flusight_hub_formatter(cdc, target = "wk inc covid deaths")
-flusight_hub_formatter(cdc, target = paste(horizon, "wk inc covid deaths"))
-flusight_hub_formatter(cdc, target = "wk inc covid deaths", output_type = "quantile")
+  cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths")
+  flusight_hub_formatter(cdc)
+  flusight_hub_formatter(cdc, target = "wk inc covid deaths")
+  flusight_hub_formatter(cdc, target = paste(horizon, "wk inc covid deaths"))
+  flusight_hub_formatter(cdc, target = "wk inc covid deaths", output_type = "quantile")
+}
 }