wip: rework slide window args

DESCRIPTION

@@ -31,7 +31,6 @@ Imports:
     data.table,
     dplyr (>= 1.0.8),
     genlasso,
-    glue,
     ggplot2,
     glue,
     lifecycle (>= 1.0.1),

R/autoplot.R

@@ -47,8 +47,8 @@ autoplot.epi_df <- function(
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
     .base_color = "#3A448F",
     .max_facets = Inf) {
-  .color_by <- match.arg(.color_by)
-  .facet_by <- match.arg(.facet_by)
+  .color_by <- rlang::arg_match(.color_by)
+  .facet_by <- rlang::arg_match(.facet_by)
 
   assert(anyInfinite(.max_facets), checkInt(.max_facets), combine = "or")
   assert_character(.base_color, len = 1)

R/correlation.R

@@ -99,7 +99,7 @@ epi_cor <- function(x, var1, var2, dt1 = 0, dt2 = 0, shift_by = geo_value, # nol
   shift_by <- syms(names(eval_select(enquo(shift_by), x)))
 
   # Which method?
-  method <- match.arg(method)
+  method <- rlang::arg_match(method)
 
   # Perform time shifts, then compute appropriate correlations and return
   return(x %>%

R/growth_rate.R

@@ -120,7 +120,7 @@ growth_rate <- function(x = seq_along(y), y, x0 = x,
   # Check x, y, x0
   if (length(x) != length(y)) cli_abort("`x` and `y` must have the same length.")
   if (!all(x0 %in% x)) cli_abort("`x0` must be a subset of `x`.")
-  method <- match.arg(method)
+  method <- rlang::arg_match(method)
 
   # Arrange in increasing order of x
   o <- order(x)

R/outliers.R

@@ -89,7 +89,7 @@ detect_outlr <- function(x = seq_along(y), y,
                          ),
                          combiner = c("median", "mean", "none")) {
   # Validate combiner
-  combiner <- match.arg(combiner)
+  combiner <- rlang::arg_match(combiner)
 
   # Validate that x contains all distinct values
   if (any(duplicated(x))) {
@@ -189,7 +189,7 @@ detect_outlr_rm <- function(x = seq_along(y), y, n = 21,
 
   # Calculate lower and upper thresholds and replacement value
   z <- z %>%
-    epi_slide(fitted = median(y), before = floor((n - 1) / 2), after = ceiling((n - 1) / 2)) %>%
+    epi_slide(fitted = median(y), .window_size = n, .align = "center") %>%
     dplyr::mutate(resid = y - fitted) %>%
     roll_iqr(
       n = n,
@@ -360,8 +360,7 @@ roll_iqr <- function(z, n, detection_multiplier, min_radius,
   z %>%
     epi_slide(
       roll_iqr = stats::IQR(resid),
-      before = floor((n - 1) / 2),
-      after = ceiling((n - 1) / 2)
+      .window_size = n, .align = "center"
     ) %>%
     dplyr::mutate(
       lower = pmax(