Copyediting, roxygen link styling, formatting

Author: brookslogan

R/cdc_baseline_forecaster.R

@@ -12,13 +12,13 @@
 #' This forecaster is meant to produce exactly the CDC Baseline used for
 #' [COVID19ForecastHub](https://covid19forecasthub.org)
 #'
-#' @param epi_data An [epiprocess::epi_df]
+#' @param epi_data An [`epiprocess::epi_df`]
 #' @param outcome A scalar character for the column name we wish to predict.
 #' @param args_list A list of additional arguments as created by the
 #'   [cdc_baseline_args_list()] constructor function.
 #'
-#' @return A data frame of point and interval forecasts at for all
-#'   aheads (unique horizons) for each unique combination of `key_vars`.
+#' @return A data frame of point and interval forecasts for all aheads (unique
+#'   horizons) for each unique combination of `key_vars`.
 #' @export
 #'
 #' @examples

R/layer_cdc_flatline_quantiles.R

@@ -22,15 +22,15 @@
 #' @inheritParams layer_residual_quantiles
 #' @param aheads Numeric vector of desired forecast horizons. These should be
 #'   given in the "units of the training data". So, for example, for data
-#'   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`.
+#'   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 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.
-#'   These samples are spaced evenly on the (0, 1) scale, F_X(x) resulting
-#'   in linear interpolation on the X scale. This is achieved with
+#'   These samples are spaced evenly on the (0, 1) scale, F_X(x) resulting in
+#'   linear interpolation on the X scale. This is achieved with
 #'   [stats::quantile()] Type 7 (the default for that function).
 #' @param symmetrize Logical. If `TRUE`, does two things: (i) forces the
 #'   "empirical" CDF of residuals to be symmetric by pretending that for every
@@ -223,7 +223,7 @@ slather.layer_cdc_flatline_quantiles <-
     res <- dplyr::left_join(p, r, by = avail_grps) %>%
       dplyr::rowwise() %>%
       dplyr::mutate(
-        .pred_distn_all = propogate_samples(
+        .pred_distn_all = propagate_samples(
           .resid, .pred, object$quantile_levels,
           object$aheads, object$nsim, object$symmetrize, object$nonneg
         )
@@ -239,7 +239,7 @@ slather.layer_cdc_flatline_quantiles <-
     components
   }
 
-propogate_samples <- function(
+propagate_samples <- function(
     r, p, quantile_levels, aheads, nsim, symmetrize, nonneg) {
   max_ahead <- max(aheads)
   if (symmetrize) {

man/cdc_baseline_forecaster.Rd

@@ -1,4 +1,4 @@
-test_that("propogate_samples", {
+test_that("propagate_samples", {
   r <- -30:50
   p <- 40
   quantiles <- 1:9 / 10