diff --git a/.Rbuildignore b/.Rbuildignore
index dc41e622..92351a76 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -22,3 +22,6 @@
 ^.lintr$
 ^.venv$
 ^inst/templates$
+^\.workflow$
+^\.plans$
+^\.claude$
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 7dc0f9d8..c0683f98 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -17,7 +17,7 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       R_KEEP_PKG_SOURCE: yes
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - uses: r-lib/actions/setup-r@v2
         with:
diff --git a/.github/workflows/doc-preview.yaml b/.github/workflows/doc-preview.yaml
index 06818422..22d487f0 100644
--- a/.github/workflows/doc-preview.yaml
+++ b/.github/workflows/doc-preview.yaml
@@ -17,7 +17,7 @@ jobs:
       # Needed to read the PR branch
       contents: read
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           # Checkout the PR branch
           ref: refs/pull/${{ github.event.issue.number }}/head
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 2aae89fa..c8462eea 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -26,7 +26,7 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       DELPHI_EPIDATA_KEY: ${{ secrets.SECRET_EPIPREDICT_GHACTIONS_DELPHI_EPIDATA_KEY }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - uses: r-lib/actions/setup-pandoc@v2
 
diff --git a/.github/workflows/pr-commands.yaml b/.github/workflows/pr-commands.yaml
index 8bb12b9e..5c4ea908 100644
--- a/.github/workflows/pr-commands.yaml
+++ b/.github/workflows/pr-commands.yaml
@@ -57,7 +57,7 @@ jobs:
     permissions:
       contents: write
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
diff --git a/.gitignore b/.gitignore
index ae7df412..51bb061e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,6 @@ inst/doc
 renv.lock
 renv/
 .Renviron
+.workflow/
+.plans/
+.claude/
diff --git a/DESCRIPTION b/DESCRIPTION
index ae08a35c..8aa08d33 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.2.1
+Version: 0.2.7
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
@@ -21,7 +21,7 @@ Description: A forecasting "framework" for creating epidemiological
     are included.
 License: MIT + file LICENSE
 URL: https://github.com/cmu-delphi/epipredict/,
-    https://cmu-delphi.github.io/epipredict
+    https://cmu-delphi.github.io/epipredict/
 BugReports: https://github.com/cmu-delphi/epipredict/issues/
 Depends:
     epidatasets,
@@ -31,7 +31,7 @@ Imports:
     checkmate,
     cli,
     dplyr,
-    epiprocess (>= 0.11.2),
+    epiprocess (>= 0.13.0),
     generics,
     ggplot2,
     glue,
@@ -78,4 +78,4 @@ Config/Needs/website: cmu-delphi/delphidocs
 Config/testthat/edition: 3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
diff --git a/NEWS.md b/NEWS.md
index c851711c..77b60459 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,39 @@
 
 Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicate PR's.
 
+# epipredict 0.2.7
+
+- `autoplot.canned_epipred()` had internal code using
+  `epiprocess:::autoplot.epi_df(.max_facets = Inf)`. This argument no longer
+  exists there, so it caused an error (noted by rebuilding the `README.Rmd`)
+
+
+# epipredict 0.2.6
+
+- `arx_forecaster()` and `flatline_forecaster()` now error early when `quantile_by_key` contains columns that are not keys of the input `epi_df`, rather than silently dropping the invalid keys (#229).
+- `arx_forecaster()` now warns when `quantile_by_key` is supplied with a quantile-output trainer (`quantile_reg()`, `rand_forest()` with engine `"grf_quantiles"`), where the argument would otherwise be silently ignored (#229).
+- Regenerate roxygen-derived `man/step_adjust_latency.Rd` so its recorded
+  example output matches the current `epi_df` print phrasing (`lag` → `latency`)
+  from upstream `epiprocess`. No user-visible behavior change.
+- Bump GitHub action checkout version.
+
+# epipredict 0.2.5
+
+- Fix `arx_forecaster()` and `arx_fcast_epi_workflow()` so that the error raised when `forecast_date + ahead != target_date` reports the actual validation message rather than a cryptic `cli` template-evaluation error (#473).
+
+# epipredict 0.2.4
+
+- Fix `flatline_forecaster()` to return one prediction per geographic key when the input `epi_df` has trailing rows with `NA`s in the outcome (#454). Previously, the forecast was duplicated once per trailing-NA day.
+
+# epipredict 0.2.3
+
+- Fix `print.canned_epipred()` so the latency-adjustment information actually displays for canned forecasters that include `step_adjust_latency` in their recipe (#447).
+
+# epipredict 0.2.2
+
+- Fix `autoplot.epi_workflow()` to correctly handle the response variable and avoid errors related to `.response`.
+- Prevent subsampling in `autoplot.epi_workflow()` by setting `.max_keys = Inf`.
+
 # epipredict 0.2.1
 
 - Fix bug in `flusight_hub_formatter()` so that it works as expected even if the user has not first loaded the `epidatasets` package.
@@ -16,12 +49,12 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   `data(<dataset name>, package = "epidatasets")`, `epidatasets::<dataset name>`
   or, after loading the package, the name of the dataset alone (#382).
 - `step_adjust_latency()` no longer allows empty column selection.
-- Addresses upstream breaking changes from cmu-delphi/epiprocess#595 (`growth_rate()`). 
+- 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.
 - Moves `epiprocess` out of depends (#440). No internals have changed, but downstream
   users may need to add `library(epiprocess)` to existing code.
-- Removes dependence on the `distributional` package, replacing the quantiles 
+- Removes dependence on the `distributional` package, replacing the quantiles
   with `hardhat::quantile_pred()`. Some associated functions are deprecated with
   `lifecycle` messages.
 - Rename `check_enough_train_data()` to `check_enough_data()`, and generalize it
@@ -43,6 +76,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Allow `quantile()` to threshold to an interval if desired (#434)
 - `arx_forecaster()` detects if there's enough data to predict
 - Add `observed_response` to `autoplot` so that forecasts can be plotted against the values they're predicting
+- `pivot_quantiles_longer()` now appropriately adds `quantile_level` to the
+  `epi_df` other keys
 
 ## Bug fixes
 
diff --git a/R/arx_classifier.R b/R/arx_classifier.R
index 57812233..44acca28 100644
--- a/R/arx_classifier.R
+++ b/R/arx_classifier.R
@@ -141,21 +141,33 @@
 #'   )
 #' )
 arx_classifier <- function(
-    epi_data,
-    outcome,
-    predictors,
-    trainer = logistic_reg(),
-    args_list = arx_class_args_list()) {
+  epi_data,
+  outcome,
+  predictors,
+  trainer = logistic_reg(),
+  args_list = arx_class_args_list()
+) {
   if (!is_classification(trainer)) {
-    cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'classification'.")
+    cli_abort(
+      "`trainer` must be a {.pkg parsnip} model of mode 'classification'."
+    )
   }
 
-  wf <- arx_class_epi_workflow(epi_data, outcome, predictors, trainer, args_list)
+  wf <- arx_class_epi_workflow(
+    epi_data,
+    outcome,
+    predictors,
+    trainer,
+    args_list
+  )
   wf <- fit(wf, epi_data)
 
   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) {
+    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}."
@@ -218,23 +230,31 @@ arx_classifier <- function(
 #'   )
 #' )
 arx_class_epi_workflow <- function(
-    epi_data,
-    outcome,
-    predictors,
-    trainer = parsnip::logistic_reg(),
-    args_list = arx_class_args_list()) {
+  epi_data,
+  outcome,
+  predictors,
+  trainer = parsnip::logistic_reg(),
+  args_list = arx_class_args_list()
+) {
   validate_forecaster_inputs(epi_data, outcome, predictors)
   if (!inherits(args_list, c("arx_class", "alist"))) {
     cli_abort("`args_list` was not created using `arx_class_args_list()`.")
   }
   if (!(is.null(trainer) || is_classification(trainer))) {
-    cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'classification'.")
+    cli_abort(
+      "`trainer` must be a {.pkg parsnip} model of mode 'classification'."
+    )
   }
 
   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$forecast_date} doesn't match the date from which the forecast is occurring {forecast_date}.")
+    if (
+      !is.null(args_list$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}."
+      )
     }
   } else {
     forecast_date_default <- attributes(epi_data)$metadata$as_of
@@ -257,7 +277,8 @@ arx_class_epi_workflow <- function(
   for (l in seq_along(lags)) {
     pred_names <- predictors[l]
     pred_names <- as.character(glue::glue_data(
-      args_list, "gr_{horizon}_{method}_{pred_names}"
+      args_list,
+      "gr_{horizon}_{method}_{pred_names}"
     ))
     r <- step_epi_lag(r, !!pred_names, lag = lags[[l]])
   }
@@ -293,17 +314,24 @@ arx_class_epi_workflow <- function(
   method_adjust_latency <- args_list$adjust_latency
   if (method_adjust_latency != "none") {
     if (method_adjust_latency != "extend_ahead") {
-      cli_abort("only extend_ahead is currently supported",
+      cli_abort(
+        "only extend_ahead is currently supported",
         class = "epipredict__arx_classifier__adjust_latency_unsupported_method"
       )
     }
-    r <- r %>% step_adjust_latency(!!pre_out_name,
-      fixed_forecast_date = forecast_date,
-      method = method_adjust_latency
-    )
+    r <- r %>%
+      step_adjust_latency(
+        !!pre_out_name,
+        fixed_forecast_date = forecast_date,
+        method = method_adjust_latency
+      )
   }
   r <- r %>%
-    step_epi_ahead(!!pre_out_name, ahead = args_list$ahead, role = "pre-outcome")
+    step_epi_ahead(
+      !!pre_out_name,
+      ahead = args_list$ahead,
+      role = "pre-outcome"
+    )
   r <- r %>%
     step_mutate(
       across(
@@ -345,8 +373,8 @@ arx_class_epi_workflow <- function(
 #'   be created using growth rates (as the predictors are) or lagged
 #'   differences. The second case is closer to the requirements for the
 #'   [2022-23 CDC Flusight Hospitalization Experimental Target](https://github.com/cdcepi/Flusight-forecast-data/blob/745511c436923e1dc201dea0f4181f21a8217b52/data-experimental/README.md).
-#'   See the [Classification chapter from the forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/arx-classifier.html) Vignette for details of how to create a reasonable
-#'   baseline for this case. Selecting `"growth_rate"` (the default) uses
+#'   See the `vignette("epipredict")` for more details.
+#'   Selecting `"growth_rate"` (the default) uses
 #'   [epiprocess::growth_rate()] to create the outcome using some of the
 #'   additional arguments below. Choosing `"lag_difference"` instead simply
 #'   uses the change from the value at the selected `horizon`.
@@ -384,36 +412,48 @@ arx_class_epi_workflow <- function(
 #' # also needs arx_classifier(trainer = parsnip::multinom_reg())
 #' arx_class_args_list(breaks = c(-.2, .25))
 arx_class_args_list <- function(
-    lags = c(0L, 7L, 14L),
-    ahead = 7L,
-    n_training = Inf,
-    forecast_date = NULL,
-    target_date = NULL,
-    adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
-    warn_latency = TRUE,
-    outcome_transform = c("growth_rate", "lag_difference"),
-    breaks = 0.25,
-    horizon = 7L,
-    method = c("rel_change", "linear_reg"),
-    log_scale = FALSE,
-    check_enough_data_n = NULL,
-    check_enough_data_epi_keys = NULL,
-    ...) {
+  lags = c(0L, 7L, 14L),
+  ahead = 7L,
+  n_training = Inf,
+  forecast_date = NULL,
+  target_date = NULL,
+  adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
+  warn_latency = TRUE,
+  outcome_transform = c("growth_rate", "lag_difference"),
+  breaks = 0.25,
+  horizon = 7L,
+  method = c("rel_change", "linear_reg"),
+  log_scale = FALSE,
+  check_enough_data_n = NULL,
+  check_enough_data_epi_keys = NULL,
+  ...
+) {
   rlang::check_dots_empty()
   .lags <- lags
-  if (is.list(lags)) lags <- unlist(lags)
+  if (is.list(lags)) {
+    lags <- unlist(lags)
+  }
   method <- rlang::arg_match(method)
   outcome_transform <- rlang::arg_match(outcome_transform)
 
   adjust_latency <- rlang::arg_match(adjust_latency)
-  arg_is_scalar(ahead, n_training, horizon, log_scale, adjust_latency, warn_latency)
+  arg_is_scalar(
+    ahead,
+    n_training,
+    horizon,
+    log_scale,
+    adjust_latency,
+    warn_latency
+  )
   arg_is_scalar(forecast_date, target_date, allow_null = TRUE)
   arg_is_date(forecast_date, target_date, allow_null = TRUE)
   arg_is_nonneg_int(ahead, lags, horizon)
   arg_is_numeric(breaks)
   arg_is_lgl(log_scale)
   arg_is_pos(n_training)
-  if (is.finite(n_training)) arg_is_pos_int(n_training)
+  if (is.finite(n_training)) {
+    arg_is_pos_int(n_training)
+  }
   arg_is_pos(check_enough_data_n, allow_null = TRUE)
   arg_is_chr(check_enough_data_epi_keys, allow_null = TRUE)
 
@@ -428,9 +468,12 @@ arx_class_args_list <- function(
   }
 
   breaks <- sort(breaks)
-  if (min(breaks) > -Inf) breaks <- c(-Inf, breaks)
-  if (max(breaks) < Inf) breaks <- c(breaks, Inf)
-
+  if (min(breaks) > -Inf) {
+    breaks <- c(-Inf, breaks)
+  }
+  if (max(breaks) < Inf) {
+    breaks <- c(breaks, Inf)
+  }
 
   max_lags <- max(lags)
   structure(
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index 7ef60966..bc479143 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -127,6 +127,20 @@ arx_fcast_epi_workflow <- function(
   if (!(is.null(trainer) || is_regression(trainer))) {
     cli_abort("`trainer` must be a {.pkg parsnip} model of mode 'regression'.")
   }
+  if (length(args_list$quantile_by_key) > 0L) {
+    valid_keys <- key_colnames(epi_data)
+    missing_keys <- setdiff(args_list$quantile_by_key, valid_keys)
+    if (length(missing_keys) > 0L) {
+      cli_abort(
+        c(
+          "Some {.arg quantile_by_key} columns are not key columns of the input {.cls epi_df}.",
+          "!" = "Missing: {.val {missing_keys}}.",
+          i = "Available keys: {.val {valid_keys}}."
+        ),
+        class = "epipredict__arx_forecaster__quantile_by_key_invalid"
+      )
+    }
+  }
   # forecast_date is above all what they set;
   # if they don't and they're not adjusting latency, it defaults to the max time_value
   # if they're adjusting, it defaults to the as_of
@@ -144,7 +158,7 @@ arx_fcast_epi_workflow <- function(
   forecast_date <- args_list$forecast_date %||% forecast_date_default
   target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
   if (forecast_date + args_list$ahead != target_date) {
-    cli_abort("`forecast_date` {.val {forecast_date}} + `ahead` {.val {ahead}} must equal `target_date` {.val {target_date}}.",
+    cli_abort("`forecast_date` {.val {forecast_date}} + `ahead` {.val {args_list$ahead}} must equal `target_date` {.val {target_date}}.",
       class = "epipredict__arx_forecaster__inconsistent_target_ahead_forecaste_date"
     )
   }
@@ -194,6 +208,17 @@ arx_fcast_epi_workflow <- function(
   f <- frosting() %>% layer_predict() # %>% layer_naomit()
   is_quantile_reg <- inherits(trainer, "quantile_reg") |
     (inherits(trainer, "rand_forest") & trainer$engine == "grf_quantiles")
+  if (is_quantile_reg && length(args_list$quantile_by_key) > 0L) {
+    cli_warn(
+      paste0(
+        "{.arg quantile_by_key} (set to {.val {args_list$quantile_by_key}}) ",
+        "has no effect when the trainer produces quantile distributions ",
+        "directly (e.g., {.fn quantile_reg}, {.fn rand_forest} with engine ",
+        "{.val grf_quantiles}). The argument is being ignored."
+      ),
+      class = "epipredict__arx_forecaster__quantile_by_key_ignored"
+    )
+  }
   if (is_quantile_reg) {
     # add all quantile_level to the forecaster and update postprocessor
     if (inherits(trainer, "quantile_reg")) {
diff --git a/R/autoplot.R b/R/autoplot.R
index 3295992c..08184e28 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -64,7 +64,7 @@ ggplot2::autoplot
 #'   forecast(ewf)
 #' })
 #'
-#' p <- do.call(rbind, p)
+#' p <- vctrs::vec_rbind(!!!p)
 #' autoplot(wf, p, .facet_filter = geo_value %in% c("ca", "ny", "de", "mt"))
 #'
 #' # ------- Plotting canned forecaster output
@@ -83,16 +83,31 @@ NULL
 #' @export
 #' @rdname autoplot-epipred
 autoplot.epi_workflow <- function(
-    object,
-    predictions = NULL,
-    observed_response = NULL,
-    .levels = c(.5, .8, .9), ...,
-    .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
-    .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
-    .base_color = "dodgerblue4",
-    .point_pred_color = "orange",
-    .facet_filter = NULL,
-    .max_facets = deprecated()) {
+  object,
+  predictions = NULL,
+  observed_response = NULL,
+  .levels = c(.5, .8, .9),
+  ...,
+  .color_by = c(
+    "all_keys",
+    "geo_value",
+    "other_keys",
+    ".response",
+    "all",
+    "none"
+  ),
+  .facet_by = c(
+    ".response",
+    "other_keys",
+    "all_keys",
+    "geo_value",
+    "all",
+    "none"
+  ),
+  .base_color = "dodgerblue4",
+  .point_pred_color = "orange",
+  .facet_filter = NULL
+) {
   rlang::check_dots_empty()
   arg_is_probabilities(.levels)
   rlang::arg_match(.color_by)
@@ -114,7 +129,9 @@ autoplot.epi_workflow <- function(
   keys <- c("geo_value", "time_value", "key")
   mold_roles <- names(mold$extras$roles)
   # extract the relevant column names for plotting
-  if (starts_with_impl("ahead_", names(y)) || starts_with_impl("lag_", names(y))) {
+  if (
+    starts_with_impl("ahead_", names(y)) || starts_with_impl("lag_", names(y))
+  ) {
     old_name_y <- unlist(strsplit(names(y), "_"))
     new_name_y <- paste(old_name_y[-c(1:2)], collapse = "_")
   } else {
@@ -135,20 +152,28 @@ autoplot.epi_workflow <- function(
     }
     observed_response <- rename(observed_response, !!new_name_y := !!names(y))
     if (!is.null(shift)) {
-      observed_response <- mutate(observed_response, time_value = time_value + shift)
+      observed_response <- mutate(
+        observed_response,
+        time_value = time_value + shift
+      )
     }
     other_keys <- setdiff(key_colnames(object), c("geo_value", "time_value"))
-    observed_response <- as_epi_df(observed_response,
+    observed_response <- as_epi_df(
+      observed_response,
       as_of = object$fit$meta$as_of,
       other_keys = other_keys
     )
   }
   if (is.null(predictions)) {
     return(autoplot(
-      observed_response, new_name_y,
-      .color_by = .color_by, .facet_by = .facet_by, .base_color = .base_color,
+      observed_response,
+      new_name_y,
+      .color_by = .color_by,
+      .facet_by = .facet_by,
+      .base_color = .base_color,
       .facet_filter = {{ .facet_filter }},
-      .max_facets = .max_facets
+      # Avoid subsampling while it is not implemented for this method
+      .max_keys = Inf
     ))
   }
 
@@ -158,32 +183,44 @@ autoplot.epi_workflow <- function(
     }
     predictions <- rename(predictions, time_value = target_date)
   }
-  pred_cols_ok <- hardhat::check_column_names(predictions, key_colnames(observed_response))
+  pred_cols_ok <- hardhat::check_column_names(
+    predictions,
+    key_colnames(observed_response)
+  )
   if (!pred_cols_ok$ok) {
     cli_warn(c(
       "`predictions` is missing required variables: {.var {pred_cols_ok$missing_names}}.",
       i = "Plotting the original data."
     ))
     return(autoplot(
-      observed_response, !!new_name_y,
-      .color_by = .color_by, .facet_by = .facet_by, .base_color = .base_color,
+      observed_response,
+      !!new_name_y,
+      .color_by = .color_by,
+      .facet_by = .facet_by,
+      .base_color = .base_color,
       .facet_filter = {{ .facet_filter }},
-      .max_facets = .max_facets
+      # Avoid subsampling while it is not implemented for this method
+      .max_keys = Inf
     ))
   }
 
   # First we plot the history, always faceted by everything
-  bp <- autoplot(observed_response, !!new_name_y,
-    .color_by = "none", .facet_by = "all_keys",
-    .base_color = "black", .facet_filter = {{ .facet_filter }},
-    .max_facets = .max_facets
+  bp <- autoplot(
+    observed_response,
+    !!new_name_y,
+    .color_by = "none",
+    .facet_by = "all_keys",
+    .base_color = "black",
+    .facet_filter = {{ .facet_filter }},
+    # Avoid subsampling while it is not implemented for this method
+    .max_keys = Inf
   )
 
   # Now, prepare matching facets in the predictions
   ek <- epi_keys_only(observed_response)
   predictions <- predictions %>%
     mutate(
-      .facets = interaction(!!!rlang::syms(as.list(ek)), sep = " / "),
+      .facets = interaction(!!!rlang::syms(as.list(ek)), sep = "; "),
     )
   .facet_filter <- rlang::enquo(.facet_filter)
   if (!rlang::quo_is_null(.facet_filter) && ".facets" %in% names(bp$data)) {
@@ -191,7 +228,6 @@ autoplot.epi_workflow <- function(
       mutate(.facets = droplevels(.facets))
   }
 
-
   if (".pred_distn" %in% names(predictions)) {
     bp <- plot_bands(bp, predictions, .levels, .base_color)
   }
@@ -201,13 +237,17 @@ autoplot.epi_workflow <- function(
     if (ntarget_dates > 1L) {
       bp <- bp +
         geom_line(
-          data = predictions, aes(y = .data$.pred),
+          data = predictions,
+          aes(x = .data$time_value, y = .data$.pred),
+          inherit.aes = FALSE,
           color = .point_pred_color
         )
     } else {
       bp <- bp +
         geom_point(
-          data = predictions, aes(y = .data$.pred),
+          data = predictions,
+          aes(x = .data$time_value, y = .data$.pred),
+          inherit.aes = FALSE,
           color = .point_pred_color
         )
     }
@@ -218,13 +258,29 @@ autoplot.epi_workflow <- function(
 #' @export
 #' @rdname autoplot-epipred
 autoplot.canned_epipred <- function(
-    object, observed_response = NULL, ...,
-    .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
-    .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
-    .base_color = "dodgerblue4",
-    .point_pred_color = "orange",
-    .facet_filter = NULL,
-    .max_facets = deprecated()) {
+  object,
+  observed_response = NULL,
+  ...,
+  .color_by = c(
+    "all_keys",
+    "geo_value",
+    "other_keys",
+    ".response",
+    "all",
+    "none"
+  ),
+  .facet_by = c(
+    ".response",
+    "other_keys",
+    "all_keys",
+    "geo_value",
+    "all",
+    "none"
+  ),
+  .base_color = "dodgerblue4",
+  .point_pred_color = "orange",
+  .facet_filter = NULL
+) {
   rlang::check_dots_empty()
   rlang::arg_match(.color_by)
   rlang::arg_match(.facet_by)
@@ -233,10 +289,15 @@ autoplot.canned_epipred <- function(
   predictions <- object$predictions %>%
     rename(time_value = target_date)
 
-  autoplot(ewf, predictions, observed_response, ...,
-    .color_by = .color_by, .facet_by = .facet_by,
-    .base_color = .base_color, .facet_filter = {{ .facet_filter }},
-    .max_facets = .max_facets
+  autoplot(
+    ewf,
+    predictions,
+    observed_response,
+    ...,
+    .color_by = .color_by,
+    .facet_by = .facet_by,
+    .base_color = .base_color,
+    .facet_filter = {{ .facet_filter }}
   )
 }
 
@@ -259,11 +320,13 @@ starts_with_impl <- function(x, vars) {
 }
 
 plot_bands <- function(
-    base_plot, predictions,
-    levels = c(.5, .8, .9),
-    fill = "blue4",
-    alpha = 0.6,
-    linewidth = 0.05) {
+  base_plot,
+  predictions,
+  levels = c(.5, .8, .9),
+  fill = "blue4",
+  alpha = 0.6,
+  linewidth = 0.05
+) {
   innames <- names(predictions)
   n_levels <- length(levels)
   alpha <- alpha / (n_levels - 1)
@@ -274,7 +337,9 @@ plot_bands <- function(
   ntarget_dates <- dplyr::n_distinct(predictions$time_value)
 
   predictions <- predictions %>%
-    mutate(.pred_distn = quantile_pred(quantile(.pred_distn, levels), levels)) %>%
+    mutate(
+      .pred_distn = quantile_pred(quantile(.pred_distn, levels), levels)
+    ) %>%
     pivot_quantiles_wider(.pred_distn)
   qnames <- setdiff(names(predictions), innames)
 
@@ -286,15 +351,29 @@ plot_bands <- function(
         base_plot <- base_plot +
           geom_ribbon(
             data = predictions,
-            aes(ymin = .data[[bottom]], ymax = .data[[top]]),
-            alpha = 0.2, linewidth = linewidth, fill = fill
+            aes(
+              x = .data$time_value,
+              ymin = .data[[bottom]],
+              ymax = .data[[top]]
+            ),
+            inherit.aes = FALSE,
+            alpha = 0.2,
+            linewidth = linewidth,
+            fill = fill
           )
       } else {
         base_plot <- base_plot +
           geom_linerange(
             data = predictions,
-            aes(ymin = .data[[bottom]], ymax = .data[[top]]),
-            alpha = 0.2, linewidth = 2, color = fill
+            aes(
+              x = .data$time_value,
+              ymin = .data[[bottom]],
+              ymax = .data[[top]]
+            ),
+            inherit.aes = FALSE,
+            alpha = 0.2,
+            linewidth = 2,
+            color = fill
           )
       }
     } else {
@@ -302,15 +381,28 @@ plot_bands <- function(
         base_plot <- base_plot +
           geom_ribbon(
             data = predictions,
-            aes(ymin = .data[[bottom]], ymax = .data[[top]]),
-            fill = fill, alpha = alpha
+            aes(
+              x = .data$time_value,
+              ymin = .data[[bottom]],
+              ymax = .data[[top]]
+            ),
+            inherit.aes = FALSE,
+            fill = fill,
+            alpha = alpha
           )
       } else {
         base_plot <- base_plot +
           geom_linerange(
             data = predictions,
-            aes(ymin = .data[[bottom]], ymax = .data[[top]]),
-            color = fill, alpha = alpha, linewidth = 2
+            aes(
+              x = .data$time_value,
+              ymin = .data[[bottom]],
+              ymax = .data[[top]]
+            ),
+            inherit.aes = FALSE,
+            color = fill,
+            alpha = alpha,
+            linewidth = 2
           )
       }
     }
diff --git a/R/canned-epipred.R b/R/canned-epipred.R
index 7d53862c..2683acad 100644
--- a/R/canned-epipred.R
+++ b/R/canned-epipred.R
@@ -112,7 +112,7 @@ print.canned_epipred <- function(x, name, ...) {
     "At forecast date{?s}: {.val {fds}},",
     "For target date{?s}: {.val {tds}},"
   ))
-  if ("pre" %in% names(x) && "actions" %in% names(x$pre) && "recipe" %in% names(x$pre$actions)) {
+  if (!is.null(x$epi_workflow$pre$actions$recipe)) {
     fit_recipe <- extract_recipe(x$epi_workflow)
     if (detect_step(fit_recipe, "adjust_latency")) {
       is_adj_latency <- map_lgl(fit_recipe$steps, function(x) inherits(x, "step_adjust_latency"))
diff --git a/R/cdc_baseline_forecaster.R b/R/cdc_baseline_forecaster.R
index a97eece8..34f017d6 100644
--- a/R/cdc_baseline_forecaster.R
+++ b/R/cdc_baseline_forecaster.R
@@ -9,8 +9,8 @@
 #' By default, the predictive intervals are computed separately for each
 #' combination of `geo_value` in the `epi_data` argument.
 #'
-#' This forecaster is meant to produce exactly the CDC Baseline used for
-#' [COVID19ForecastHub](https://covid19forecasthub.org)
+#' This forecaster is meant to produce exactly the CDC Baseline used for, e.g.,
+#' <https://github.com/cdcepi/Flusight-baseline/>
 #'
 #' @param epi_data An [`epiprocess::epi_df`][epiprocess::as_epi_df]
 #' @param outcome A scalar character for the column name we wish to predict.
@@ -56,9 +56,10 @@
 #'   theme_bw() +
 #'   geom_vline(xintercept = forecast_date)
 cdc_baseline_forecaster <- function(
-    epi_data,
-    outcome,
-    args_list = cdc_baseline_args_list()) {
+  epi_data,
+  outcome,
+  args_list = cdc_baseline_args_list()
+) {
   validate_forecaster_inputs(epi_data, outcome, "time_value")
   if (!inherits(args_list, c("cdc_flat_fcast", "alist"))) {
     cli_abort("`args_list` was not created using `cdc_baseline_args_list().")
@@ -67,7 +68,6 @@ cdc_baseline_forecaster <- function(
   ek <- kill_time_value(keys)
   outcome <- rlang::sym(outcome)
 
-
   r <- epi_recipe(epi_data) %>%
     step_epi_ahead(!!outcome, ahead = args_list$data_frequency, skip = TRUE) %>%
     recipes::update_role(!!outcome, new_role = "predictor") %>%
@@ -77,7 +77,6 @@ cdc_baseline_forecaster <- function(
   forecast_date <- args_list$forecast_date %||% max(epi_data$time_value)
   # target_date <- args_list$target_date %||% (forecast_date + args_list$ahead)
 
-
   latest <- get_test_data(epi_recipe(epi_data), epi_data)
 
   f <- frosting() %>%
@@ -93,7 +92,9 @@ cdc_baseline_forecaster <- function(
     layer_add_forecast_date(forecast_date = forecast_date) %>%
     layer_unnest(.pred_distn_all)
   # layer_add_target_date(target_date = target_date)
-  if (args_list$nonneg) f <- layer_threshold(f, ".pred")
+  if (args_list$nonneg) {
+    f <- layer_threshold(f, ".pred")
+  }
 
   eng <- linear_reg(engine = "flatline")
 
@@ -118,7 +119,6 @@ cdc_baseline_forecaster <- function(
 }
 
 
-
 #' CDC baseline forecaster argument constructor
 #'
 #' Constructs a list of arguments for [cdc_baseline_forecaster()].
@@ -157,16 +157,17 @@ cdc_baseline_forecaster <- function(
 #' cdc_baseline_args_list(symmetrize = FALSE)
 #' cdc_baseline_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
 cdc_baseline_args_list <- function(
-    data_frequency = "1 week",
-    aheads = 1:5,
-    n_training = Inf,
-    forecast_date = NULL,
-    quantile_levels = c(.01, .025, 1:19 / 20, .975, .99),
-    nsims = 1e5L,
-    symmetrize = TRUE,
-    nonneg = TRUE,
-    quantile_by_key = "geo_value",
-    ...) {
+  data_frequency = "1 week",
+  aheads = 1:5,
+  n_training = Inf,
+  forecast_date = NULL,
+  quantile_levels = c(.01, .025, 1:19 / 20, .975, .99),
+  nsims = 1e5L,
+  symmetrize = TRUE,
+  nonneg = TRUE,
+  quantile_by_key = "geo_value",
+  ...
+) {
   rlang::check_dots_empty()
   arg_is_scalar(n_training, nsims, data_frequency)
   data_frequency <- parse_period(data_frequency)
@@ -178,7 +179,9 @@ cdc_baseline_args_list <- function(
   arg_is_lgl(symmetrize, nonneg)
   arg_is_probabilities(quantile_levels, allow_null = TRUE)
   arg_is_pos(n_training)
-  if (is.finite(n_training)) arg_is_pos_int(n_training)
+  if (is.finite(n_training)) {
+    arg_is_pos_int(n_training)
+  }
 
   structure(
     enlist(
@@ -206,10 +209,13 @@ parse_period <- function(x) {
   arg_is_scalar(x)
   if (is.character(x)) {
     x <- unlist(strsplit(x, " "))
-    if (length(x) == 1L) x <- as.numeric(x)
+    if (length(x) == 1L) {
+      x <- as.numeric(x)
+    }
     if (length(x) == 2L) {
       mult <- substr(x[2], 1, 3)
-      mult <- switch(mult,
+      mult <- switch(
+        mult,
         day = 1L,
         wee = 7L,
         cli_abort("incompatible timespan in `aheads`.")
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index 617d703e..8cf9bc84 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -10,8 +10,8 @@
 #'   combination of key values (`geo_value` + any additional keys) in the
 #'   `epi_data` argument.
 #'
-#' This forecaster is very similar to that used by the
-#'   [COVID19ForecastHub](https://covid19forecasthub.org)
+#' This forecaster is very similar to that used as the
+#'   as [FluSight Baseline](https://github.com/cdcepi/Flusight-baseline/).
 #'
 #' @details
 #'  Here is (roughly) the code for the `flatline_forecaster()` applied to the
@@ -52,18 +52,32 @@
 #'
 #' out <- flatline_forecaster(jhu, "death_rate")
 flatline_forecaster <- function(
-    epi_data,
-    outcome,
-    args_list = flatline_args_list()) {
+  epi_data,
+  outcome,
+  args_list = flatline_args_list()
+) {
   validate_forecaster_inputs(epi_data, outcome, "time_value")
   if (!inherits(args_list, c("flat_fcast", "alist"))) {
     cli_abort("`args_list` was not created using `flatline_args_list()`.")
   }
+  if (length(args_list$quantile_by_key) > 0L) {
+    valid_keys <- key_colnames(epi_data)
+    missing_keys <- setdiff(args_list$quantile_by_key, valid_keys)
+    if (length(missing_keys) > 0L) {
+      cli_abort(
+        c(
+          "Some {.arg quantile_by_key} columns are not key columns of the input {.cls epi_df}.",
+          "!" = "Missing: {.val {missing_keys}}.",
+          i = "Available keys: {.val {valid_keys}}."
+        ),
+        class = "epipredict__flatline_forecaster__quantile_by_key_invalid"
+      )
+    }
+  }
   keys <- key_colnames(epi_data)
   ek <- kill_time_value(keys)
   outcome <- rlang::sym(outcome)
 
-
   r <- epi_recipe(epi_data) %>%
     step_epi_ahead(!!outcome, ahead = args_list$ahead, skip = TRUE) %>%
     recipes::update_role(!!outcome, new_role = "predictor") %>%
@@ -82,7 +96,9 @@ flatline_forecaster <- function(
     ) %>%
     layer_add_forecast_date(forecast_date = forecast_date) %>%
     layer_add_target_date(target_date = target_date)
-  if (args_list$nonneg) f <- layer_threshold(f, dplyr::starts_with(".pred"))
+  if (args_list$nonneg) {
+    f <- layer_threshold(f, dplyr::starts_with(".pred"))
+  }
 
   eng <- linear_reg(engine = "flatline")
 
@@ -106,7 +122,6 @@ flatline_forecaster <- function(
 }
 
 
-
 #' Flatline forecaster argument constructor
 #'
 #' Constructs a list of arguments for [flatline_forecaster()].
@@ -127,15 +142,16 @@ flatline_forecaster <- function(
 #' flatline_args_list(symmetrize = FALSE)
 #' flatline_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
 flatline_args_list <- function(
-    ahead = 7L,
-    n_training = Inf,
-    forecast_date = NULL,
-    target_date = NULL,
-    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
-    symmetrize = TRUE,
-    nonneg = TRUE,
-    quantile_by_key = character(0L),
-    ...) {
+  ahead = 7L,
+  n_training = Inf,
+  forecast_date = NULL,
+  target_date = NULL,
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
+  symmetrize = TRUE,
+  nonneg = TRUE,
+  quantile_by_key = character(0L),
+  ...
+) {
   rlang::check_dots_empty()
   arg_is_scalar(ahead, n_training)
   arg_is_chr(quantile_by_key, allow_empty = TRUE)
@@ -145,7 +161,9 @@ flatline_args_list <- function(
   arg_is_lgl(symmetrize, nonneg)
   arg_is_probabilities(quantile_levels, allow_null = TRUE)
   arg_is_pos(n_training)
-  if (is.finite(n_training)) arg_is_pos_int(n_training)
+  if (is.finite(n_training)) {
+    arg_is_pos_int(n_training)
+  }
 
   if (!is.null(forecast_date) && !is.null(target_date)) {
     if (forecast_date + ahead != target_date) {
diff --git a/R/get_test_data.R b/R/get_test_data.R
index 17430824..e5cc9516 100644
--- a/R/get_test_data.R
+++ b/R/get_test_data.R
@@ -66,13 +66,13 @@ get_test_data <- function(recipe, x) {
 
   # If we skip NA completion, we remove undesirably early time values
   # Happens globally, over all groups
-  x <- filter(x, max_time_value - time_value <= keep)
+  x <- filter(x, max_time_value - time_value <= keep, time_value <= max_time_value)
 
   # If all(lags > 0), then we get rid of recent data
   if (min_lags > 0 && min_lags < Inf) {
     x <- filter(x, max_time_value - time_value >= min_lags)
   }
 
-  filter(x, max_time_value - time_value <= keep) %>%
+  filter(x, max_time_value - time_value <= keep, time_value <= max_time_value) %>%
     epiprocess::ungroup()
 }
diff --git a/R/globals.R b/R/globals.R
new file mode 100644
index 00000000..0433300d
--- /dev/null
+++ b/R/globals.R
@@ -0,0 +1,13 @@
+# Handle "no visible binding for global variable" notes from R CMD check
+# when using tidyverse/rlang pronouns and column names in NSE.
+utils::globalVariables(c(
+  ".", ".data", ".env", ".facets", ".idx", ".pred", ".pred_distn",
+  ".pred_distn_all", ".pred_distn_quantile_level", ".pred_distn_value",
+  ".pred_quantile", ".quantile_levels", ".resid", ".weights", "abbr",
+  "ahead", "any_are_na", "cli_stop", "climate_pred", "col_name",
+  "data", "distn", "fips", "forecast_date", "geo_value", "horizon", "i",
+  "latency", "latency.bake", "latency.prep", "location", "matches",
+  "median", "number", "object", "output_type_id", "reference_date", "role",
+  "shift", "skip", "target_date", "target_end_date", "terms", "time_value",
+  "type", "value", "variable"
+))
diff --git a/R/layers.R b/R/layers.R
index b35dceaf..0e0c0997 100644
--- a/R/layers.R
+++ b/R/layers.R
@@ -122,7 +122,7 @@ reconstruct_layer <- function(x) {
 #' f <- frosting() %>% layer_predict()
 #' wf <- epi_workflow(postprocessor = f)
 #'
-#' is_layer(layer("what_the_what"))
+#' is_layer(extract_layers(f)[[1]])
 #' detect_layer(f, "layer_predict")
 #' detect_layer(wf, "layer_predict")
 #'
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index 3534b4ee..fa7cb604 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -80,8 +80,16 @@ pivot_quantiles_longer <- function(.data, ...) {
   long_tib <- as_tibble(.data[[col]])
   .data <- select(.data, !all_of(col))
   names(long_tib)[1:2] <- c(glue::glue("{col}_value"), glue::glue("{col}_quantile_level"))
-  left_join(.data, long_tib, by = ".row") %>%
-    select(!.row)
+  out <- left_join(.data, long_tib, by = ".row") %>% select(!.row)
+  if (inherits(.data, "epi_df")) {
+    # register the new quantile_level column as a key
+    out <- as_epi_df(
+      out,
+      as_of = attr(.data, "metadata")$as_of,
+      other_keys = c(attr(.data, "metadata")$other_keys, glue::glue("{col}_quantile_level"))
+    )
+  }
+  out
 }
 
 #' @rdname pivot_quantiles
diff --git a/README.Rmd b/README.Rmd
index 28ae1409..31956753 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -11,7 +11,7 @@ knitr::opts_chunk$set(
   digits = 3,
   comment = "#>",
   collapse = TRUE,
-  cache = TRUE,
+  cache = FALSE,
   dev.args = list(bg = "transparent"),
   dpi = 300,
   cache.lazy = FALSE,
@@ -51,15 +51,20 @@ tertiary <- "#177245"
 fourth_colour <- "#A393BF"
 fifth_colour <- "#2e8edd"
 colvec <- c(
-  base = base, primary = primary, secondary = secondary,
-  tertiary = tertiary, fourth_colour = fourth_colour,
+  base = base,
+  primary = primary,
+  secondary = secondary,
+  tertiary = tertiary,
+  fourth_colour = fourth_colour,
   fifth_colour = fifth_colour
 )
 library(epiprocess)
 suppressMessages(library(tidyverse))
 theme_update(legend.position = "bottom", legend.title = element_blank())
 delphi_pal <- function(n) {
-  if (n > 6L) warning("Not enough colors in this palette!")
+  if (n > 6L) {
+    warning("Not enough colors in this palette!")
+  }
   unname(colvec)[1:n]
 }
 scale_fill_delphi <- function(..., aesthetics = "fill") {
@@ -87,16 +92,17 @@ Both of the packages are meant to work well with the panel data provided by
 Pre-compiled example datasets are also available in
 [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
 
+<!--
 If you are looking for detail beyond the package documentation, see our
 [forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/).
-
+-->
 
 ## Installation
 
 Unless you’re planning on contributing to package development, we suggest using the stable version.
 To install, run:
 
-```r
+```{r echo=TRUE, eval=FALSE}
 # Stable version
 pak::pkg_install("cmu-delphi/epipredict@main")
 
@@ -105,8 +111,8 @@ pak::pkg_install("cmu-delphi/epipredict@dev")
 ```
 
 The documentation for the stable version is at
-<https://cmu-delphi.github.io/epipredict>, while the development version is at
-<https://cmu-delphi.github.io/epipredict/dev>.
+<https://cmu-delphi.github.io/epipredict/>, while the development version is at
+<https://cmu-delphi.github.io/epipredict/dev/>.
 
 
 ## Motivating example
@@ -151,7 +157,7 @@ First we pull both `jhu-csse` cases and deaths data from the
 [Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the
 [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
 
-```{r case_death, warning = FALSE, eval = TRUE}
+```{r case_death, warning = FALSE, eval = FALSE}
 cases <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
@@ -177,6 +183,15 @@ cases_deaths <-
   as_epi_df(as_of = as.Date("2022-01-01"))
 ```
 
+```{r case_death_epidatasets, echo = FALSE}
+cases_deaths <- epidatasets::covid_case_death_rates_extended |>
+  filter(
+    geo_value %in% used_locations,
+    between(time_value, ymd("2020-06-01"), ymd("2021-12-31"))
+  ) |>
+  as_epi_df(as_of = as.Date("2022-01-01"))
+```
+
 Since visualizing the results on every geography is somewhat overwhelming,
 we’ll only train on a subset of locations.
 
@@ -208,11 +223,13 @@ cases_deaths <-
   group_by(geo_value) |>
   mutate(
     outlr_death_rate = detect_outlr_rm(
-      time_value, death_rate,
+      time_value,
+      death_rate,
       detect_negatives = TRUE
     ),
     outlr_case_rate = detect_outlr_rm(
-      time_value, case_rate,
+      time_value,
+      case_rate,
       detect_negatives = TRUE
     )
   ) |>
@@ -257,7 +274,8 @@ processed_data_plot <-
   geom_text(
     data = forecast_date_label,
     aes(x = dates, label = "forecast\ndate", y = heights),
-    size = 3, hjust = "right"
+    size = 3,
+    hjust = "right"
   ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
@@ -315,7 +333,8 @@ forecast_plot <-
   geom_text(
     data = forecast_date_label %>% filter(.response_name == "death_rate"),
     aes(x = dates, label = "forecast\ndate", y = heights),
-    size = 3, hjust = "right"
+    size = 3,
+    hjust = "right"
   ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
@@ -331,7 +350,13 @@ And as a tibble of quantile level-value pairs:
 four_week_ahead$predictions |>
   select(-.pred) |>
   pivot_quantiles_longer(.pred_distn) |>
-  select(geo_value, forecast_date, target_date, quantile = .pred_distn_quantile_level, value = .pred_distn_value)
+  select(
+    geo_value,
+    forecast_date,
+    target_date,
+    quantile = .pred_distn_quantile_level,
+    value = .pred_distn_value
+  )
 ```
 
 The orange dot gives the point prediction, while the blue intervals give the
diff --git a/README.md b/README.md
index 97ee8121..0ebe963d 100644
--- a/README.md
+++ b/README.md
@@ -23,8 +23,10 @@ provided by [`{epidatr}`](https://cmu-delphi.github.io/epidatr/).
 Pre-compiled example datasets are also available in
 [`{epidatasets}`](https://cmu-delphi.github.io/epidatasets/).
 
+<!--
 If you are looking for detail beyond the package documentation, see our
 [forecasting book](https://cmu-delphi.github.io/delphi-tooling-book/).
+-->
 
 ## Installation
 
@@ -40,13 +42,15 @@ pak::pkg_install("cmu-delphi/epipredict@dev")
 ```
 
 The documentation for the stable version is at
-<https://cmu-delphi.github.io/epipredict>, while the development version
-is at <https://cmu-delphi.github.io/epipredict/dev>.
+<https://cmu-delphi.github.io/epipredict/>, while the development
+version is at <https://cmu-delphi.github.io/epipredict/dev/>.
 
 ## Motivating example
 
 <details>
+
 <summary>
+
 Required packages
 </summary>
 
@@ -81,7 +85,9 @@ data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse
 Below the fold, we pull the dataset from the epidata API and clean it.
 
 <details>
+
 <summary>
+
 Creating the dataset using `{epidatr}` and `{epiprocess}`
 </summary>
 
@@ -141,7 +147,7 @@ cases_deaths |>
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
 
-<img src="man/figures/README-date-1.png" width="90%" style="display: block; margin: auto;" />
+<img src="man/figures/README-date-1.png" alt="" width="90%" style="display: block; margin: auto;" />
 
 As with the typical dataset, we will need to do some cleaning to make it
 actually usable; we’ll use some utilities from
@@ -154,11 +160,13 @@ cases_deaths <-
   group_by(geo_value) |>
   mutate(
     outlr_death_rate = detect_outlr_rm(
-      time_value, death_rate,
+      time_value,
+      death_rate,
       detect_negatives = TRUE
     ),
     outlr_case_rate = detect_outlr_rm(
-      time_value, case_rate,
+      time_value,
+      case_rate,
       detect_negatives = TRUE
     )
   ) |>
@@ -178,7 +186,9 @@ After downloading and cleaning deaths per capita, as well as cases per
 forecast date with a vertical line:
 
 <details>
+
 <summary>
+
 Plot
 </summary>
 
@@ -207,7 +217,8 @@ processed_data_plot <-
   geom_text(
     data = forecast_date_label,
     aes(x = dates, label = "forecast\ndate", y = heights),
-    size = 3, hjust = "right"
+    size = 3,
+    hjust = "right"
   ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
@@ -215,7 +226,7 @@ processed_data_plot <-
 
 </details>
 
-<img src="man/figures/README-show-processed-data-1.png" width="90%" style="display: block; margin: auto;" />
+<img src="man/figures/README-show-processed-data-1.png" alt="" width="90%" style="display: block; margin: auto;" />
 
 To make a forecast, we will use a simple “canned” auto-regressive
 forecaster to predict the death rate four weeks into the future using
@@ -235,12 +246,12 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-05-22 11:56:44.
+#> This forecaster was fit on 2026-06-30 18:35:46.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
 #> • Time type: day,
-#> • Using data up-to-date as of: 2022-01-01.
+#> • Using data up-to-date as of: 2023-03-10.
 #> • With the last data available on 2021-08-01
 #> 
 #> ── Predictions ──────────────────────────────────────────────────────────────
@@ -263,7 +274,9 @@ Plotting the prediction intervals on the true values for our location
 subset[^2]:
 
 <details>
+
 <summary>
+
 Plot
 </summary>
 
@@ -280,7 +293,8 @@ forecast_plot <-
   geom_text(
     data = forecast_date_label %>% filter(.response_name == "death_rate"),
     aes(x = dates, label = "forecast\ndate", y = heights),
-    size = 3, hjust = "right"
+    size = 3,
+    hjust = "right"
   ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
@@ -288,7 +302,7 @@ forecast_plot <-
 
 </details>
 
-<img src="man/figures/README-show-single-forecast-1.png" width="90%" style="display: block; margin: auto;" />
+<img src="man/figures/README-show-single-forecast-1.png" alt="" width="90%" style="display: block; margin: auto;" />
 
 And as a tibble of quantile level-value pairs:
 
@@ -296,7 +310,13 @@ And as a tibble of quantile level-value pairs:
 four_week_ahead$predictions |>
   select(-.pred) |>
   pivot_quantiles_longer(.pred_distn) |>
-  select(geo_value, forecast_date, target_date, quantile = .pred_distn_quantile_level, value = .pred_distn_value)
+  select(
+    geo_value,
+    forecast_date,
+    target_date,
+    quantile = .pred_distn_quantile_level,
+    value = .pred_distn_value
+  )
 #> # A tibble: 20 × 5
 #>   geo_value forecast_date target_date quantile  value
 #>   <chr>     <date>        <date>         <dbl>  <dbl>
diff --git a/man/apply_frosting.Rd b/man/apply_frosting.Rd
index ece3261e..00361261 100644
--- a/man/apply_frosting.Rd
+++ b/man/apply_frosting.Rd
@@ -35,7 +35,7 @@ here for ease.
 \item{new_data}{a data frame containing the new predictors to preprocess
 and predict on}
 
-\item{type, opts}{forwarded (along with \code{...}) to \code{\link[=predict.model_fit]{predict.model_fit()}} and
+\item{type, opts}{forwarded (along with \code{...}) to \code{\link[parsnip:predict.model_fit]{parsnip::predict.model_fit()}} and
 \code{\link[=slather]{slather()}} for supported layers}
 }
 \description{
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index 7359c876..919894b3 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -67,8 +67,8 @@ latency is large. If this is \code{FALSE}, that warning is turned off.}
 be created using growth rates (as the predictors are) or lagged
 differences. The second case is closer to the requirements for the
 \href{https://github.com/cdcepi/Flusight-forecast-data/blob/745511c436923e1dc201dea0f4181f21a8217b52/data-experimental/README.md}{2022-23 CDC Flusight Hospitalization Experimental Target}.
-See the \href{https://cmu-delphi.github.io/delphi-tooling-book/arx-classifier.html}{Classification chapter from the forecasting book} Vignette for details of how to create a reasonable
-baseline for this case. Selecting \code{"growth_rate"} (the default) uses
+See the \code{vignette("epipredict")} for more details.
+Selecting \code{"growth_rate"} (the default) uses
 \code{\link[epiprocess:growth_rate]{epiprocess::growth_rate()}} to create the outcome using some of the
 additional arguments below. Choosing \code{"lag_difference"} instead simply
 uses the change from the value at the selected \code{horizon}.}
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index 2de338e1..bb24bab4 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -18,8 +18,7 @@
   .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
   .base_color = "dodgerblue4",
   .point_pred_color = "orange",
-  .facet_filter = NULL,
-  .max_facets = deprecated()
+  .facet_filter = NULL
 )
 
 \method{autoplot}{canned_epipred}(
@@ -30,8 +29,7 @@
   .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
   .base_color = "dodgerblue4",
   .point_pred_color = "orange",
-  .facet_filter = NULL,
-  .max_facets = deprecated()
+  .facet_filter = NULL
 )
 
 \method{plot}{epi_workflow}(x, ...)
@@ -81,8 +79,6 @@ contrasts to the typical use case which allows multiple comma-separated expressi
 which are implicitly combined with \code{&}. When multiple variables are selected
 with \code{...}, their names can be filtered in combination with other factors
 by using \code{.response_name}. See the examples below.}
-
-\item{.max_facets}{\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}}
 }
 \description{
 For a fit workflow, the training data will be displayed, the response by
@@ -125,7 +121,7 @@ p <- lapply(c(7, 14, 21, 28), function(h) {
   forecast(ewf)
 })
 
-p <- do.call(rbind, p)
+p <- vctrs::vec_rbind(!!!p)
 autoplot(wf, p, .facet_filter = geo_value \%in\% c("ca", "ny", "de", "mt"))
 
 # ------- Plotting canned forecaster output
diff --git a/man/cdc_baseline_forecaster.Rd b/man/cdc_baseline_forecaster.Rd
index c201bd99..08e741e2 100644
--- a/man/cdc_baseline_forecaster.Rd
+++ b/man/cdc_baseline_forecaster.Rd
@@ -33,8 +33,8 @@ forward over all available training data.
 By default, the predictive intervals are computed separately for each
 combination of \code{geo_value} in the \code{epi_data} argument.
 
-This forecaster is meant to produce exactly the CDC Baseline used for
-\href{https://covid19forecasthub.org}{COVID19ForecastHub}
+This forecaster is meant to produce exactly the CDC Baseline used for, e.g.,
+\url{https://github.com/cdcepi/Flusight-baseline/}
 }
 \examples{
 library(dplyr)
diff --git a/man/check_enough_data.Rd b/man/check_enough_data.Rd
index 969caa1d..62fcebcb 100644
--- a/man/check_enough_data.Rd
+++ b/man/check_enough_data.Rd
@@ -21,7 +21,7 @@ check_enough_data(
 sequence of operations for this recipe.}
 
 \item{...}{One or more selector functions to choose variables for this check.
-See \code{\link[=selections]{selections()}} for more details. You will usually want to use
+See \code{\link[recipes:selections]{recipes::selections()}} for more details. You will usually want to use
 \code{\link[recipes:has_role]{recipes::all_predictors()}} and/or \code{\link[recipes:has_role]{recipes::all_outcomes()}} here.}
 
 \item{min_observations}{The minimum number of data points required for
@@ -63,7 +63,7 @@ For checking training data, it is best to set \code{...} to be
 is best to set \code{...} to be \code{all_predictors()} only, with \code{n = 1}.
 }
 \section{tidy() results}{
-When you \code{\link[=tidy.recipe]{tidy()}} this check, a tibble with column
+When you \code{\link[recipes:tidy.recipe]{tidy()}} this check, a tibble with column
 \code{terms} (the selectors or variables selected) is returned.
 }
 
diff --git a/man/figures/README-date-1.png b/man/figures/README-date-1.png
index b66ec04c..15eb4f10 100644
Binary files a/man/figures/README-date-1.png and b/man/figures/README-date-1.png differ
diff --git a/man/figures/README-show-processed-data-1.png b/man/figures/README-show-processed-data-1.png
index e312c9fd..0f7973d7 100644
Binary files a/man/figures/README-show-processed-data-1.png and b/man/figures/README-show-processed-data-1.png differ
diff --git a/man/figures/README-show-single-forecast-1.png b/man/figures/README-show-single-forecast-1.png
index 8ec4f90a..3d880600 100644
Binary files a/man/figures/README-show-single-forecast-1.png and b/man/figures/README-show-single-forecast-1.png differ
diff --git a/man/flatline_forecaster.Rd b/man/flatline_forecaster.Rd
index cc789bac..bbe0576b 100644
--- a/man/flatline_forecaster.Rd
+++ b/man/flatline_forecaster.Rd
@@ -29,8 +29,8 @@ By default, the predictive intervals are computed separately for each
 combination of key values (\code{geo_value} + any additional keys) in the
 \code{epi_data} argument.
 
-This forecaster is very similar to that used by the
-\href{https://covid19forecasthub.org}{COVID19ForecastHub}
+This forecaster is very similar to that used as the
+as \href{https://github.com/cdcepi/Flusight-baseline/}{FluSight Baseline}.
 }
 \details{
 Here is (roughly) the code for the \code{flatline_forecaster()} applied to the
diff --git a/man/layer-processors.Rd b/man/layer-processors.Rd
index 76e230a7..8abc71b6 100644
--- a/man/layer-processors.Rd
+++ b/man/layer-processors.Rd
@@ -52,7 +52,7 @@ different layers of \code{frosting}.
 f <- frosting() \%>\% layer_predict()
 wf <- epi_workflow(postprocessor = f)
 
-is_layer(layer("what_the_what"))
+is_layer(extract_layers(f)[[1]])
 detect_layer(f, "layer_predict")
 detect_layer(wf, "layer_predict")
 
diff --git a/man/quantile_reg.Rd b/man/quantile_reg.Rd
index 31a5dd12..5d6975d4 100644
--- a/man/quantile_reg.Rd
+++ b/man/quantile_reg.Rd
@@ -39,5 +39,5 @@ ff <- rq_spec \%>\% fit(y ~ ., data = tib)
 predict(ff, new_data = tib)
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{\link[parsnip:fit]{parsnip::fit.model_spec()}}, \code{\link[parsnip:set_engine]{parsnip::set_engine()}}
 }
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index 5d401c79..2eae29ec 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -84,5 +84,5 @@ ggplot(data.frame(x = x, y = y), aes(x)) +
   ylab("y")
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{\link[parsnip:fit]{parsnip::fit.model_spec()}}, \code{\link[parsnip:set_engine]{parsnip::set_engine()}}
 }
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index b2972a8a..6be009c4 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -2,18 +2,17 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
-    0.1975426), .pred_distn = structure(list(c(0, 0.00989957999999999, 
-    0.09353595, 0.1393442, 0.18515245, 0.26878882, 0.34820911), c(0, 
-    0, 0.05739075, 0.103199, 0.14900725, 0.23264362, 0.31206391), 
-        c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 0.35793265, 
-        0.44156902, 0.52098931), c(0.21298119, 0.29240148, 0.37603785, 
-        0.4218461, 0.46765435, 0.55129072, 0.63071101), c(0.52311949, 
-        0.60253978, 0.68617615, 0.7319844, 0.77779265, 0.86142902, 
-        0.94084931), c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
-        0.32698722, 0.40640751)), quantile_levels = c(0.05, 0.1, 
-    0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+    0.1975426), .pred_distn = structure(list(quantile_values = structure(c(0, 
+    0, 0.10325949, 0.21298119, 0.52311949, 0, 0.00989957999999999, 
+    0, 0.18267978, 0.29240148, 0.60253978, 0.06809798, 0.09353595, 
+    0.05739075, 0.26631615, 0.37603785, 0.68617615, 0.15173435, 0.1393442, 
+    0.103199, 0.3121244, 0.4218461, 0.7319844, 0.1975426, 0.18515245, 
+    0.14900725, 0.35793265, 0.46765435, 0.77779265, 0.24335085, 0.26878882, 
+    0.23264362, 0.44156902, 0.55129072, 0.86142902, 0.32698722, 0.34820911, 
+    0.31206391, 0.52098931, 0.63071101, 0.94084931, 0.40640751), dim = 6:7)), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
+    "vctrs_rcrd", "vctrs_vctr")), forecast_date = structure(c(18992, 
+    18992, 18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -21,18 +20,18 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
-    0.1975426), .pred_distn = structure(list(c(0.084583345, 0.1073314, 
-    0.1292864, 0.1393442, 0.149402, 0.171357, 0.194105055), c(0.048438145, 
-    0.0711862, 0.0931412, 0.103199, 0.1132568, 0.1352118, 0.157959855
-    ), c(0.257363545, 0.2801116, 0.3020666, 0.3121244, 0.3221822, 
-    0.3441372, 0.366885255), c(0.367085245, 0.3898333, 0.4117883, 
-    0.4218461, 0.4319039, 0.4538589, 0.476606955), c(0.677223545, 
-    0.6999716, 0.7219266, 0.7319844, 0.7420422, 0.7639972, 0.786745255
-    ), c(0.142781745, 0.1655298, 0.1874848, 0.1975426, 0.2076004, 
-    0.2295554, 0.252303455)), quantile_levels = c(0.05, 0.1, 0.25, 
-    0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18993, 
+    0.1975426), .pred_distn = structure(list(quantile_values = structure(c(0.084583345, 
+    0.048438145, 0.257363545, 0.367085245, 0.677223545, 0.142781745, 
+    0.1073314, 0.0711862, 0.2801116, 0.3898333, 0.6999716, 0.1655298, 
+    0.1292864, 0.0931412, 0.3020666, 0.4117883, 0.7219266, 0.1874848, 
+    0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 0.1975426, 
+    0.149402, 0.1132568, 0.3221822, 0.4319039, 0.7420422, 0.2076004, 
+    0.171357, 0.1352118, 0.3441372, 0.4538589, 0.7639972, 0.2295554, 
+    0.194105055, 0.157959855, 0.366885255, 0.476606955, 0.786745255, 
+    0.252303455), dim = 6:7)), quantile_levels = c(0.05, 0.1, 0.25, 
+    0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_rcrd", 
+    "vctrs_vctr")), forecast_date = structure(c(18992, 18992, 18992, 
+    18992, 18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -40,18 +39,17 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
-    0.1975426), .pred_distn = structure(list(c(0, 0.00989957999999999, 
-    0.09353595, 0.1393442, 0.18515245, 0.26878882, 0.34820911), c(0, 
-    0, 0.05739075, 0.103199, 0.14900725, 0.23264362, 0.31206391), 
-        c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 0.35793265, 
-        0.44156902, 0.52098931), c(0.21298119, 0.29240148, 0.37603785, 
-        0.4218461, 0.46765435, 0.55129072, 0.63071101), c(0.52311949, 
-        0.60253978, 0.68617615, 0.7319844, 0.77779265, 0.86142902, 
-        0.94084931), c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
-        0.32698722, 0.40640751)), quantile_levels = c(0.05, 0.1, 
-    0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+    0.1975426), .pred_distn = structure(list(quantile_values = structure(c(0, 
+    0, 0.10325949, 0.21298119, 0.52311949, 0, 0.00989957999999999, 
+    0, 0.18267978, 0.29240148, 0.60253978, 0.06809798, 0.09353595, 
+    0.05739075, 0.26631615, 0.37603785, 0.68617615, 0.15173435, 0.1393442, 
+    0.103199, 0.3121244, 0.4218461, 0.7319844, 0.1975426, 0.18515245, 
+    0.14900725, 0.35793265, 0.46765435, 0.77779265, 0.24335085, 0.26878882, 
+    0.23264362, 0.44156902, 0.55129072, 0.86142902, 0.32698722, 0.34820911, 
+    0.31206391, 0.52098931, 0.63071101, 0.94084931, 0.40640751), dim = 6:7)), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
+    "vctrs_rcrd", "vctrs_vctr")), forecast_date = structure(c(18992, 
+    18992, 18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -59,18 +57,17 @@
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
-    0.1975426), .pred_distn = structure(list(c(0, 0.00989957999999999, 
-    0.09353595, 0.1393442, 0.18515245, 0.26878882, 0.34820911), c(0, 
-    0, 0.05739075, 0.103199, 0.14900725, 0.23264362, 0.31206391), 
-        c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 0.35793265, 
-        0.44156902, 0.52098931), c(0.21298119, 0.29240148, 0.37603785, 
-        0.4218461, 0.46765435, 0.55129072, 0.63071101), c(0.52311949, 
-        0.60253978, 0.68617615, 0.7319844, 0.77779265, 0.86142902, 
-        0.94084931), c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
-        0.32698722, 0.40640751)), quantile_levels = c(0.05, 0.1, 
-    0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18993, 
+    0.1975426), .pred_distn = structure(list(quantile_values = structure(c(0, 
+    0, 0.10325949, 0.21298119, 0.52311949, 0, 0.00989957999999999, 
+    0, 0.18267978, 0.29240148, 0.60253978, 0.06809798, 0.09353595, 
+    0.05739075, 0.26631615, 0.37603785, 0.68617615, 0.15173435, 0.1393442, 
+    0.103199, 0.3121244, 0.4218461, 0.7319844, 0.1975426, 0.18515245, 
+    0.14900725, 0.35793265, 0.46765435, 0.77779265, 0.24335085, 0.26878882, 
+    0.23264362, 0.44156902, 0.55129072, 0.86142902, 0.32698722, 0.34820911, 
+    0.31206391, 0.52098931, 0.63071101, 0.94084931, 0.40640751), dim = 6:7)), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
+    "vctrs_rcrd", "vctrs_vctr")), forecast_date = structure(c(18992, 
+    18992, 18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -86,152 +83,155 @@
     0.7319844, 0.7319844, 0.7319844, 0.1975426, 0.1975426, 0.1975426, 
     0.1975426, 0.1975426), ahead = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 
     3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 
-    4L, 5L, 1L, 2L, 3L, 4L, 5L), .pred_distn = structure(list(c(0, 
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-    0.309768685, 0.3567520625, 0.439580229), c(0, 0, 0, 0, 0.0335550493877939, 
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+    "vctrs_rcrd", "vctrs_vctr")), forecast_date = structure(c(18992, 
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+    "vctrs_rcrd", "vctrs_vctr")), forecast_date = structure(c(18992, 
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     0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    ), class = c("quantile_pred", "vctrs_vctr", "list")), forecast_date = structure(c(18992, 
+    ), class = c("quantile_pred", "vctrs_rcrd", "vctrs_vctr")), 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, 
@@ -610,24 +592,23 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.353013358779435, 0.648525432444877, 0.667670289394328, 
     1.1418673907239, 0.830448695683587, 0.329799431948649), .pred_distn = structure(list(
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-        0.437866891103361, 0.511789833825796)), quantile_levels = c(0.05, 
-    0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
-    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999, 
-    18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
+        quantile_values = structure(c(0.171022956902288, 0.46653503056773, 
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+        ), dim = 6:7)), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
+    0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_rcrd", "vctrs_vctr"
+    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
+    18992), class = "Date"), target_date = structure(c(18999, 18999, 
+    18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
@@ -635,23 +616,22 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645, 
     0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
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-        0.725986105412008, 0.73688554347046, 0.756977115764833, 0.779177689906517
-        ), c(0.159495080779498, 0.181695654921182, 0.201787227215555, 
-        0.212686665274007, 0.223586103332459, 0.243677675626832, 
-        0.265878249768516)), quantile_levels = c(0.05, 0.1, 0.25, 
-    0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18993, 
+        quantile_values = structure(c(0.0961118191398634, 0.0865730800114383, 
+        0.279994736572136, 0.417153993342634, 0.672794520917498, 
+        0.159495080779498, 0.118312393281548, 0.108773654153123, 
+        0.30219531071382, 0.439354567484319, 0.694995095059183, 0.181695654921182, 
+        0.13840396557592, 0.128865226447495, 0.322286883008193, 0.459446139778691, 
+        0.715086667353556, 0.201787227215555, 0.149303403634373, 
+        0.139764664505948, 0.333186321066645, 0.470345577837144, 
+        0.725986105412008, 0.212686665274007, 0.160202841692825, 
+        0.1506641025644, 0.344085759125097, 0.481245015895596, 0.73688554347046, 
+        0.223586103332459, 0.180294413987198, 0.170755674858773, 
+        0.36417733141947, 0.501336588189969, 0.756977115764833, 0.243677675626832, 
+        0.202494988128882, 0.192956249000457, 0.386377905561154, 
+        0.523537162331653, 0.779177689906517, 0.265878249768516), dim = 6:7)), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
+    "vctrs_rcrd", "vctrs_vctr")), forecast_date = structure(c(18992, 
+    18992, 18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18993, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -660,22 +640,22 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
-        c(0.136509784083987, 0.202348949370703, 0.263837900408968, 
-        0.303244704017742, 0.342651507626517, 0.404140458664782, 
-        0.469979623951498), c(0.364597933377326, 0.430437098664042, 
-        0.491926049702307, 0.531332853311081, 0.570739656919856, 
-        0.632228607958121, 0.698067773244837), c(0.422093024752224, 
-        0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754, 
-        0.689723699333019, 0.755562864619735), c(0.821955329282475, 
-        0.887794494569191, 0.949283445607456, 0.98869024921623, 1.028097052825, 
-        1.08958600386327, 1.15542516914999), c(0.628067077067884, 
-        0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414, 
-        0.895697751648679, 0.961536916935395), c(0.140160537291566, 
-        0.205999702578282, 0.267488653616547, 0.306895457225321, 
-        0.346302260834096, 0.407791211872361, 0.473630377159077)), quantile_levels = c(0.05, 
+        quantile_values = structure(c(0.136509784083987, 0.364597933377326, 
+        0.422093024752224, 0.821955329282475, 0.628067077067884, 
+        0.140160537291566, 0.202348949370703, 0.430437098664042, 
+        0.48793219003894, 0.887794494569191, 0.693906242354601, 0.205999702578282, 
+        0.263837900408968, 0.491926049702307, 0.549421141077205, 
+        0.949283445607456, 0.755395193392866, 0.267488653616547, 
+        0.303244704017742, 0.531332853311081, 0.58882794468598, 0.98869024921623, 
+        0.79480199700164, 0.306895457225321, 0.342651507626517, 0.570739656919856, 
+        0.628234748294754, 1.028097052825, 0.834208800610414, 0.346302260834096, 
+        0.404140458664782, 0.632228607958121, 0.689723699333019, 
+        1.08958600386327, 0.895697751648679, 0.407791211872361, 0.469979623951498, 
+        0.698067773244837, 0.755562864619735, 1.15542516914999, 0.961536916935395, 
+        0.473630377159077), dim = 6:7)), quantile_levels = c(0.05, 
     0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
-    18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
+    "vctrs_rcrd", "vctrs_vctr")), forecast_date = structure(c(18997, 
+    18997, 18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -684,22 +664,22 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
-        c(0.136509784083987, 0.202348949370703, 0.263837900408968, 
-        0.303244704017742, 0.342651507626517, 0.404140458664782, 
-        0.469979623951498), c(0.364597933377326, 0.430437098664042, 
-        0.491926049702307, 0.531332853311081, 0.570739656919856, 
-        0.632228607958121, 0.698067773244837), c(0.422093024752224, 
-        0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754, 
-        0.689723699333019, 0.755562864619735), c(0.821955329282475, 
-        0.887794494569191, 0.949283445607456, 0.98869024921623, 1.028097052825, 
-        1.08958600386327, 1.15542516914999), c(0.628067077067884, 
-        0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414, 
-        0.895697751648679, 0.961536916935395), c(0.140160537291566, 
-        0.205999702578282, 0.267488653616547, 0.306895457225321, 
-        0.346302260834096, 0.407791211872361, 0.473630377159077)), quantile_levels = c(0.05, 
+        quantile_values = structure(c(0.136509784083987, 0.364597933377326, 
+        0.422093024752224, 0.821955329282475, 0.628067077067884, 
+        0.140160537291566, 0.202348949370703, 0.430437098664042, 
+        0.48793219003894, 0.887794494569191, 0.693906242354601, 0.205999702578282, 
+        0.263837900408968, 0.491926049702307, 0.549421141077205, 
+        0.949283445607456, 0.755395193392866, 0.267488653616547, 
+        0.303244704017742, 0.531332853311081, 0.58882794468598, 0.98869024921623, 
+        0.79480199700164, 0.306895457225321, 0.342651507626517, 0.570739656919856, 
+        0.628234748294754, 1.028097052825, 0.834208800610414, 0.346302260834096, 
+        0.404140458664782, 0.632228607958121, 0.689723699333019, 
+        1.08958600386327, 0.895697751648679, 0.407791211872361, 0.469979623951498, 
+        0.698067773244837, 0.755562864619735, 1.15542516914999, 0.961536916935395, 
+        0.473630377159077), dim = 6:7)), quantile_levels = c(0.05, 
     0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
-    18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
+    "vctrs_rcrd", "vctrs_vctr")), forecast_date = structure(c(18997, 
+    18997, 18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -747,6 +727,7 @@
       * 56 unique geographic regions,
       * At forecast date: 2022-01-03,
       * For target date: 2022-01-10,
+      * Lags adjusted per column: case_rate=3, death_rate=3
       
 
 ---
@@ -770,6 +751,7 @@
       * 56 unique geographic regions,
       * At forecast date: 2022-01-03,
       * For target date: 2022-01-10,
+      * Aheads adjusted for death_rate=3
       
 
 # arx_classifier snapshots
@@ -843,60 +825,58 @@
     64.1929086, 67.80972325, 58.13744405, 63.10147305, 84.3449863, 
     51.8545645, 66.9333338, 65.8523132, 55.9202576, 64.1234883, 59.50597115, 
     48.9275239, 64.0481843, 63.45754255, 43.7883142, 65.37832155), 
-        .pred_distn = structure(list(c(55.01884375, 61.9936534, 76.8558811, 
-        85.8705552, 95.7238376875, 107.595355333333, 124.8294820725
-        ), c(22.4852324, 29.46004205, 44.32226975, 53.33694385, 63.1902263375, 
-        75.0617439833333, 92.2958707225), c(33.03575305, 40.0105627, 
-        54.8727904, 63.8874645, 73.7407469875, 85.6122646333333, 
-        102.8463913725), c(31.00572905, 37.9805387, 52.8427664, 61.8574405, 
-        71.7107229875, 83.5822406333333, 100.8163673725), c(36.34589965, 
-        43.3207093, 58.182937, 67.1976111, 77.0508935875, 88.9224112333333, 
-        106.1565379725), c(27.4126191, 34.38742875, 49.24965645, 
-        58.26433055, 68.1176130375, 79.9891306833333, 97.2232574225
-        ), c(59.1059257625, 65.4267824, 80.893949775, 88.512741775, 
-        98.9851218875, 111.446583986667, 128.951180125), c(26.1435490625, 
-        32.4644057, 47.931573075, 55.550365075, 66.0227451875, 78.4842072866667, 
-        95.988803425), c(36.8149959625, 43.1358526, 58.603019975, 
-        66.221811975, 76.6941920875, 89.1556541866667, 106.660250325
-        ), c(41.8898725625, 48.2107292, 63.677896575, 71.296688575, 
-        81.7690686875, 94.2305307866667, 111.735126925), c(37.4322168625, 
-        43.7530735, 59.220240875, 66.839032875, 77.3114129875, 89.7728750866667, 
-        107.277471225), c(34.2319594625, 40.5528161, 56.019983475, 
-        63.638775475, 74.1111555875, 86.5726176866667, 104.077213825
-        ), c(76.597008525, 83.6285187166667, 89.8466094166667, 96.39787945, 
-        106.5883326375, 118.488928483333, 139.8972249625), c(36.051994425, 
-        43.0835046166667, 49.3015953166667, 55.85286535, 66.0433185375, 
-        77.9439143833333, 99.3522108625), c(48.463300925, 55.4948111166667, 
-        61.7129018166667, 68.26417185, 78.4546250375, 90.3552208833333, 
-        111.7635173625), c(52.080115575, 59.1116257666667, 65.3297164666667, 
-        71.8809865, 82.0714396875, 93.9720355333333, 115.3803320125
-        ), c(42.407836375, 49.4393465666667, 55.6574372666667, 62.2087073, 
-        72.3991604875, 84.2997563333333, 105.7080528125), c(47.371865375, 
-        54.4033755666667, 60.6214662666667, 67.1727363, 77.3631894875, 
-        89.2637853333333, 110.6720818125), c(75.151683565, 78.2257355633333, 
-        82.1485257708333, 87.28053515, 96.1483782208333, 106.438732546667, 
-        113.7276053175), c(42.661261765, 45.7353137633333, 49.6581039708333, 
-        54.79011335, 63.6579564208333, 73.9483107466667, 81.2371835175
-        ), c(57.740031065, 60.8140830633333, 64.7368732708333, 69.86888265, 
-        78.7367257208333, 89.0270800466667, 96.3159528175), c(56.659010465, 
-        59.7330624633333, 63.6558526708333, 68.78786205, 77.6557051208333, 
-        87.9460594466667, 95.2349322175), c(46.726954865, 49.8010068633333, 
-        53.7237970708333, 58.85580645, 67.7236495208333, 78.0140038466667, 
-        85.3028766175), c(54.930185565, 58.0042375633333, 61.9270277708333, 
-        67.05903715, 75.9268802208333, 86.2172345466667, 93.5061073175
-        ), c(50.3425070425, 53.1286383166667, 57.7775218541667, 61.6092566, 
-        68.5981147666667, 77.2595451983333, 81.44671049), c(39.7640597925, 
-        42.5501910666667, 47.1990746041667, 51.03080935, 58.0196675166667, 
-        66.6810979483333, 70.86826324), c(54.8847201925, 57.6708514666667, 
-        62.3197350041667, 66.15146975, 73.1403279166667, 81.8017583483333, 
-        85.98892364), c(54.2940784425, 57.0802097166667, 61.7290932541667, 
-        65.560828, 72.5496861666667, 81.2111165983333, 85.39828189
-        ), c(34.6248500925, 37.4109813666667, 42.0598649041667, 45.89159965, 
-        52.8804578166667, 61.5418882483333, 65.72905354), c(56.2148574425, 
-        59.0009887166667, 63.6498722541667, 67.481607, 74.4704651666667, 
-        83.1318955983333, 87.31906089)), quantile_levels = c(0.05, 
+        .pred_distn = structure(list(quantile_values = structure(c(55.01884375, 
+        22.4852324, 33.03575305, 31.00572905, 36.34589965, 27.4126191, 
+        59.1059257625, 26.1435490625, 36.8149959625, 41.8898725625, 
+        37.4322168625, 34.2319594625, 76.597008525, 36.051994425, 
+        48.463300925, 52.080115575, 42.407836375, 47.371865375, 75.151683565, 
+        42.661261765, 57.740031065, 56.659010465, 46.726954865, 54.930185565, 
+        50.3425070425, 39.7640597925, 54.8847201925, 54.2940784425, 
+        34.6248500925, 56.2148574425, 61.9936534, 29.46004205, 40.0105627, 
+        37.9805387, 43.3207093, 34.38742875, 65.4267824, 32.4644057, 
+        43.1358526, 48.2107292, 43.7530735, 40.5528161, 83.6285187166667, 
+        43.0835046166667, 55.4948111166667, 59.1116257666667, 49.4393465666667, 
+        54.4033755666667, 78.2257355633333, 45.7353137633333, 60.8140830633333, 
+        59.7330624633333, 49.8010068633333, 58.0042375633333, 53.1286383166667, 
+        42.5501910666667, 57.6708514666667, 57.0802097166667, 37.4109813666667, 
+        59.0009887166667, 76.8558811, 44.32226975, 54.8727904, 52.8427664, 
+        58.182937, 49.24965645, 80.893949775, 47.931573075, 58.603019975, 
+        63.677896575, 59.220240875, 56.019983475, 89.8466094166667, 
+        49.3015953166667, 61.7129018166667, 65.3297164666667, 55.6574372666667, 
+        60.6214662666667, 82.1485257708333, 49.6581039708333, 64.7368732708333, 
+        63.6558526708333, 53.7237970708333, 61.9270277708333, 57.7775218541667, 
+        47.1990746041667, 62.3197350041667, 61.7290932541667, 42.0598649041667, 
+        63.6498722541667, 85.8705552, 53.33694385, 63.8874645, 61.8574405, 
+        67.1976111, 58.26433055, 88.512741775, 55.550365075, 66.221811975, 
+        71.296688575, 66.839032875, 63.638775475, 96.39787945, 55.85286535, 
+        68.26417185, 71.8809865, 62.2087073, 67.1727363, 87.28053515, 
+        54.79011335, 69.86888265, 68.78786205, 58.85580645, 67.05903715, 
+        61.6092566, 51.03080935, 66.15146975, 65.560828, 45.89159965, 
+        67.481607, 95.7238376875, 63.1902263375, 73.7407469875, 71.7107229875, 
+        77.0508935875, 68.1176130375, 98.9851218875, 66.0227451875, 
+        76.6941920875, 81.7690686875, 77.3114129875, 74.1111555875, 
+        106.5883326375, 66.0433185375, 78.4546250375, 82.0714396875, 
+        72.3991604875, 77.3631894875, 96.1483782208333, 63.6579564208333, 
+        78.7367257208333, 77.6557051208333, 67.7236495208333, 75.9268802208333, 
+        68.5981147666667, 58.0196675166667, 73.1403279166667, 72.5496861666667, 
+        52.8804578166667, 74.4704651666667, 107.595355333333, 75.0617439833333, 
+        85.6122646333333, 83.5822406333333, 88.9224112333333, 79.9891306833333, 
+        111.446583986667, 78.4842072866667, 89.1556541866667, 94.2305307866667, 
+        89.7728750866667, 86.5726176866667, 118.488928483333, 77.9439143833333, 
+        90.3552208833333, 93.9720355333333, 84.2997563333333, 89.2637853333333, 
+        106.438732546667, 73.9483107466667, 89.0270800466667, 87.9460594466667, 
+        78.0140038466667, 86.2172345466667, 77.2595451983333, 66.6810979483333, 
+        81.8017583483333, 81.2111165983333, 61.5418882483333, 83.1318955983333, 
+        124.8294820725, 92.2958707225, 102.8463913725, 100.8163673725, 
+        106.1565379725, 97.2232574225, 128.951180125, 95.988803425, 
+        106.660250325, 111.735126925, 107.277471225, 104.077213825, 
+        139.8972249625, 99.3522108625, 111.7635173625, 115.3803320125, 
+        105.7080528125, 110.6720818125, 113.7276053175, 81.2371835175, 
+        96.3159528175, 95.2349322175, 85.3028766175, 93.5061073175, 
+        81.44671049, 70.86826324, 85.98892364, 85.39828189, 65.72905354, 
+        87.31906089), dim = c(30L, 7L))), quantile_levels = c(0.05, 
         0.1, 0.25, 0.5, 0.75, 0.9, 0.95), class = c("quantile_pred", 
-        "vctrs_vctr", "list"))), row.names = c(NA, -30L), class = c("tbl_df", 
+        "vctrs_rcrd", "vctrs_vctr"))), row.names = c(NA, -30L), class = c("tbl_df", 
     "tbl", "data.frame"))
 
 ---
@@ -915,49 +895,46 @@
     ), class = "Date"), .pred = c(1782, 927.5, 577.5, 935, 635, 1321, 
     1791, 934.5, 561, 765.5, 529.5, 1435, 2153.5, 946.5, 607, 673, 
     476, 1663.5, 2486.5, 1138, 659.5, 637.5, 446.5, 2002, 3236, 1311, 
-    879.5, 666.5, 446.5, 2964), .pred_distn = structure(list(c(512, 
-    651.333333333334, 1098.45833333333, 1678, 2429.04166666667, 3110.91666666667, 
-    3621.875), c(0, 0, 365.541666666667, 927.5, 2329.29166666667, 
-    3263.83333333333, 3892.5), c(220.125, 253.166666666667, 342, 
-    540.5, 733.375, 854.166666666667, 977), c(435.375, 506.583333333333, 
-    567.875, 776, 1103.75, 1465.83333333333, 1673.125), c(312.25, 
-    377.833333333333, 453.416666666667, 584.25, 726.541666666667, 
-    1037.33333333333, 1516.5), c(149.5, 363.666666666667, 814.333333333333, 
-    1149, 2003.95833333333, 3254.66666666667, 3883.375), c(0, 450.083333333333, 
-    924.75, 1670.5, 2544.375, 3821.08333333334, 4407.25), c(0, 0, 
-    6.41666666666674, 941.5, 2380.375, 4497.16666666667, 9047.75), 
-        c(118.25, 198.583333333333, 288.916666666667, 495.75, 736.625, 
-        962, 1521.5), c(313.375, 350.166666666667, 409.083333333333, 
-        597, 860.791666666666, 1210.41666666667, 1353.25), c(206.75, 
-        258.666666666667, 330.166666666667, 478.75, 598.041666666667, 
-        734.833333333333, 926.25), c(0, 281.083333333333, 790.666666666667, 
-        1242.5, 2162.125, 3593.5, 4306.625), c(0, 121.916666666668, 
-        1184, 2179.25, 3261.875, 4614.33333333333, 5419.75), c(0, 
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+    "tbl", "data.frame"))
 
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@@ -1041,260 +1018,246 @@
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+        30210.6666666667, 28117, 13841.6666666667, 9142.33333333333, 
+        15644, 7006.66666666667, 29214.3333333333, 27642, 13335.6666666667, 
+        9179.33333333333, 15555, 6725.66666666667, 26049, 25537.3333333333, 
+        13302.6666666667, 9004.33333333333, 15162.3333333333, 6695.66666666667, 
+        24995.3333333333, 25455.3333333333, 13064.6666666667, 8785, 
+        14703.3333333333, 6255.33333333333, 24506.6666666667, 23113, 
+        12587, 7636.66666666667, 14513, 6039.33333333333, 23361.1666666667, 
+        22413, 12470, 7462.66666666667, 14080.3333333333, 5921.33333333333, 
+        23361.1666666667, 21732, 12027, 7421.66666666667, 13742.3333333333, 
+        6121.66666666667, 23361.1666666667, 21681, 11235, 7410.66666666667, 
+        13331.3333333333, 5853.66666666667, 21943, 20535, 11013, 
+        6784.66666666667, 13193.3333333333, 5712.66666666667, 21297.6666666667, 
+        19117.3333333333, 10946.6666666667, 6039.66666666667, 12910.3333333333, 
+        5124.66666666667, 20165.6666666667, 18266, 10050.3333333333, 
+        5878.66666666667, 12458, 5032.66666666667, 18812.6666666667, 
+        52943, 37913.2833333333, 14971.85, 51192.7333333333, 13200.8666666667, 
+        25060.15, 52838, 39212.3333333334, 15999.5, 52706.3333333334, 
+        13271.6666666667, 25184.5, 52746.9333333333, 40798.3833333333, 
+        16818.15, 55006.4333333333, 13076.9666666667, 25433.85, 52291.8, 
+        42384.4333333333, 17636.8, 56248.5333333333, 13119.2666666667, 
+        25971.2, 52755.95, 43759.4833333333, 18015.45, 57417.6333333333, 
+        13264.5666666667, 25945.55, 53220.1, 45134.5333333333, 18834.1, 
+        58586.7333333334, 13306.8666666667, 24836.8, 52931.75, 46358.5833333334, 
+        19212.75, 60013.3333333334, 13295.6666666667, 25051.5, 52643.4, 
+        47339.3666666666, 19591.4, 61439.9333333333, 12995.4666666667, 
+        25275, 52688.85, 32358.8166666666, 19977.2166666667, 62772.8333333333, 
+        12715.05, 25927.9166666667, 52733.9, 34023.7666666667, 20507.3666666667, 
+        63744.3333333333, 12844.2, 26772.6666666667, 51866.35, 35687.7166666666, 
+        21089.5166666666, 51192.7333333332, 13267.35, 27167.4166666667, 
+        51942.5, 37351.6666666666, 15999.5, 52619.3333333333, 13396.5, 
+        27562.1666666667, 51943.65, 38593.1166666666, 16246.2, 38273.0999999999, 
+        12298.8833333333, 30189.4166666667, 51783.8, 25362.8666666667, 
+        12628.6, 19398.1666666667, 9516.13333333334, 28098, 50277.7666666667, 
+        18303.3166666667, 11747.7666666667, 18348.5166666667, 8852.8, 
+        29565.5, 47717.1333333333, 17648.3, 10560.0666666667, 16913.2666666667, 
+        8782.4, 27669.7333333333, 42381.4666666667, 18330.8666666667, 
+        10012.9333333333, 16912.2666666667, 8114.4, 27669.7333333333, 
+        40884.4666666667, 17648.3, 10012.9333333333, 16787.2666666667, 
+        8068.4, 27669.7333333333, 36898.4666666667, 17648.3, 10012.9333333333, 
+        16787.2666666667, 8068.4, 31033, 36826.4666666667, 17648.3, 
+        10012.9333333333, 16787.2666666667, 8068.4, 31670.8, 35784.4666666667, 
+        17443.3, 9630.93333333333, 16787.2666666667, 8036.4, 31033, 
+        35309.4666666667, 17105.3, 9573.93333333333, 16698.2666666667, 
+        7913.4, 28257, 32287, 16077.6666666667, 9398.93333333333, 
+        16256.1333333333, 7883.4, 27790.7333333333, 29471.8333333333, 
+        14882.3333333333, 9005.33333333333, 15439.2666666667, 7378.46666666667, 
+        27742.7333333333, 26977, 14552.3333333333, 8200.53333333333, 
+        15178.4666666667, 6873.33333333333, 26869.7333333333, 26277, 
+        14435.3333333333, 8026.53333333333, 15007.4666666667, 6755.33333333333, 
+        26869.7333333333, 25596, 13992.3333333333, 7985.53333333333, 
+        14669.4666666667, 6755.33333333333, 26869.7333333333, 25545, 
+        13275.3333333333, 7974.53333333333, 14258.4666666667, 6487.33333333333, 
+        26372.7333333333, 24399, 13053.3333333333, 7348.53333333333, 
+        13778.4666666667, 6263.53333333333, 26243.7333333333, 21921.5333333333, 
+        12994.3333333333, 6603.53333333333, 13495.4666666667, 5675.53333333333, 
+        25111.7333333333, 19933.0666666667, 10882.4, 6580.4, 13329.8, 
+        5583.53333333333, 23952.7333333333, 54849.3, 77464.75, 24055.4, 
+        74599.425, 17771.625, 28504.575, 54064.5, 77512.5, 24553, 
+        74960.25, 17869.75, 28772.25, 54427.7, 77847.25, 24841.6, 
+        76107.575, 17702.375, 29164.925, 54173.4, 78182, 25130.2, 
+        76196.9, 17772, 29845.6, 54634.1, 78305.75, 24978.8, 76213.225, 
+        17944.625, 29963.275, 55094.8, 78429.5, 25267.4, 76229.55, 
+        18014.25, 30080.95, 54803, 78402.25, 25116, 76503.375, 18030.375, 
+        30486.625, 54511.2, 78375, 24964.6, 76777.2, 17757.5, 30892.3, 
+        54583.9, 74049.6499999998, 24890.7, 77138.025, 17443.625, 
+        31707.975, 54717.6, 75556.1, 25101.8, 77498.85, 17417.75, 
+        32676.65, 54783.025, 77061.5499999999, 25364.9, 71279.5, 
+        17685.875, 33195.325, 54863, 78567, 23867, 71700, 17660, 
+        33714, 54882.775, 80240.5749999997, 23904.1, 71758.825, 17550.65, 
+        38204.6499999997, 54741.55, 81914.1500000001, 23801.2, 71817.65, 
+        17299.3, 39082.3000000001, 52591.25, 43700.7499999999, 14690.275, 
+        28410.3749999999, 11127.4, 42471.4499999999, 49796.3, 20612.7, 
+        11782.5, 18871, 9804.8, 29791.2, 45904.3, 20967.1, 11700.5, 
+        18870, 9136.8, 29791.2, 44407.3, 18255.6, 11700.5, 18745, 
+        9090.8, 29791.2, 40421.3, 18255.6, 11700.5, 18745, 9090.8, 
+        38341.6, 40349.3, 18255.6, 11700.5, 18745, 9090.8, 38571.6, 
+        39307.3, 18050.6, 11318.5, 18745, 9058.8, 33266.4, 38832.3, 
+        17712.6, 11261.5, 18656, 8935.8, 30490.4, 35424.1, 17451.2, 
+        11086.5, 18372, 8905.8, 30028.4, 33457.4, 16487.6, 9658, 
+        16501.4, 8258.6, 29980.4, 28232, 16157.6, 8292.8, 16052, 
+        7838.8, 29107.4, 27532, 16040.6, 8118.8, 15881, 7720.8, 29107.4, 
+        26851, 15597.6, 8077.8, 15543, 7720.8, 29107.4, 26800, 14880.6, 
+        8066.8, 15132, 7452.8, 28610.4, 25654, 14658.6, 7440.8, 14652, 
+        7399.8, 28481.4, 24647, 14599.6, 6695.8, 14369, 6811.8, 27349.4, 
+        22864, 13120.6, 6683.8, 13463.8, 6719.8, 26190.4), dim = c(186L, 
+        7L))), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+    0.95), class = c("quantile_pred", "vctrs_rcrd", "vctrs_vctr"))), row.names = c(NA, 
     -186L), class = c("tbl_df", "tbl", "data.frame"))
 
diff --git a/tests/testthat/test-arx_forecaster.R b/tests/testthat/test-arx_forecaster.R
index d13e6d2e..3a14109f 100644
--- a/tests/testthat/test-arx_forecaster.R
+++ b/tests/testthat/test-arx_forecaster.R
@@ -43,3 +43,39 @@ test_that("warns if there's not enough data to predict", {
     class = "epipredict__not_enough_data"
   )
 })
+
+test_that("arx_forecaster errors on invalid quantile_by_key columns (issue #229)", {
+  jhu <- epidatasets::covid_case_death_rates
+  expect_error(
+    arx_forecaster(jhu, "death_rate", c("death_rate"),
+      args_list = arx_args_list(quantile_by_key = "nonexistent_column")
+    ),
+    class = "epipredict__arx_forecaster__quantile_by_key_invalid"
+  )
+})
+
+test_that("arx_forecaster warns when quantile_by_key is used with quantile_reg trainer (issue #229)", {
+  jhu <- epidatasets::covid_case_death_rates
+  expect_warning(
+    arx_forecaster(jhu, "death_rate", c("death_rate"),
+      trainer = quantile_reg(),
+      args_list = arx_args_list(quantile_by_key = "geo_value")
+    ),
+    class = "epipredict__arx_forecaster__quantile_by_key_ignored"
+  )
+})
+
+test_that("arx_forecaster errors with documented class when forecast_date + ahead != target_date (issue #473)", {
+  df <- tibble(
+    geo_value = "ri",
+    time_value = seq.Date(as.Date("2026-01-01"), as.Date("2026-01-31"), by = "day"),
+    value = 0
+  ) %>%
+    as_epi_df(as_of = as.Date("2026-02-10"))
+  expect_error(
+    arx_forecaster(df, "value",
+      args_list = arx_args_list(target_date = as.Date("2026-02-17"))
+    ),
+    class = "epipredict__arx_forecaster__inconsistent_target_ahead_forecaste_date"
+  )
+})
diff --git a/tests/testthat/test-autoplot.R b/tests/testthat/test-autoplot.R
new file mode 100644
index 00000000..3de90989
--- /dev/null
+++ b/tests/testthat/test-autoplot.R
@@ -0,0 +1,51 @@
+
+train_data <- epidatasets::cases_deaths_subset
+
+test_that("autoplot renders a single-target-date forecast (geom_linerange band)", {
+  fc <- arx_forecaster(
+    train_data,
+    "death_rate_7d_av",
+    c("death_rate_7d_av", "case_rate_7d_av"),
+    args_list = arx_args_list(ahead = 7L, quantile_levels = c(.1, .5, .9))
+  )
+  p <- autoplot(fc, observed_response = train_data)
+  expect_s3_class(p, "ggplot")
+  expect_silent(ggplot2::ggplot_build(p))
+})
+
+test_that("autoplot renders a multi-target-date forecast (geom_ribbon band)", {
+
+  forecast_date <- as.Date("2021-08-01")
+
+  # arx_forecaster only takes a scalar `ahead`, so a real multi-horizon
+  # forecast means forecasting each ahead separately and combining them.
+  all_canned_results <- lapply(
+    seq(0, 28),
+    \(days_ahead) {
+      arx_forecaster(
+        train_data |>
+          filter(time_value <= forecast_date),
+        outcome = "death_rate_7d_av",
+        predictors = c("case_rate_7d_av", "death_rate_7d_av"),
+        trainer = quantile_reg(),
+        args_list = arx_args_list(
+          lags = list(c(0, 1, 2, 3, 7, 14), c(0, 7, 14)),
+          ahead = days_ahead
+        )
+      )
+    }
+  )
+  # pull out the workflow and the predictions to be able to use autoplot
+  workflow <- all_canned_results[[1]]$epi_workflow
+  results <- all_canned_results |>
+    purrr::map(~ `$`(., "predictions")) |>
+    purrr::list_rbind()
+  p <- autoplot(
+    object = workflow,
+    predictions = results,
+    observed_response = train_data |>
+      filter(time_value > "2021-07-01")
+  )
+  expect_s3_class(p, "ggplot")
+  expect_silent(ggplot2::ggplot_build(p))
+})
diff --git a/tests/testthat/test-flatline_forecaster.R b/tests/testthat/test-flatline_forecaster.R
new file mode 100644
index 00000000..d6e73147
--- /dev/null
+++ b/tests/testthat/test-flatline_forecaster.R
@@ -0,0 +1,32 @@
+test_that("flatline_forecaster returns one prediction per geo with trailing NAs (issue #454)", {
+  jhu <- epidatasets::covid_case_death_rates %>%
+    dplyr::filter(time_value >= as.Date("2021-11-01"))
+  geos <- unique(jhu$geo_value)
+  one_day <- tibble::tibble(
+    geo_value = geos,
+    time_value = as.Date("2022-01-01"),
+    case_rate = NA_real_,
+    death_rate = 0
+  )
+  two_day <- one_day %>% dplyr::mutate(time_value = as.Date("2022-01-02"))
+  jhu_nad <- jhu %>%
+    tibble::as_tibble() %>%
+    dplyr::bind_rows(one_day, two_day) %>%
+    epiprocess::as_epi_df()
+
+  res <- flatline_forecaster(jhu_nad, "case_rate")
+
+  expect_equal(nrow(res$predictions), length(geos))
+  counts <- res$predictions %>% dplyr::count(geo_value, target_date)
+  expect_true(all(counts$n == 1L))
+})
+
+test_that("flatline_forecaster errors on invalid quantile_by_key columns (issue #229)", {
+  jhu <- epidatasets::covid_case_death_rates
+  expect_error(
+    flatline_forecaster(jhu, "death_rate",
+      flatline_args_list(quantile_by_key = "nonexistent_column")
+    ),
+    class = "epipredict__flatline_forecaster__quantile_by_key_invalid"
+  )
+})
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index d32c1649..b8253286 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -45,6 +45,11 @@ test_that("quantile pivotting longer behaves", {
   expect_length(pivot_quantiles_longer(tib, d1), 4L)
   expect_identical(nrow(pivot_quantiles_longer(tib, d1)), 6L)
   expect_identical(pivot_quantiles_longer(tib, d1)$d1_value, c(1:3, 2:4))
+
+  # add quantile_level to epi_df other_keys, if epi_df
+  tib <- tibble(geo_value = c("a", "b"), time_value = as.Date(c("2021-01-01", "2021-01-02")), d1 = d1, d2 = d2)
+  epi_df <- tib %>% as_epi_df() %>% pivot_quantiles_longer(d1)
+  expect_equal(key_colnames(epi_df), c("geo_value","d1_quantile_level", "time_value"))
 })
 
 test_that("nested_quantiles is deprecated, but works where possible", {
diff --git a/tests/testthat/test-snapshots.R b/tests/testthat/test-snapshots.R
index eb0439c1..e559ff48 100644
--- a/tests/testthat/test-snapshots.R
+++ b/tests/testthat/test-snapshots.R
@@ -132,6 +132,10 @@ test_that("arx_forecaster output format snapshots", {
   expect_equal(as.Date(format(out2$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
   out2$metadata$forecast_created <- as.Date("1999-01-01")
   expect_snapshot(out2)
+  expect_match(
+    paste(testthat::capture_messages(print(out2)), collapse = ""),
+    "adjusted"
+  )
   out3 <- arx_forecaster(jhu, "death_rate",
     c("case_rate", "death_rate"),
     trainer = quantile_reg(),
@@ -143,6 +147,10 @@ test_that("arx_forecaster output format snapshots", {
   expect_equal(as.Date(format(out3$metadata$forecast_created, "%Y-%m-%d")), Sys.Date())
   out3$metadata$forecast_created <- as.Date("1999-01-01")
   expect_snapshot(out3)
+  expect_match(
+    paste(testthat::capture_messages(print(out3)), collapse = ""),
+    "adjusted"
+  )
 })
 
 test_that("arx_classifier snapshots", {
diff --git a/vignettes/backtesting.Rmd b/vignettes/backtesting.Rmd
index f355f551..4039f80a 100644
--- a/vignettes/backtesting.Rmd
+++ b/vignettes/backtesting.Rmd
@@ -24,7 +24,7 @@ new data is constantly being collected and added to the dataset, and old data po
 Training and making
 predictions only on finalized data can lead to overly optimistic estimates of accuracy
 (see, for example, [McDonald et al.
-2021](https://www.pnas.org/content/118/51/e2111453118/) and the references
+2021](https://doi.org/10.1073/pnas.2111453118) and the references
 therein).
 
 In the `{epiprocess}` package, we provide the function `epix_slide()` to help conviently perform version-faithful forecasting by only using the data as
@@ -95,7 +95,8 @@ geo_choose <- "ca"
 forecast_dates <- seq(
   from = as.Date("2020-08-01"),
   to = as.Date("2021-11-01"),
-  by = "1 month")
+  by = "1 month"
+)
 percent_cli_data <- bind_rows(
   # Snapshotted data for the version-faithful forecasts
   map(
@@ -110,18 +111,22 @@ percent_cli_data <- bind_rows(
   doctor_visits |>
     epix_as_of(doctor_visits$versions_end) |>
     mutate(version_faithful = "Version un-faithful")
-) |> as_tibble()
+) |>
+  as_tibble()
 p0 <- autoplot(
-  archive_cases_dv_subset, percent_cli, 
-  .versions = forecast_dates, 
+  archive_cases_dv_subset,
+  percent_cli,
+  .versions = forecast_dates,
   .mark_versions = TRUE,
   .facet_filter = (geo_value == "ca")
 ) +
   scale_x_date(minor_breaks = "month", date_labels = "%b %Y") +
-  labs(x = "", y = "% of doctor's visits with\n Covid-like illness") + 
+  labs(x = "", y = "% of doctor's visits with\n Covid-like illness") +
   scale_color_viridis_c(
     option = "viridis",
-    guide = guide_legend(reverse=TRUE), direction = -1) +
+    guide = guide_legend(reverse = TRUE),
+    direction = -1
+  ) +
   scale_y_continuous(limits = c(0, NA), expand = expansion(c(0, 0.05))) +
   theme(legend.position = "none")
 ```
@@ -246,21 +251,29 @@ We will also create the helper function `forecast_wrapper()` to let us easily ma
 
 ```{r arx-kweek-preliminaries, warning = FALSE}
 forecast_wrapper <- function(
-    epi_data, aheads, outcome, predictors, process_data = identity
-    ) {
+  epi_data,
+  aheads,
+  outcome,
+  predictors,
+  process_data = identity
+) {
   map(
     aheads,
     \(ahead) {
       arx_forecaster(
-        process_data(epi_data), outcome, predictors,
+        process_data(epi_data),
+        outcome,
+        predictors,
         args_list = arx_args_list(
           ahead = ahead,
           lags = c(0:7, 14, 21),
           adjust_latency = "extend_ahead"
         )
-      )$predictions |> pivot_quantiles_wider(.pred_distn)
+      )$predictions |>
+        pivot_quantiles_wider(.pred_distn)
     }
-  ) |> bind_rows()
+  ) |>
+    bind_rows()
 }
 ```
 
@@ -332,11 +345,14 @@ plotting_data <- bind_rows(
     mutate(version_faithful = "Version faithful")
 )
 
-p1 <- ggplot(data = forecasts_filtered,
-             aes(x = target_date, group = time_value)) +
+p1 <- ggplot(
+  data = forecasts_filtered,
+  aes(x = target_date, group = time_value)
+) +
   geom_ribbon(
     aes(ymin = `0.05`, ymax = `0.95`, fill = (time_value)),
-    alpha = 0.4) +
+    alpha = 0.4
+  ) +
   geom_line(aes(y = .pred, color = (time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = (time_value)), size = 0.75) +
   # the forecast date
@@ -351,13 +367,16 @@ p1 <- ggplot(data = forecasts_filtered,
   geom_line(
     data = plotting_data |> filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = (version), group = version),
-    inherit.aes = FALSE, na.rm = TRUE
+    inherit.aes = FALSE,
+    na.rm = TRUE
   ) +
   facet_grid(version_faithful ~ geo_value, scales = "free") +
   scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
-  labs(x = "Date",
-       y = "smoothed, day of week adjusted covid-like doctors visits") +
+  labs(
+    x = "Date",
+    y = "smoothed, day of week adjusted covid-like doctors visits"
+  ) +
   scale_color_viridis_c(option = "viridis", direction = -1) +
   scale_fill_viridis_c(option = "viridis", direction = -1) +
   theme(legend.position = "none")
@@ -372,12 +391,17 @@ forecasts_filtered <- forecasts |>
 forecasts_filtered %>% names
 p2 <-
   ggplot(data = forecasts_filtered, aes(x = target_date, group = time_value)) +
-  geom_ribbon(aes(ymin = `0.05`, ymax = `0.95`, fill = (time_value)), alpha = 0.4) +
+  geom_ribbon(
+    aes(ymin = `0.05`, ymax = `0.95`, fill = (time_value)),
+    alpha = 0.4
+  ) +
   geom_line(aes(y = .pred, color = (time_value)), linetype = 2L) +
   geom_point(aes(y = .pred, color = (time_value)), size = 0.75) +
   # the forecast date
   geom_vline(
-    data = percent_cli_data |> filter(geo_value == geo_choose) |> select(-version_faithful),
+    data = percent_cli_data |>
+      filter(geo_value == geo_choose) |>
+      select(-version_faithful),
     aes(color = version, xintercept = version, group = version),
     lty = 2
   ) +
@@ -385,12 +409,16 @@ p2 <-
   geom_line(
     data = plotting_data |> filter(geo_value == geo_choose),
     aes(x = time_value, y = percent_cli, color = (version), group = version),
-    inherit.aes = FALSE, na.rm = TRUE
+    inherit.aes = FALSE,
+    na.rm = TRUE
   ) +
   facet_grid(version_faithful ~ geo_value, scales = "free") +
   scale_x_date(breaks = "2 months", date_labels = "%b %Y") +
   scale_y_continuous(expand = expansion(c(0, 0.05))) +
-  labs(x = "Date", y = "smoothed, day of week adjusted covid-like doctors visits") +
+  labs(
+    x = "Date",
+    y = "smoothed, day of week adjusted covid-like doctors visits"
+  ) +
   scale_color_viridis_c(option = "viridis", direction = -1) +
   scale_fill_viridis_c(option = "viridis", direction = -1) +
   theme(legend.position = "none")
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 3786bc35..ef9a36c6 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -272,14 +272,15 @@ results <- all_flatlines |>
 autoplot(
   object = workflow,
   predictions = results,
-  observed_response = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
+  observed_response = covid_case_death_rates |>
+    filter(geo_value %in% used_locations, time_value > "2021-07-01")
 )
 ```
 
 ### `cdc_baseline_forecaster()`
 
 This is a different method of generating a flatline forecast, used as a baseline
-for [the CDC COVID-19 Forecasting Hub](https://covid19forecasthub.org).
+for various CDC forecasting challenges, e.g., <https://github.com/cdcepi/Flusight-baseline/>.
 
 ```{r make-cdc-forecast, warning=FALSE}
 all_cdc_flatline <-
@@ -298,7 +299,8 @@ results <- all_cdc_flatline$predictions
 autoplot(
   object = workflow,
   predictions = results,
-  observed_response = covid_case_death_rates |> filter(geo_value %in% used_locations, time_value > "2021-07-01")
+  observed_response = covid_case_death_rates |>
+    filter(geo_value %in% used_locations, time_value > "2021-07-01")
 )
 ```
 
@@ -327,14 +329,15 @@ We'll predict the 2023/24 season using all previous data, including 2020-2022, t
 ```{r make-climatological-forecast, warning=FALSE}
 fluview_hhs <- pub_fluview(
   regions = paste0("hhs", 1:10),
-  epiweeks = epirange(100001,222201)
+  epiweeks = epirange(100001, 222201)
 )
 fluview <- fluview_hhs %>%
   select(
     geo_value = region,
     time_value = epiweek,
     issue,
-    ili) %>%
+    ili
+  ) %>%
   as_epi_archive() %>%
   epix_as_of_current()
 
@@ -451,7 +454,8 @@ autoplot(
   grad_forecast$epi_workflow,
   grad_forecast$predictions,
   observed_response = grad_employ,
-) + geom_vline(aes(xintercept = 2016))
+) +
+  geom_vline(aes(xintercept = 2016))
 ```
 
 The 8 graphs represent all combinations of the `geo_values` (`"Quebec"` and `"British Columbia"`), `edu_quals` (`"Undergraduate degree"` and `"Professional degree"`), and age brackets (`"15 to 34 years"` and `"35 to 64 years"`).