local checks pass

Author: dajmcdon

.Rbuildignore

@@ -12,3 +12,4 @@
 ^musings$
 ^data-raw$
 ^vignettes/articles$
+^.git-blame-ignore-revs$

NAMESPACE

@@ -186,6 +186,7 @@ importFrom(rlang,caller_env)
 importFrom(rlang,is_empty)
 importFrom(rlang,is_null)
 importFrom(rlang,quos)
+importFrom(smoothqr,smooth_qr)
 importFrom(stats,as.formula)
 importFrom(stats,family)
 importFrom(stats,lm)

R/cdc_baseline_forecaster.R

@@ -33,7 +33,7 @@
 #'   ungroup() %>%
 #'   filter(weekdays(time_value) == "Saturday")
 #'
-#' cdc <- cdc_baseline_forecaster(deaths, "deaths")
+#' cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths")
 #' preds <- pivot_quantiles(cdc$predictions, .pred_distn)
 #'
 #' if (require(ggplot2)) {
@@ -46,7 +46,7 @@
 #'   geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
 #'   geom_line(aes(y = .pred), color = "orange") +
 #'   geom_line(
-#'     data = deaths %>% filter(geo_value %in% four_states),
+#'     data = weekly_deaths %>% filter(geo_value %in% four_states),
 #'     aes(x = time_value, y = deaths)
 #'   ) +
 #'   scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
@@ -150,15 +150,15 @@ cdc_baseline_forecaster <- function(
 #' @param nonneg Logical. Force all predictive intervals be non-negative.
 #'   Because non-negativity is forced _before_ propagating forward, this
 #'   has slightly different behaviour than would occur if using
-#'   [layer_threshold_preds()].
+#'   [layer_threshold()].
 #'
 #' @return A list containing updated parameter choices with class `cdc_flat_fcast`.
 #' @export
 #'
 #' @examples
 #' cdc_baseline_args_list()
 #' cdc_baseline_args_list(symmetrize = FALSE)
-#' cdc_baseline_args_list(levels = c(.1, .3, .7, .9), n_training = 120)
+#' cdc_baseline_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
 cdc_baseline_args_list <- function(
     data_frequency = "1 week",
     aheads = 1:4,

R/layer_cdc_flatline_quantiles.R

@@ -25,7 +25,7 @@
 #'   typically observed daily (possibly with missing values), but
 #'   with weekly forecast targets, you would use `c(7, 14, 21, 28)`. But with
 #'   weekly data, you would use `1:4`.
-#' @param quantiles Numeric vector of probabilities with values in (0,1)
+#' @param quantile_levels Numeric vector of probabilities with values in (0,1)
 #'   referring to the desired predictive intervals. The default is the standard
 #'   set for the COVID Forecast Hub.
 #' @param nsims Positive integer. The number of draws from the empirical CDF.
@@ -35,7 +35,7 @@
 #' @param nonneg Logical. Force all predictive intervals be non-negative.
 #'   Because non-negativity is forced _before_ propagating forward, this
 #'   has slightly different behaviour than would occur if using
-#'   [layer_threshold_preds()].
+#'   [layer_threshold()].
 #'
 #' @return an updated `frosting` postprocessor. Calling [predict()] will result
 #'   in an additional `<list-col>` named `.pred_distn_all` containing 2-column

R/make_smooth_quantile_reg.R

@@ -21,7 +21,7 @@
 #'
 #' @seealso [fit.model_spec()], [set_engine()]
 #'
-#' @importFrom quantreg rq
+#' @importFrom smoothqr smooth_qr
 #' @examples
 #' tib <- data.frame(
 #'   y1 = rnorm(100), y2 = rnorm(100), y3 = rnorm(100),