From 6e76e2b379b107f72fd3c88e8b9f1289d02bf4a1 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 30 Sep 2024 16:12:09 -0700
Subject: [PATCH 01/22] skeleton

---
 DESCRIPTION                          |   2 +-
 NAMESPACE                            |  24 +--
 R/dist_quantiles.R                   | 259 ++++-----------------------
 man/dist_quantiles.Rd                |  37 ----
 tests/testthat/test-dist_quantiles.R |  43 ++---
 5 files changed, 53 insertions(+), 312 deletions(-)
 delete mode 100644 man/dist_quantiles.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 9c6f6b38a..9746d81da 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -36,7 +36,7 @@ Imports:
     generics,
     ggplot2,
     glue,
-    hardhat (>= 1.3.0),
+    hardhat (>= 1.4.0.9002),
     lifecycle,
     lubridate,
     magrittr,
diff --git a/NAMESPACE b/NAMESPACE
index 076714517..4ce906754 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,8 +2,6 @@
 
 S3method(Add_model,epi_workflow)
 S3method(Add_model,workflow)
-S3method(Math,dist_quantiles)
-S3method(Ops,dist_quantiles)
 S3method(Remove_model,epi_workflow)
 S3method(Remove_model,workflow)
 S3method(Update_model,epi_workflow)
@@ -48,13 +46,9 @@ S3method(fit,epi_workflow)
 S3method(flusight_hub_formatter,canned_epipred)
 S3method(flusight_hub_formatter,data.frame)
 S3method(forecast,epi_workflow)
-S3method(format,dist_quantiles)
-S3method(is.na,dist_quantiles)
-S3method(is.na,distribution)
 S3method(key_colnames,epi_workflow)
 S3method(key_colnames,recipe)
-S3method(mean,dist_quantiles)
-S3method(median,dist_quantiles)
+S3method(mean,quantile_pred)
 S3method(predict,epi_workflow)
 S3method(predict,flatline)
 S3method(prep,check_enough_train_data)
@@ -101,7 +95,7 @@ S3method(print,step_lag_difference)
 S3method(print,step_naomit)
 S3method(print,step_population_scaling)
 S3method(print,step_training_window)
-S3method(quantile,dist_quantiles)
+S3method(quantile,quantile_pred)
 S3method(refresh_blueprint,default_epi_recipe_blueprint)
 S3method(residuals,flatline)
 S3method(run_mold,default_epi_recipe_blueprint)
@@ -125,8 +119,6 @@ S3method(tidy,check_enough_train_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
 S3method(update,layer)
-S3method(vec_ptype_abbr,dist_quantiles)
-S3method(vec_ptype_full,dist_quantiles)
 S3method(weighted_interval_score,default)
 S3method(weighted_interval_score,dist_default)
 S3method(weighted_interval_score,dist_quantiles)
@@ -160,7 +152,6 @@ export(climate_args_list)
 export(climatological_forecaster)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
-export(dist_quantiles)
 export(epi_recipe)
 export(epi_recipe_blueprint)
 export(epi_workflow)
@@ -230,7 +221,6 @@ export(update_frosting)
 export(update_model)
 export(validate_layer)
 export(weighted_interval_score)
-import(distributional)
 import(epidatasets)
 import(epiprocess)
 import(parsnip)
@@ -325,14 +315,11 @@ importFrom(rlang,list2)
 importFrom(rlang,set_names)
 importFrom(rlang,sym)
 importFrom(stats,as.formula)
-importFrom(stats,family)
 importFrom(stats,lm)
-importFrom(stats,median)
 importFrom(stats,model.frame)
 importFrom(stats,na.omit)
 importFrom(stats,poly)
 importFrom(stats,predict)
-importFrom(stats,qnorm)
 importFrom(stats,quantile)
 importFrom(stats,residuals)
 importFrom(tibble,as_tibble)
@@ -346,13 +333,6 @@ importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)
-importFrom(vctrs,as_list_of)
-importFrom(vctrs,field)
-importFrom(vctrs,new_rcrd)
-importFrom(vctrs,new_vctr)
 importFrom(vctrs,vec_cast)
 importFrom(vctrs,vec_data)
-importFrom(vctrs,vec_ptype_abbr)
-importFrom(vctrs,vec_ptype_full)
-importFrom(vctrs,vec_recycle_common)
 importFrom(workflows,extract_preprocessor)
diff --git a/R/dist_quantiles.R b/R/dist_quantiles.R
index d40a1d481..db8e62d10 100644
--- a/R/dist_quantiles.R
+++ b/R/dist_quantiles.R
@@ -1,209 +1,68 @@
-#' @importFrom vctrs field vec_cast new_rcrd
-new_quantiles <- function(values = double(1), quantile_levels = double(1)) {
-  arg_is_probabilities(quantile_levels)
-
-  vec_cast(values, double())
-  vec_cast(quantile_levels, double())
-  values <- unname(values)
-  if (length(values) == 0L) {
-    return(new_rcrd(
-      list(
-        values = rep(NA_real_, length(quantile_levels)),
-        quantile_levels = quantile_levels
-      ),
-      class = c("dist_quantiles", "dist_default")
-    ))
-  }
-  stopifnot(length(values) == length(quantile_levels))
-
-  stopifnot(!vctrs::vec_duplicate_any(quantile_levels))
-  if (is.unsorted(quantile_levels)) {
-    o <- vctrs::vec_order(quantile_levels)
-    values <- values[o]
-    quantile_levels <- quantile_levels[o]
-  }
-  if (is.unsorted(values, na.rm = TRUE)) {
-    cli_abort("`values[order(quantile_levels)]` produces unsorted quantiles.")
-  }
-
-  new_rcrd(list(values = values, quantile_levels = quantile_levels),
-    class = c("dist_quantiles", "dist_default")
-  )
-}
-
-
-
-#' @importFrom vctrs vec_ptype_abbr vec_ptype_full
-#' @export
-vec_ptype_abbr.dist_quantiles <- function(x, ...) "dist_qntls"
-#' @export
-vec_ptype_full.dist_quantiles <- function(x, ...) "dist_quantiles"
-
-#' @export
-format.dist_quantiles <- function(x, digits = 2, ...) {
-  m <- suppressWarnings(median(x))
-  paste0("quantiles(", round(m, digits), ")[", vctrs::vec_size(x), "]")
-}
-
-
-#' A distribution parameterized by a set of quantiles
-#'
-#' @param values A vector (or list of vectors) of values.
-#' @param quantile_levels A vector (or list of vectors) of probabilities
-#'   corresponding to `values`.
-#'
-#' When creating multiple sets of `values`/`quantile_levels` resulting in
-#' different distributions, the sizes must match. See the examples below.
-#'
-#' @return A vector of class `"distribution"`.
-#'
-#' @export
-#'
-#' @examples
-#' dist_quantiles(1:4, 1:4 / 5)
-#' dist_quantiles(list(1:3, 1:4), list(1:3 / 4, 1:4 / 5))
-#' dstn <- dist_quantiles(list(1:4, 8:11), c(.2, .4, .6, .8))
-#' dstn
-#'
-#' quantile(dstn, p = c(.1, .25, .5, .9))
-#' median(dstn)
-#'
-#' # it's a bit annoying to inspect the data
-#' distributional::parameters(dstn[1])
-#' nested_quantiles(dstn[1])[[1]]
-#'
-#' @importFrom vctrs as_list_of vec_recycle_common new_vctr
-dist_quantiles <- function(values, quantile_levels) {
-  if (!is.list(quantile_levels)) {
-    assert_numeric(quantile_levels, lower = 0, upper = 1, any.missing = FALSE, min.len = 1L)
-    quantile_levels <- list(quantile_levels)
-  }
-  if (!is.list(values)) {
-    if (length(values) == 0L) values <- NA_real_
-    values <- list(values)
-  }
-
-  values <- as_list_of(values, .ptype = double())
-  quantile_levels <- as_list_of(quantile_levels, .ptype = double())
-  args <- vec_recycle_common(values = values, quantile_levels = quantile_levels)
-
-  qntls <- as_list_of(
-    map2(args$values, args$quantile_levels, new_quantiles),
-    .ptype = new_quantiles(NA_real_, 0.5)
-  )
-  new_vctr(qntls, class = "distribution")
-}
-
-validate_dist_quantiles <- function(values, quantile_levels) {
-  map(quantile_levels, arg_is_probabilities)
-  common_length <- vctrs::vec_size_common( # aborts internally
-    values = values,
-    quantile_levels = quantile_levels
-  )
-  length_diff <- vctrs::list_sizes(values) != vctrs::list_sizes(quantile_levels)
-  if (any(length_diff)) {
-    cli_abort(c(
-      "`values` and `quantile_levels` must have common length.",
-      i = "Mismatches found at position(s): {.val {which(length_diff)}}."
-    ))
-  }
-  level_duplication <- map_lgl(quantile_levels, vctrs::vec_duplicate_any)
-  if (any(level_duplication)) {
-    cli_abort(c(
-      "`quantile_levels` must not be duplicated.",
-      i = "Duplicates found at position(s): {.val {which(level_duplication)}}."
-    ))
-  }
-}
-
-
-is_dist_quantiles <- function(x) {
-  is_distribution(x) & all(stats::family(x) == "quantiles")
-}
-
-
-
-#' @export
-#' @importFrom stats median qnorm family
-median.dist_quantiles <- function(x, na.rm = FALSE, ..., middle = c("cubic", "linear")) {
-  quantile_levels <- field(x, "quantile_levels")
-  values <- field(x, "values")
-  # we have exactly that quantile
-  if (0.5 %in% quantile_levels) {
-    return(values[match(0.5, quantile_levels)])
-  }
-  # if there's only 1 quantile_level (and it isn't 0.5), or the smallest quantile is larger than 0.5 or the largest smaller than 0.5, or if every value is NA, return NA
-  if (length(quantile_levels) < 2 || min(quantile_levels) > 0.5 || max(quantile_levels) < 0.5 || all(is.na(values))) {
-    return(NA)
-  }
-  if (length(quantile_levels) < 3 || min(quantile_levels) > .25 || max(quantile_levels) < .75) {
-    return(stats::approx(quantile_levels, values, xout = 0.5)$y)
-  }
-  quantile(x, 0.5, ..., middle = middle)
-}
 
 # placeholder to avoid errors, but not ideal
 #' @export
-mean.dist_quantiles <- function(x, na.rm = FALSE, ..., middle = c("cubic", "linear")) {
-  median(x, ..., middle = middle)
+mean.quantile_pred <- function(x, na.rm = FALSE, ...) {
+  median(x, ...)
 }
 
 #' @export
 #' @importFrom stats quantile
-#' @import distributional
-quantile.dist_quantiles <- function(x, p, ..., middle = c("cubic", "linear")) {
+quantile.quantile_pred <- function(x, p, ..., middle = c("cubic", "linear")) {
   arg_is_probabilities(p)
   p <- sort(p)
-  middle <- match.arg(middle)
+  middle <- rlang::arg_match(middle)
   quantile_extrapolate(x, p, middle)
 }
 
 
 quantile_extrapolate <- function(x, tau_out, middle) {
-  tau <- field(x, "quantile_levels")
-  qvals <- field(x, "values")
-  nas <- is.na(qvals)
-  if (all(nas)) {
-    return(rep(NA, times = length(tau_out)))
-  }
-  qvals_out <- rep(NA, length(tau_out))
-  qvals <- qvals[!nas]
-  tau <- tau[!nas]
+  tau <- x %@% "quantile_levels"
+  qvals <- as.matrix(x)
 
   # short circuit if we aren't actually extrapolating
   # matches to ~15 decimals
   if (all(tau_out %in% tau)) {
-    return(qvals[match(tau_out, tau)])
+    return(hardhat::quantile_pred(
+      qvals[ ,match(tau_out, tau), drop = FALSE], tau_out
+    ))
   }
   if (length(tau) < 2) {
-    cli_abort(
-      "Quantile extrapolation is not possible with fewer than 2 quantiles."
-    )
-    return(qvals_out)
+    cli_abort(paste(
+      "Quantile extrapolation is not possible when fewer than 2 quantiles",
+      "are available."
+    ))
   }
+  qvals_out <- map(
+    vctrs::vec_chop(qvals),
+    ~ extrapolate_quantiles_single(.x, tau, tau_out, middle)
+  )
 
+  hardhat::quantile_pred(qvals_out, tau_out)
+}
+
+extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
   indl <- tau_out < min(tau)
   indr <- tau_out > max(tau)
   indm <- !indl & !indr
+  qvals_out <- rep(NA, length(tau_out))
 
   if (middle == "cubic") {
     method <- "cubic"
-    result <- tryCatch(
-      {
-        Q <- stats::splinefun(tau, qvals, method = "hyman")
-        quartiles <- Q(c(.25, .5, .75))
-      },
-      error = function(e) {
-        return(NA)
-      }
-    )
+    result <- tryCatch({
+      Q <- stats::splinefun(tau, qvals, method = "hyman")
+      quartiles <- Q(c(.25, .5, .75))
+    },
+    error = function(e) {
+      return(NA)
+    })
   }
   if (middle == "linear" || any(is.na(result))) {
     method <- "linear"
     quartiles <- stats::approx(tau, qvals, c(.25, .5, .75))$y
   }
   if (any(indm)) {
-    qvals_out[indm] <- switch(method,
+    qvals_out[indm] <- switch(
+      method,
       linear = stats::approx(tau, qvals, tau_out[indm])$y,
       cubic = Q(tau_out[indm])
     )
@@ -242,59 +101,3 @@ tail_extrapolate <- function(tau_out, qv) {
   m <- diff(y) / diff(x)
   m * (x0 - x[1]) + y[1]
 }
-
-
-#' @method Math dist_quantiles
-#' @export
-Math.dist_quantiles <- function(x, ...) {
-  quantile_levels <- field(x, "quantile_levels")
-  values <- field(x, "values")
-  values <- vctrs::vec_math(.Generic, values, ...)
-  new_quantiles(values = values, quantile_levels = quantile_levels)
-}
-
-#' @method Ops dist_quantiles
-#' @export
-Ops.dist_quantiles <- function(e1, e2) {
-  is_quantiles <- c(
-    inherits(e1, "dist_quantiles"),
-    inherits(e2, "dist_quantiles")
-  )
-  is_dist <- c(inherits(e1, "dist_default"), inherits(e2, "dist_default"))
-  tau1 <- tau2 <- NULL
-  if (is_quantiles[1]) {
-    q1 <- field(e1, "values")
-    tau1 <- field(e1, "quantile_levels")
-  }
-  if (is_quantiles[2]) {
-    q2 <- field(e2, "values")
-    tau2 <- field(e2, "quantile_levels")
-  }
-  tau <- union(tau1, tau2)
-  if (all(is_dist)) {
-    cli_abort(
-      "You can't perform arithmetic between two distributions like this."
-    )
-  } else {
-    if (is_quantiles[1]) {
-      q2 <- e2
-    } else {
-      q1 <- e1
-    }
-  }
-  q <- vctrs::vec_arith(.Generic, q1, q2)
-  new_quantiles(values = q, quantile_levels = tau)
-}
-
-#' @method is.na distribution
-#' @export
-is.na.distribution <- function(x) {
-  sapply(vec_data(x), is.na)
-}
-
-#' @method is.na dist_quantiles
-#' @export
-is.na.dist_quantiles <- function(x) {
-  q <- field(x, "values")
-  all(is.na(q))
-}
diff --git a/man/dist_quantiles.Rd b/man/dist_quantiles.Rd
deleted file mode 100644
index 1a3226e36..000000000
--- a/man/dist_quantiles.Rd
+++ /dev/null
@@ -1,37 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/dist_quantiles.R
-\name{dist_quantiles}
-\alias{dist_quantiles}
-\title{A distribution parameterized by a set of quantiles}
-\usage{
-dist_quantiles(values, quantile_levels)
-}
-\arguments{
-\item{values}{A vector (or list of vectors) of values.}
-
-\item{quantile_levels}{A vector (or list of vectors) of probabilities
-corresponding to \code{values}.
-
-When creating multiple sets of \code{values}/\code{quantile_levels} resulting in
-different distributions, the sizes must match. See the examples below.}
-}
-\value{
-A vector of class \code{"distribution"}.
-}
-\description{
-A distribution parameterized by a set of quantiles
-}
-\examples{
-dist_quantiles(1:4, 1:4 / 5)
-dist_quantiles(list(1:3, 1:4), list(1:3 / 4, 1:4 / 5))
-dstn <- dist_quantiles(list(1:4, 8:11), c(.2, .4, .6, .8))
-dstn
-
-quantile(dstn, p = c(.1, .25, .5, .9))
-median(dstn)
-
-# it's a bit annoying to inspect the data
-distributional::parameters(dstn[1])
-nested_quantiles(dstn[1])[[1]]
-
-}
diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index 78d74c812..dd078c881 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -1,36 +1,31 @@
-suppressPackageStartupMessages(library(distributional))
-
-test_that("constructor returns reasonable quantiles", {
-  expect_snapshot(error = TRUE, new_quantiles(rnorm(5), c(-2, -1, 0, 1, 2)))
-  expect_silent(new_quantiles(sort(rnorm(5)), sort(runif(5))))
-  expect_snapshot(error = TRUE, new_quantiles(sort(rnorm(5)), sort(runif(2))))
-  expect_silent(new_quantiles(1:5, 1:5 / 10))
-  expect_snapshot(error = TRUE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .1, .2, .5, .8)))
-  expect_snapshot(error = TRUE, new_quantiles(c(2, 1, 3, 4, 5), c(.1, .15, .2, .5, .8)))
-  expect_snapshot(error = TRUE, new_quantiles(c(1, 2, 3), c(.1, .2, 3)))
-})
-
-
-test_that("single dist_quantiles works, quantiles are accessible", {
-  z <- new_quantiles(values = 1:5, quantile_levels = c(.2, .4, .5, .6, .8))
-  expect_s3_class(z, "dist_quantiles")
+test_that("single quantile_pred works, quantiles are accessible", {
+  z <- hardhat::quantile_pred(
+    values = matrix(1:5, nrow = 1),
+    quantile_levels = c(.2, .4, .5, .6, .8)
+  )
   expect_equal(median(z), 3)
-  expect_equal(quantile(z, c(.2, .4, .5, .6, .8)), 1:5)
-  expect_equal(quantile(z, c(.3, .7), middle = "linear"), c(1.5, 4.5))
+  expect_equal(
+    quantile(z, c(.2, .4, .5, .6, .8)),
+    hardhat::quantile_pred(matrix(1:5, nrow = 1), c(.2, .4, .5, .6, .8))
+  )
+  expect_equal(
+    quantile(z, c(.3, .7), middle = "linear"),
+    hardhat::quantile_pred(matrix(c(1.5, 4.5), nrow = 1), c(.3, .7))
+  )
 
   Q <- stats::splinefun(c(.2, .4, .5, .6, .8), 1:5, method = "hyman")
   expect_equal(quantile(z, c(.3, .7), middle = "cubic"), Q(c(.3, .7)))
   expect_identical(
     extrapolate_quantiles(z, c(.3, .7), middle = "linear"),
-    new_quantiles(values = c(1, 1.5, 2, 3, 4, 4.5, 5), quantile_levels = 2:8 / 10)
+    hardhat::quantile_pred(c(1, 1.5, 2, 3, 4, 4.5, 5), 2:8 / 10)
   )
   # empty values slot results in a length zero distribution
   # see issue #361
-  expect_length(dist_quantiles(list(), c(.1, .9)), 0L)
-  expect_identical(
-    dist_quantiles(list(), c(.1, .9)),
-    distributional::dist_degenerate(double())
-  )
+  # expect_length(dist_quantiles(list(), c(.1, .9)), 0L)
+  # expect_identical(
+  #   dist_quantiles(list(), c(.1, .9)),
+  #   distributional::dist_degenerate(double())
+  # )
 })
 
 

From 7a87ae62465ed8e78304b6072142eb414ee887c4 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:04:05 -0800
Subject: [PATCH 02/22] add math ops and related tests

---
 NAMESPACE                                     | 12 ++-
 R/extrapolate_quantiles.R                     | 40 ++++----
 ...st_quantiles.R => quantile_pred-methods.R} | 75 +++++++++++++--
 tests/testthat/test-dist_quantiles.R          | 96 ++++++++-----------
 4 files changed, 131 insertions(+), 92 deletions(-)
 rename R/{dist_quantiles.R => quantile_pred-methods.R} (52%)

diff --git a/NAMESPACE b/NAMESPACE
index 4ce906754..3733d5ae2 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -39,9 +39,7 @@ S3method(extract_frosting,default)
 S3method(extract_frosting,epi_workflow)
 S3method(extract_layers,frosting)
 S3method(extract_layers,workflow)
-S3method(extrapolate_quantiles,dist_default)
-S3method(extrapolate_quantiles,dist_quantiles)
-S3method(extrapolate_quantiles,distribution)
+S3method(extrapolate_quantiles,quantile_pred)
 S3method(fit,epi_workflow)
 S3method(flusight_hub_formatter,canned_epipred)
 S3method(flusight_hub_formatter,data.frame)
@@ -119,6 +117,10 @@ S3method(tidy,check_enough_train_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
 S3method(update,layer)
+S3method(vec_arith,quantile_pred)
+S3method(vec_arith.numeric,quantile_pred)
+S3method(vec_arith.quantile_pred,numeric)
+S3method(vec_math,quantile_pred)
 S3method(weighted_interval_score,default)
 S3method(weighted_interval_score,dist_default)
 S3method(weighted_interval_score,dist_quantiles)
@@ -333,6 +335,8 @@ importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)
+importFrom(vctrs,vec_arith)
+importFrom(vctrs,vec_arith.numeric)
 importFrom(vctrs,vec_cast)
-importFrom(vctrs,vec_data)
+importFrom(vctrs,vec_math)
 importFrom(workflows,extract_preprocessor)
diff --git a/R/extrapolate_quantiles.R b/R/extrapolate_quantiles.R
index 3362e339e..d9e899ef6 100644
--- a/R/extrapolate_quantiles.R
+++ b/R/extrapolate_quantiles.R
@@ -32,34 +32,28 @@ extrapolate_quantiles <- function(x, probs, replace_na = TRUE, ...) {
 }
 
 #' @export
-#' @importFrom vctrs vec_data
-extrapolate_quantiles.distribution <- function(x, probs, replace_na = TRUE, ...) {
-  rlang::check_dots_empty()
+extrapolate_quantiles.quantile_pred <- function(x, probs, replace_na = TRUE, ...) {
   arg_is_lgl_scalar(replace_na)
   arg_is_probabilities(probs)
   if (is.unsorted(probs)) probs <- sort(probs)
-  dstn <- lapply(vec_data(x), extrapolate_quantiles, probs = probs, replace_na = replace_na)
-  new_vctr(dstn, vars = NULL, class = "distribution")
-}
-
-#' @export
-extrapolate_quantiles.dist_default <- function(x, probs, replace_na = TRUE, ...) {
-  values <- quantile(x, probs, ...)
-  new_quantiles(values = values, quantile_levels = probs)
-}
+  orig_probs <- x %@% "quantile_levels"
+  orig_values <- as.matrix(x)
 
-#' @export
-extrapolate_quantiles.dist_quantiles <- function(x, probs, replace_na = TRUE, ...) {
-  orig_probs <- field(x, "quantile_levels")
-  orig_values <- field(x, "values")
-  new_probs <- c(orig_probs, probs)
-  dups <- duplicated(new_probs)
   if (!replace_na || !anyNA(orig_values)) {
-    new_values <- c(orig_values, quantile(x, probs, ...))
+    all_values <- cbind(orig_values, quantile(x, probs, ...))
   } else {
-    nas <- is.na(orig_values)
-    orig_values[nas] <- quantile(x, orig_probs[nas], ...)
-    new_values <- c(orig_values, quantile(x, probs, ...))
+    newx <- quantile(x, orig_probs, ...) %>%
+      hardhat::quantile_pred(orig_probs)
+    all_values <- cbind(as.matrix(newx), quantile(newx, probs, ...))
   }
-  new_quantiles(new_values[!dups], new_probs[!dups])
+  all_probs <- c(orig_probs, probs)
+  dups <- duplicated(all_probs)
+  all_values <- all_values[, !dups, drop = FALSE]
+  all_probs <- all_probs[!dups]
+  o <- order(all_probs)
+
+  hardhat::quantile_pred(
+    all_values[, o, drop = FALSE],
+    quantile_levels = all_probs[o]
+  )
 }
diff --git a/R/dist_quantiles.R b/R/quantile_pred-methods.R
similarity index 52%
rename from R/dist_quantiles.R
rename to R/quantile_pred-methods.R
index db8e62d10..20379b46e 100644
--- a/R/dist_quantiles.R
+++ b/R/quantile_pred-methods.R
@@ -5,26 +5,29 @@ mean.quantile_pred <- function(x, na.rm = FALSE, ...) {
   median(x, ...)
 }
 
+
+# quantiles by treating quantile_pred like a distribution -----------------
+
+
 #' @export
 #' @importFrom stats quantile
-quantile.quantile_pred <- function(x, p, ..., middle = c("cubic", "linear")) {
+quantile.quantile_pred <- function(x, p, na.rm = FALSE, ...,
+                                   middle = c("cubic", "linear")) {
   arg_is_probabilities(p)
   p <- sort(p)
   middle <- rlang::arg_match(middle)
-  quantile_extrapolate(x, p, middle)
+  quantile_internal(x, p, middle)
 }
 
 
-quantile_extrapolate <- function(x, tau_out, middle) {
+quantile_internal <- function(x, tau_out, middle) {
   tau <- x %@% "quantile_levels"
   qvals <- as.matrix(x)
 
   # short circuit if we aren't actually extrapolating
   # matches to ~15 decimals
-  if (all(tau_out %in% tau)) {
-    return(hardhat::quantile_pred(
-      qvals[ ,match(tau_out, tau), drop = FALSE], tau_out
-    ))
+  if (all(tau_out %in% tau) && !anyNA(qvals)) {
+    return(qvals[ , match(tau_out, tau), drop = FALSE])
   }
   if (length(tau) < 2) {
     cli_abort(paste(
@@ -36,15 +39,26 @@ quantile_extrapolate <- function(x, tau_out, middle) {
     vctrs::vec_chop(qvals),
     ~ extrapolate_quantiles_single(.x, tau, tau_out, middle)
   )
-
-  hardhat::quantile_pred(qvals_out, tau_out)
+  qvals_out <- do.call(rbind, qvals_out) # ensure a matrix of the proper dims
+  qvals_out
 }
 
 extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
+  qvals_out <- rep(NA, length(tau_out))
+  good <- !is.na(qvals)
+  qvals <- qvals[good]
+  tau <- tau[good]
+
+  # in case we only have one point, and it matches something we wanted
+  if (length(good) < 2) {
+    matched_one <- tau_out %in% tau
+    qvals_out[matched_one] <- qvals[matched_one]
+    return(qvals_out)
+  }
+
   indl <- tau_out < min(tau)
   indr <- tau_out > max(tau)
   indm <- !indl & !indr
-  qvals_out <- rep(NA, length(tau_out))
 
   if (middle == "cubic") {
     method <- "cubic"
@@ -101,3 +115,44 @@ tail_extrapolate <- function(tau_out, qv) {
   m <- diff(y) / diff(x)
   m * (x0 - x[1]) + y[1]
 }
+
+
+# mathematical operations on the values -----------------------------------
+
+
+#' @importFrom vctrs vec_math
+#' @export
+#' @method vec_math quantile_pred
+vec_math.quantile_pred <- function(.fn, .x, ...) {
+  fn <- .fn
+  .fn <- getExportedValue("base", .fn)
+  if (fn %in% c("any", "all", "prod", "sum", "cumsum", "cummax", "cummin", "cumprod")) {
+    cli_abort("{.fn {fn}} is not a supported operation for {.cls quantile_pred}.")
+  }
+  quantile_levels <- .x %@% "quantile_levels"
+  .x <- as.matrix(.x)
+  hardhat::quantile_pred(.fn(.x), quantile_levels)
+}
+
+#' @importFrom vctrs vec_arith vec_arith.numeric
+#' @export
+#' @method vec_arith quantile_pred
+vec_arith.quantile_pred <- function(op, x, y, ...) {
+  UseMethod("vec_arith.quantile_pred", y)
+}
+
+#' @export
+#' @method vec_arith.quantile_pred numeric
+vec_arith.quantile_pred.numeric <- function(op, x, y, ...) {
+  op_fn <- getExportedValue("base", op)
+  out <- op_fn(as.matrix(x), y)
+  hardhat::quantile_pred(out, x %@% "quantile_levels")
+}
+
+#' @export
+#' @method vec_arith.numeric quantile_pred
+vec_arith.numeric.quantile_pred <- function(op, x, y, ...) {
+  op_fn <- getExportedValue("base", op)
+  out <- op_fn(x, as.matrix(y))
+  hardhat::quantile_pred(out, y %@% "quantile_levels")
+}
diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-dist_quantiles.R
index dd078c881..170a36438 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-dist_quantiles.R
@@ -4,53 +4,38 @@ test_that("single quantile_pred works, quantiles are accessible", {
     quantile_levels = c(.2, .4, .5, .6, .8)
   )
   expect_equal(median(z), 3)
-  expect_equal(
-    quantile(z, c(.2, .4, .5, .6, .8)),
-    hardhat::quantile_pred(matrix(1:5, nrow = 1), c(.2, .4, .5, .6, .8))
-  )
+  expect_equal(quantile(z, c(.2, .4, .5, .6, .8)), matrix(1:5, nrow = 1))
   expect_equal(
     quantile(z, c(.3, .7), middle = "linear"),
-    hardhat::quantile_pred(matrix(c(1.5, 4.5), nrow = 1), c(.3, .7))
+    matrix(c(1.5, 4.5), nrow = 1)
   )
 
   Q <- stats::splinefun(c(.2, .4, .5, .6, .8), 1:5, method = "hyman")
-  expect_equal(quantile(z, c(.3, .7), middle = "cubic"), Q(c(.3, .7)))
+  expect_equal(quantile(z, c(.3, .7)), Q(c(.3, .7)))
   expect_identical(
     extrapolate_quantiles(z, c(.3, .7), middle = "linear"),
-    hardhat::quantile_pred(c(1, 1.5, 2, 3, 4, 4.5, 5), 2:8 / 10)
+    hardhat::quantile_pred(matrix(c(1, 1.5, 2, 3, 4, 4.5, 5), nrow = 1), 2:8 / 10)
   )
-  # empty values slot results in a length zero distribution
-  # see issue #361
-  # expect_length(dist_quantiles(list(), c(.1, .9)), 0L)
-  # expect_identical(
-  #   dist_quantiles(list(), c(.1, .9)),
-  #   distributional::dist_degenerate(double())
-  # )
 })
 
 
 test_that("quantile extrapolator works", {
-  dstn <- dist_normal(c(10, 2), c(5, 10))
-  qq <- extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-  expect_s3_class(qq, "distribution")
-  expect_s3_class(vctrs::vec_data(qq[1])[[1]], "dist_quantiles")
-  expect_length(parameters(qq[1])$quantile_levels[[1]], 3L)
-
-
-  dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
+  dstn <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE),
+    c(.2, .4, .6, .8)
+  )
   qq <- extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-  expect_s3_class(qq, "distribution")
-  expect_s3_class(vctrs::vec_data(qq[1])[[1]], "dist_quantiles")
-  expect_length(parameters(qq[1])$quantile_levels[[1]], 7L)
+  expect_s3_class(qq, c("quantile_pred", "vctrs_vctr", "list"))
+  expect_length(qq %@% "quantile_levels", 7L)
 
-  dstn <- dist_quantiles(1:4, 1:4 / 5)
+  dstn <- hardhat::quantile_pred(matrix(1:4, nrow = 1), 1:4 / 5)
   qq <- extrapolate_quantiles(dstn, 1:9 / 10)
-  dstn_na <- dist_quantiles(c(1, 2, NA, 4), 1:4 / 5)
+  dstn_na <- hardhat::quantile_pred(matrix(c(1, 2, NA, 4), nrow = 1), 1:4 / 5)
   qq2 <- extrapolate_quantiles(dstn_na, 1:9 / 10)
   expect_equal(qq, qq2)
   qq3 <- extrapolate_quantiles(dstn_na, 1:9 / 10, replace_na = FALSE)
-  qq2_vals <- field(vec_data(qq2)[[1]], "values")
-  qq3_vals <- field(vec_data(qq3)[[1]], "values")
+  qq2_vals <- unlist(qq2)
+  qq3_vals <- unlist(qq3)
   qq2_vals[6] <- NA
   expect_equal(qq2_vals, qq3_vals)
 })
@@ -60,7 +45,7 @@ test_that("small deviations of quantile requests work", {
   v <- c(0.0890306, 0.1424997, 0.1971793, 0.2850978, 0.3832912, 0.4240479)
   badl <- l
   badl[1] <- badl[1] - 1e-14
-  distn <- dist_quantiles(list(v), list(l))
+  distn <- hardhat::quantile_pred(matrix(v, nrow = 1), l)
 
   # was broken before, now works
   expect_equal(quantile(distn, l), quantile(distn, badl))
@@ -69,50 +54,51 @@ test_that("small deviations of quantile requests work", {
   # the smallest (largest) values or we could end up unsorted
   l <- 1:9 / 10
   v <- 1:9
-  distn <- dist_quantiles(list(v), list(l))
-  expect_equal(quantile(distn, c(.25, .75)), list(c(2.5, 7.5)))
-  expect_equal(quantile(distn, c(.1, .9)), list(c(1, 9)))
+  distn <- hardhat::quantile_pred(matrix(v, nrow = 1), l)
+  expect_equal(quantile(distn, c(.25, .75)), matrix(c(2.5, 7.5), nrow = 1))
+  expect_equal(quantile(distn, c(.1, .9)), matrix(c(1, 9), nrow = 1))
   qv <- data.frame(q = l, v = v)
   expect_equal(
-    unlist(quantile(distn, c(.01, .05))),
+    drop(quantile(distn, c(.01, .05))),
     tail_extrapolate(c(.01, .05), head(qv, 2))
   )
   expect_equal(
-    unlist(quantile(distn, c(.99, .95))),
+    drop(quantile(distn, c(.99, .95))),
     tail_extrapolate(c(.95, .99), tail(qv, 2))
   )
 })
 
 test_that("unary math works on quantiles", {
-  dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-  dstn2 <- dist_quantiles(list(log(1:4), log(8:11)), list(c(.2, .4, .6, .8)))
+  dstn <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE),
+    1:4 / 5
+  )
+  dstn2 <- hardhat::quantile_pred(
+    log(matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE)),
+    1:4 / 5
+  )
   expect_identical(log(dstn), dstn2)
 
-  dstn2 <- dist_quantiles(list(cumsum(1:4), cumsum(8:11)), list(c(.2, .4, .6, .8)))
-  expect_identical(cumsum(dstn), dstn2)
 })
 
 test_that("arithmetic works on quantiles", {
-  dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-  dstn2 <- dist_quantiles(list(1:4 + 1, 8:11 + 1), list(c(.2, .4, .6, .8)))
+  dstn <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE),
+    1:4 / 5
+  )
+  dstn2 <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE) + 1,
+    1:4 / 5
+  )
   expect_identical(dstn + 1, dstn2)
   expect_identical(1 + dstn, dstn2)
 
-  dstn2 <- dist_quantiles(list(1:4 / 4, 8:11 / 4), list(c(.2, .4, .6, .8)))
+  dstn2 <- hardhat::quantile_pred(
+    matrix(c(1:4, 8:11), nrow = 2, byrow = TRUE) / 4,
+    1:4 / 5
+  )
   expect_identical(dstn / 4, dstn2)
   expect_identical((1 / 4) * dstn, dstn2)
 
-  expect_snapshot(error = TRUE, sum(dstn))
-  expect_snapshot(error = TRUE, suppressWarnings(dstn + distributional::dist_normal()))
-})
-
-test_that("quantile.dist_quantile works for NA vectors", {
-  distn <- dist_quantiles(
-    list(c(NA, NA)),
-    list(1:2 / 3)
-  )
-  expect_true(is.na(quantile(distn, p = 0.5)))
-  expect_true(is.na(median(distn)))
-  expect_true(is.na(mean(distn)))
-  expect_equal(format(distn), "quantiles(NA)[2]")
+  expect_error(sum(dstn))
 })

From dcca30138bb86de81326592e88878ca0a930b7e2 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:06:11 -0800
Subject: [PATCH 03/22] rename tests

---
 .../testthat/{test-dist_quantiles.R => test-quantile_pred.R}  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)
 rename tests/testthat/{test-dist_quantiles.R => test-quantile_pred.R} (96%)

diff --git a/tests/testthat/test-dist_quantiles.R b/tests/testthat/test-quantile_pred.R
similarity index 96%
rename from tests/testthat/test-dist_quantiles.R
rename to tests/testthat/test-quantile_pred.R
index 170a36438..90710c6e1 100644
--- a/tests/testthat/test-dist_quantiles.R
+++ b/tests/testthat/test-quantile_pred.R
@@ -11,7 +11,7 @@ test_that("single quantile_pred works, quantiles are accessible", {
   )
 
   Q <- stats::splinefun(c(.2, .4, .5, .6, .8), 1:5, method = "hyman")
-  expect_equal(quantile(z, c(.3, .7)), Q(c(.3, .7)))
+  expect_equal(quantile(z, c(.3, .7)), matrix(Q(c(.3, .7)), nrow = 1))
   expect_identical(
     extrapolate_quantiles(z, c(.3, .7), middle = "linear"),
     hardhat::quantile_pred(matrix(c(1, 1.5, 2, 3, 4, 4.5, 5), nrow = 1), 2:8 / 10)
@@ -100,5 +100,5 @@ test_that("arithmetic works on quantiles", {
   expect_identical(dstn / 4, dstn2)
   expect_identical((1 / 4) * dstn, dstn2)
 
-  expect_error(sum(dstn))
+  expect_snapshot(error = TRUE, sum(dstn))
 })

From 316065b8cfe52a64f2822bc8b2d98e745abc8351 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:06:59 -0800
Subject: [PATCH 04/22] quantile pred tests pass

---
 tests/testthat/_snaps/quantile_pred.md | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 tests/testthat/_snaps/quantile_pred.md

diff --git a/tests/testthat/_snaps/quantile_pred.md b/tests/testthat/_snaps/quantile_pred.md
new file mode 100644
index 000000000..dd13dcb86
--- /dev/null
+++ b/tests/testthat/_snaps/quantile_pred.md
@@ -0,0 +1,8 @@
+# arithmetic works on quantiles
+
+    Code
+      sum(dstn)
+    Condition
+      Error in `vec_math()`:
+      ! `sum()` is not a supported operation for <quantile_pred>.
+

From d508069c0191a699066a01dd8ace427628faabfb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:08:46 -0800
Subject: [PATCH 05/22] updates to WIS

---
 NAMESPACE                      |   5 +-
 R/weighted_interval_score.R    | 100 +++++++++++----------------------
 man/weighted_interval_score.Rd |  19 ++-----
 3 files changed, 38 insertions(+), 86 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 3733d5ae2..90dcc43c4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -121,10 +121,7 @@ S3method(vec_arith,quantile_pred)
 S3method(vec_arith.numeric,quantile_pred)
 S3method(vec_arith.quantile_pred,numeric)
 S3method(vec_math,quantile_pred)
-S3method(weighted_interval_score,default)
-S3method(weighted_interval_score,dist_default)
-S3method(weighted_interval_score,dist_quantiles)
-S3method(weighted_interval_score,distribution)
+S3method(weighted_interval_score,quantile_pred)
 export("%>%")
 export(Add_model)
 export(Remove_model)
diff --git a/R/weighted_interval_score.R b/R/weighted_interval_score.R
index 48741de7d..567ae4e69 100644
--- a/R/weighted_interval_score.R
+++ b/R/weighted_interval_score.R
@@ -13,6 +13,16 @@
 #' @param actual double. Actual value(s)
 #' @param quantile_levels probabilities. If specified, the score will be
 #'   computed at this set of levels.
+#' @param na_handling character. Determines how `quantile_levels` without a
+#'   corresponding `value` are handled. For `"impute"`, missing values will be
+#'   calculated if possible using the available quantiles. For `"drop"`,
+#'   explicitly missing values are ignored in the calculation of the score, but
+#'   implicitly missing values are imputed if possible.
+#'   For `"propogate"`, the resulting score will be `NA` if any missing values
+#'   exist in the original `quantile_levels`. Finally, if
+#'   `quantile_levels` is specified, `"fail"` will result in
+#'   the score being `NA` when any required quantile levels (implicit or explicit)
+#'   are do not have corresponding values.
 #' @param ... not used
 #'
 #' @return a vector of nonnegative scores.
@@ -44,13 +54,13 @@
 #'
 #' # Using some actual forecasts --------
 #' library(dplyr)
-#' jhu <- covid_case_death_rates %>%
+#' jhu <- case_death_rate_subset %>%
 #'   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 #' preds <- flatline_forecaster(
 #'   jhu, "death_rate",
 #'   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 #' )$predictions
-#' actuals <- covid_case_death_rates %>%
+#' actuals <- case_death_rate_subset %>%
 #'   filter(time_value == as.Date("2021-12-01") + 7) %>%
 #'   select(geo_value, time_value, actual = death_rate)
 #' preds <- left_join(preds, actuals,
@@ -58,90 +68,44 @@
 #' ) %>%
 #'   mutate(wis = weighted_interval_score(.pred_distn, actual))
 #' preds
-weighted_interval_score <- function(x, actual, quantile_levels = NULL, ...) {
+weighted_interval_score <- function(
+    x,
+    actual,
+    quantile_levels = NULL,
+    na_handling = c("impute", "drop", "propagate", "fail"),
+    ...) {
   UseMethod("weighted_interval_score")
 }
 
-#' @export
-weighted_interval_score.default <- function(x, actual,
-                                            quantile_levels = NULL, ...) {
-  cli_abort(c(
-    "Weighted interval score can only be calculated if `x`",
-    "has class {.cls distribution}."
-  ))
-}
-
-#' @export
-weighted_interval_score.distribution <- function(
-    x, actual,
-    quantile_levels = NULL, ...) {
-  assert_numeric(actual, finite = TRUE)
-  l <- vctrs::vec_recycle_common(x = x, actual = actual)
-  map2_dbl(
-    .x = vctrs::vec_data(l$x),
-    .y = l$actual,
-    .f = weighted_interval_score,
-    quantile_levels = quantile_levels,
-    ...
-  )
-}
-
-#' @export
-weighted_interval_score.dist_default <- function(x, actual,
-                                                 quantile_levels = NULL, ...) {
-  rlang::check_dots_empty()
-  if (is.null(quantile_levels)) {
-    cli_warn(c(
-      "Weighted interval score isn't implemented for {.cls {class(x)}}",
-      "as we don't know what set of quantile levels to use.",
-      "Use a {.cls dist_quantiles} or pass `quantile_levels`.",
-      "The result for this element will be `NA`."
-    ))
-    return(NA)
-  }
-  x <- extrapolate_quantiles(x, probs = quantile_levels)
-  weighted_interval_score(x, actual, quantile_levels = NULL)
-}
 
-#' @param na_handling character. Determines how `quantile_levels` without a
-#'   corresponding `value` are handled. For `"impute"`, missing values will be
-#'   calculated if possible using the available quantiles. For `"drop"`,
-#'   explicitly missing values are ignored in the calculation of the score, but
-#'   implicitly missing values are imputed if possible.
-#'   For `"propogate"`, the resulting score will be `NA` if any missing values
-#'   exist in the original `quantile_levels`. Finally, if
-#'   `quantile_levels` is specified, `"fail"` will result in
-#'   the score being `NA` when any required quantile levels (implicit or explicit)
-#'   are do not have corresponding values.
-#' @describeIn weighted_interval_score Weighted interval score with
-#'   `dist_quantiles` allows for different `NA` behaviours.
 #' @export
-weighted_interval_score.dist_quantiles <- function(
+weighted_interval_score.quantile_pred <- function(
     x, actual,
     quantile_levels = NULL,
     na_handling = c("impute", "drop", "propagate", "fail"),
     ...) {
   rlang::check_dots_empty()
-  if (is.na(actual)) {
-    return(NA)
-  }
-  if (all(is.na(vctrs::field(x, "values")))) {
-    return(NA)
-  }
+  n <- vctrs::vec_size(x)
+  if (length(actual) == 1L) actual <- rep(actual, n)
+  assert_numeric(actual, finite = TRUE, len = n)
+  assert_numeric(quantile_levels, lower = 0, upper = 1, null.ok = TRUE)
   na_handling <- rlang::arg_match(na_handling)
-  old_quantile_levels <- field(x, "quantile_levels")
+  old_quantile_levels <- x %@% "quantile_levels"
   if (na_handling == "fail") {
     if (is.null(quantile_levels)) {
       cli_abort('`na_handling = "fail"` requires `quantile_levels` to be specified.')
     }
-    old_values <- field(x, "values")
-    if (!all(quantile_levels %in% old_quantile_levels) || any(is.na(old_values))) {
-      return(NA)
+    if (!all(quantile_levels %in% old_quantile_levels)) {
+      return(rep(NA_real_, n))
     }
   }
   tau <- quantile_levels %||% old_quantile_levels
-  x <- extrapolate_quantiles(x, probs = tau, replace_na = (na_handling == "impute"))
-  q <- field(x, "values")[field(x, "quantile_levels") %in% tau]
+  x <- extrapolate_quantiles(x, tau, replace_na = (na_handling == "impute"))
+  x <- as.matrix(x)[, attr(x, "quantile_levels") %in% tau]
   na_rm <- (na_handling == "drop")
+  map2_dbl(vctrs::vec_chop(x), actual, ~ wis_one_quantile(.x, tau, .y, na_rm))
+}
+
+wis_one_quantile <- function(q, tau, actual, na_rm) {
   2 * mean(pmax(tau * (actual - q), (1 - tau) * (q - actual)), na.rm = na_rm)
 }
diff --git a/man/weighted_interval_score.Rd b/man/weighted_interval_score.Rd
index 4aac20e7d..4b7c796ea 100644
--- a/man/weighted_interval_score.Rd
+++ b/man/weighted_interval_score.Rd
@@ -2,12 +2,9 @@
 % Please edit documentation in R/weighted_interval_score.R
 \name{weighted_interval_score}
 \alias{weighted_interval_score}
-\alias{weighted_interval_score.dist_quantiles}
 \title{Compute weighted interval score}
 \usage{
-weighted_interval_score(x, actual, quantile_levels = NULL, ...)
-
-\method{weighted_interval_score}{dist_quantiles}(
+weighted_interval_score(
   x,
   actual,
   quantile_levels = NULL,
@@ -25,8 +22,6 @@ contains \code{dist_quantiles()}, though other distributions are supported when
 \item{quantile_levels}{probabilities. If specified, the score will be
 computed at this set of levels.}
 
-\item{...}{not used}
-
 \item{na_handling}{character. Determines how \code{quantile_levels} without a
 corresponding \code{value} are handled. For \code{"impute"}, missing values will be
 calculated if possible using the available quantiles. For \code{"drop"},
@@ -37,6 +32,8 @@ exist in the original \code{quantile_levels}. Finally, if
 \code{quantile_levels} is specified, \code{"fail"} will result in
 the score being \code{NA} when any required quantile levels (implicit or explicit)
 are do not have corresponding values.}
+
+\item{...}{not used}
 }
 \value{
 a vector of nonnegative scores.
@@ -48,12 +45,6 @@ approximation of the commonly-used continuous ranked probability score
 generalization of absolute error. For example, see \href{https://arxiv.org/abs/2005.12881}{Bracher et al. (2020)} for discussion in the context
 of COVID-19 forecasting.
 }
-\section{Methods (by class)}{
-\itemize{
-\item \code{weighted_interval_score(dist_quantiles)}: Weighted interval score with
-\code{dist_quantiles} allows for different \code{NA} behaviours.
-
-}}
 \examples{
 quantile_levels <- c(.2, .4, .6, .8)
 predq_1 <- 1:4 #
@@ -80,13 +71,13 @@ weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10,
 
 # Using some actual forecasts --------
 library(dplyr)
-jhu <- covid_case_death_rates \%>\%
+jhu <- case_death_rate_subset \%>\%
   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 preds <- flatline_forecaster(
   jhu, "death_rate",
   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 )$predictions
-actuals <- covid_case_death_rates \%>\%
+actuals <- case_death_rate_subset \%>\%
   filter(time_value == as.Date("2021-12-01") + 7) \%>\%
   select(geo_value, time_value, actual = death_rate)
 preds <- left_join(preds, actuals,

From 7278c69053317202b7ddbab1ab071f16a7961375 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 19 Feb 2025 15:55:16 -0800
Subject: [PATCH 06/22] remove the distributional package

---
 DESCRIPTION                                   |   2 +-
 NAMESPACE                                     |   8 +-
 R/autoplot.R                                  |   2 +-
 R/extrapolate_quantiles.R                     |  32 ++--
 R/flusight_hub_formatter.R                    |   7 +-
 R/layer_cdc_flatline_quantiles.R              |   7 +-
 R/layer_predictive_distn.R                    |   6 +
 R/layer_quantile_distn.R                      |  20 ++-
 R/layer_residual_quantiles.R                  |  15 +-
 R/layer_threshold_preds.R                     |  25 +--
 R/make_grf_quantiles.R                        |   4 +-
 R/make_quantile_reg.R                         |  20 +--
 R/make_smooth_quantile_reg.R                  |   4 +-
 R/pivot_quantiles.R                           | 156 +++++-------------
 R/quantile_pred-methods.R                     |  14 +-
 R/weighted_interval_score.R                   |  34 ++--
 man/extrapolate_quantiles.Rd                  |  32 ++--
 man/layer_cdc_flatline_quantiles.Rd           |   2 +-
 man/nested_quantiles.Rd                       |  24 ---
 man/pivot_quantiles_longer.Rd                 |  26 +--
 man/pivot_quantiles_wider.Rd                  |  20 +--
 man/smooth_quantile_reg.Rd                    |   2 +-
 man/weighted_interval_score.Rd                |  32 ++--
 tests/testthat/_snaps/pivot_quantiles.md      |  33 ++--
 tests/testthat/_snaps/wis-quantile_pred.md    |  16 ++
 .../testthat/test-layer_residual_quantiles.R  |   6 +-
 tests/testthat/test-layer_threshold_preds.R   |   9 +-
 tests/testthat/test-pivot_quantiles.R         |  72 +++-----
 ...t-quantiles.R => test-wis-quantile_pred.R} |  27 ++-
 29 files changed, 244 insertions(+), 413 deletions(-)
 delete mode 100644 man/nested_quantiles.Rd
 create mode 100644 tests/testthat/_snaps/wis-quantile_pred.md
 rename tests/testthat/{test-wis-dist-quantiles.R => test-wis-quantile_pred.R} (65%)

diff --git a/DESCRIPTION b/DESCRIPTION
index 9746d81da..01e07520d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -30,7 +30,6 @@ Depends:
 Imports:
     checkmate,
     cli,
-    distributional,
     dplyr,
     epiprocess (>= 0.10.4),
     generics,
@@ -51,6 +50,7 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
+    distributional,
     epidatr (>= 1.0.0),
     fs,
     grf,
diff --git a/NAMESPACE b/NAMESPACE
index 90dcc43c4..204175a04 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -110,9 +110,7 @@ S3method(slather,layer_residual_quantiles)
 S3method(slather,layer_threshold)
 S3method(slather,layer_unnest)
 S3method(snap,default)
-S3method(snap,dist_default)
-S3method(snap,dist_quantiles)
-S3method(snap,distribution)
+S3method(snap,quantile_pred)
 S3method(tidy,check_enough_train_data)
 S3method(tidy,frosting)
 S3method(tidy,layer)
@@ -184,8 +182,6 @@ export(layer_quantile_distn)
 export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
-export(mutate)
-export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
 export(pivot_longer)
@@ -202,6 +198,7 @@ export(rename)
 export(select)
 export(slather)
 export(smooth_quantile_reg)
+export(snap)
 export(step_adjust_latency)
 export(step_climate)
 export(step_epi_ahead)
@@ -280,6 +277,7 @@ importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
 importFrom(glue,glue)
 importFrom(hardhat,extract_recipe)
+importFrom(hardhat,quantile_pred)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
 importFrom(lubridate,"%m-%")
diff --git a/R/autoplot.R b/R/autoplot.R
index 60b395a7d..4e52ac3e3 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -243,7 +243,7 @@ plot_bands <- function(
   ntarget_dates <- dplyr::n_distinct(predictions$time_value)
 
   predictions <- predictions %>%
-    mutate(.pred_distn = dist_quantiles(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)
 
diff --git a/R/extrapolate_quantiles.R b/R/extrapolate_quantiles.R
index d9e899ef6..82116c1d3 100644
--- a/R/extrapolate_quantiles.R
+++ b/R/extrapolate_quantiles.R
@@ -1,32 +1,26 @@
 #' Summarize a distribution with a set of quantiles
 #'
-#' @param x a `distribution` vector
+#' This function takes a `quantile_pred` vector and returns the same
+#' type of object, expanded to include
+#' *additional* quantiles computed at `probs`. If you want behaviour more
+#' similar to [stats::quantile()], then `quantile(x,...)` may be more
+#' appropriate.
+#'
+#' @param x A vector of class `quantile_pred`.
 #' @param probs a vector of probabilities at which to calculate quantiles
 #' @param replace_na logical. If `x` contains `NA`'s, these are imputed if
-#'   possible (if `TRUE`) or retained (if `FALSE`). This only effects
-#'   elements of class `dist_quantiles`.
+#'   possible (if `TRUE`) or retained (if `FALSE`).
 #' @param ... additional arguments passed on to the `quantile` method
 #'
-#' @return a `distribution` vector containing `dist_quantiles`. Any elements
-#'   of `x` which were originally `dist_quantiles` will now have a superset
+#' @return a `quantile_pred` vector. Each element
+#'   of `x` will now have a superset
 #'   of the original `quantile_values` (the union of those and `probs`).
 #' @export
 #'
 #' @examples
-#' library(distributional)
-#' dstn <- dist_normal(c(10, 2), c(5, 10))
-#' extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-#'
-#' dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-#' # because this distribution is already quantiles, any extra quantiles are
-#' # appended
-#' extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-#'
-#' dstn <- c(
-#'   dist_normal(c(10, 2), c(5, 10)),
-#'   dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-#' )
-#' extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
+#' dstn <- quantile_dstn(rbind(1:4, 8:11), c(.2, .4, .6, .8))
+#' # extra quantiles are appended
+#' as.tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
 extrapolate_quantiles <- function(x, probs, replace_na = TRUE, ...) {
   UseMethod("extrapolate_quantiles")
 }
diff --git a/R/flusight_hub_formatter.R b/R/flusight_hub_formatter.R
index feac624be..3deba254e 100644
--- a/R/flusight_hub_formatter.R
+++ b/R/flusight_hub_formatter.R
@@ -104,12 +104,11 @@ flusight_hub_formatter.data.frame <- function(
 
   object <- object %>%
     # combine the predictions and the distribution
-    mutate(.pred_distn = nested_quantiles(.pred_distn)) %>%
-    tidyr::unnest(.pred_distn) %>%
+    pivot_quantiles_longer(.pred_distn) %>%
     # now we create the correct column names
     rename(
-      value = values,
-      output_type_id = quantile_levels,
+      value = .pred_distn_value,
+      output_type_id = .pred_distn_quantile_level,
       reference_date = forecast_date
     ) %>%
     # convert to fips codes, and add any constant cols passed in ...
diff --git a/R/layer_cdc_flatline_quantiles.R b/R/layer_cdc_flatline_quantiles.R
index 5211170ad..9c08ef768 100644
--- a/R/layer_cdc_flatline_quantiles.R
+++ b/R/layer_cdc_flatline_quantiles.R
@@ -51,7 +51,7 @@
 #'   in an additional `<list-col>` named `.pred_distn_all` containing 2-column
 #'   [tibble::tibble()]'s. For each
 #'   desired combination of `key`'s, the tibble will contain one row per ahead
-#'   with the associated [dist_quantiles()].
+#'   with the associated [quantile_pred()].
 #' @export
 #'
 #' @examples
@@ -265,11 +265,10 @@ propagate_samples <- function(
     }
   }
   res <- res[aheads]
+  res_quantiles <- map(res, quantile, probs = quantile_levels)
   list(tibble(
     ahead = aheads,
-    .pred_distn = map_vec(
-      res, ~ dist_quantiles(quantile(.x, quantile_levels), quantile_levels)
-    )
+    .pred_distn = quantile_pred(do.call(rbind, res_quantiles), quantile_levels)
   ))
 }
 
diff --git a/R/layer_predictive_distn.R b/R/layer_predictive_distn.R
index 2b18fbf8e..8ce55e1c3 100644
--- a/R/layer_predictive_distn.R
+++ b/R/layer_predictive_distn.R
@@ -45,6 +45,12 @@ layer_predictive_distn <- function(frosting,
                                    truncate = c(-Inf, Inf),
                                    name = ".pred_distn",
                                    id = rand_id("predictive_distn")) {
+  if (!requireNamespace("distributional", quietly = TRUE)) {
+    cli_abort(paste(
+      "You must install the {.pkg distributional} package for",
+      "this functionality."
+    ))
+  }
   rlang::check_dots_empty()
   arg_is_chr_scalar(name, id)
   dist_type <- match.arg(dist_type)
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index 2d991a20f..67fbfa28c 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -78,15 +78,23 @@ layer_quantile_distn_new <- function(quantile_levels, truncate, name, id) {
 slather.layer_quantile_distn <-
   function(object, components, workflow, new_data, ...) {
     dstn <- components$predictions$.pred
-    if (!inherits(dstn, "distribution")) {
-      cli_abort(c(
-        "`layer_quantile_distn()` requires distributional predictions.",
-        "These are of class {.cls {class(dstn)}}."
-      ))
+    is_supported <- inherits(dstn, "distribution") ||
+      inherits(dstn, "quantile_pred")
+    if (!is_supported) {
+      cli_abort(
+        "`layer_quantile_distn()` requires distributional or quantile
+        predictions. These are of class {.cls {class(dstn)}}."
+      )
+    }
+    if (inherits(dstn, "distribution") && !requireNamespace("distributional", quietly = TRUE)) {
+      cli_abort(
+        "You must install the {.pkg distributional} package for this
+        functionality."
+      )
     }
     rlang::check_dots_empty()
 
-    dstn <- dist_quantiles(
+    dstn <- quantile_pred(
       quantile(dstn, object$quantile_levels),
       object$quantile_levels
     )
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 9a811e531..0517a0ffd 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -125,14 +125,11 @@ slather.layer_residual_quantiles <-
     }
 
     r <- r %>%
-      summarise(
-        dstn = list(quantile(
-          c(.resid, s * .resid),
-          probs = object$quantile_levels, na.rm = TRUE
-        ))
-      )
+      summarize(dstn = quantile_pred(matrix(quantile(
+        c(.resid, s * .resid), probs = object$quantile_levels, na.rm = TRUE
+      ), nrow = 1), quantile_levels = object$quantile_levels))
     # Check for NA
-    if (any(sapply(r$dstn, is.na))) {
+    if (anyNA(as.matrix(r$dstn)))  {
       cli_abort(c(
         "Residual quantiles could not be calculated due to missing residuals.",
         i = "This may be due to `n_train` < `ahead` in your {.cls epi_recipe}."
@@ -140,9 +137,7 @@ slather.layer_residual_quantiles <-
     }
 
     estimate <- components$predictions$.pred
-    res <- tibble(
-      .pred_distn = dist_quantiles(map2(estimate, r$dstn, "+"), object$quantile_levels)
-    )
+    res <- tibble(.pred_distn = r$dstn + estimate)
     res <- check_pname(res, components$predictions, object)
     components$predictions <- mutate(components$predictions, !!!res)
     components
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index d480019a9..c0903ffda 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -62,6 +62,7 @@ layer_threshold_new <-
 
 
 # restrict various objects to the interval [lower, upper]
+#' @export
 snap <- function(x, lower, upper, ...) {
   UseMethod("snap")
 }
@@ -74,25 +75,11 @@ snap.default <- function(x, lower, upper, ...) {
 }
 
 #' @export
-snap.distribution <- function(x, lower, upper, ...) {
-  rlang::check_dots_empty()
-  arg_is_scalar(lower, upper)
-  dstn <- lapply(vec_data(x), snap, lower = lower, upper = upper)
-  distributional:::wrap_dist(dstn)
-}
-
-#' @export
-snap.dist_default <- function(x, lower, upper, ...) {
-  rlang::check_dots_empty()
-  x
-}
-
-#' @export
-snap.dist_quantiles <- function(x, lower, upper, ...) {
-  values <- field(x, "values")
-  quantile_levels <- field(x, "quantile_levels")
-  values <- snap(values, lower, upper)
-  new_quantiles(values = values, quantile_levels = quantile_levels)
+snap.quantile_pred <- function(x, lower, upper, ...) {
+  values <- as.matrix(x)
+  quantile_levels <- x %@% "quantile_levels"
+  values <- map(vctrs::vec_chop(values), ~ snap(.x, lower, upper))
+  quantile_pred(do.call(rbind, values), quantile_levels = quantile_levels)
 }
 
 #' @export
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index b1792adfd..78c855831 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -162,12 +162,12 @@ make_grf_quantiles <- function() {
     )
   )
 
-  # turn the predictions into a tibble with a dist_quantiles column
+  # turn the predictions into a tibble with a quantile_pred column
   process_qrf_preds <- function(x, object) {
     quantile_levels <- parsnip::extract_fit_engine(object)$quantiles.orig %>% sort()
     x <- x$predictions
     out <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
-    out <- dist_quantiles(out, list(quantile_levels))
+    out <- hardhat::quantile_pred(do.call(rbind, out), quantile_levels)
     return(dplyr::tibble(.pred = out))
   }
 
diff --git a/R/make_quantile_reg.R b/R/make_quantile_reg.R
index 223881c85..bc2d322bf 100644
--- a/R/make_quantile_reg.R
+++ b/R/make_quantile_reg.R
@@ -110,21 +110,11 @@ make_quantile_reg <- function() {
 
   process_rq_preds <- function(x, object) {
     object <- parsnip::extract_fit_engine(object)
-    type <- class(object)[1]
-
-    # can't make a method because object is second
-    out <- switch(type,
-      rq = dist_quantiles(unname(as.list(x)), object$tau), # one quantile
-      rqs = {
-        x <- lapply(vctrs::vec_chop(x), function(x) sort(drop(x)))
-        dist_quantiles(x, list(object$tau))
-      },
-      cli_abort(c(
-        "Prediction is not implemented for this `rq` type.",
-        i = "See {.fun quantreg::rq}."
-      ))
-    )
-    return(dplyr::tibble(.pred = out))
+    if (!is.matrix(x)) x <- as.matrix(x)
+    rownames(x) <- NULL
+    n_pred_quantiles <- ncol(x)
+    quantile_levels <- object$tau
+    tibble(.pred = hardhat::quantile_pred(x, quantile_levels))
   }
 
   parsnip::set_pred(
diff --git a/R/make_smooth_quantile_reg.R b/R/make_smooth_quantile_reg.R
index b56a67b37..1ca019f75 100644
--- a/R/make_smooth_quantile_reg.R
+++ b/R/make_smooth_quantile_reg.R
@@ -47,7 +47,7 @@
 #'     x = x[length(x) - 20] + ahead / 100 * 2 * pi,
 #'     ahead = NULL
 #'   ) %>%
-#'   pivot_wider(names_from = quantile_levels, values_from = values)
+#'   pivot_wider(names_from = distn_quantile_levels, values_from = distn_value)
 #' plot(x, y, pch = 16, xlim = c(pi, 2 * pi), col = "lightgrey")
 #' curve(sin(x), add = TRUE)
 #' abline(v = fd, lty = 2)
@@ -171,7 +171,7 @@ make_smooth_quantile_reg <- function() {
       x <- lapply(unname(split(
         p, seq(nrow(p))
       )), function(q) unname(sort(q, na.last = TRUE)))
-      dist_quantiles(x, list(object$tau))
+      quantile_pred(do.call(rbind, x), object$tau)
     })
     n_preds <- length(list_of_pred_distns[[1]])
     nout <- length(list_of_pred_distns)
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index d4ef3d57d..37f08b46f 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -1,144 +1,65 @@
-#' Turn a vector of quantile distributions into a list-col
+#' Pivot a column containing `quantile_pred` longer
 #'
-#' @param x a `distribution` containing `dist_quantiles`
-#'
-#' @return a list-col
-#' @export
-#'
-#' @examples
-#' edf <- covid_case_death_rates[1:3, ]
-#' edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
-#'
-#' edf_nested <- edf %>% mutate(q = nested_quantiles(q))
-#' edf_nested %>% unnest(q)
-nested_quantiles <- function(x) {
-  stopifnot(is_dist_quantiles(x))
-  distributional:::dist_apply(x, .f = function(z) {
-    as_tibble(vec_data(z)) %>%
-      mutate(across(everything(), as.double)) %>%
-      vctrs::list_of()
-  })
-}
-
-
-#' Pivot columns containing `dist_quantile` longer
-#'
-#' Selected columns that contain `dist_quantiles` will be "lengthened" with
+#' A column that contains `quantile_pred` will be "lengthened" with
 #' the quantile levels serving as 1 column and the values as another. If
 #' multiple columns are selected, these will be prefixed with the column name.
 #'
 #' @param .data A data frame, or a data frame extension such as a tibble or
 #'   epi_df.
-#' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One or more unquoted
+#' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One unquoted
 #'   expressions separated by commas. Variable names can be used as if they
-#'   were positions in the data frame, so expressions like `x:y` can
-#'   be used to select a range of variables.
-#' @param .ignore_length_check If multiple columns are selected, as long as
-#'   each row has contains the same number of quantiles, the result will be
-#'   reasonable. But if, for example, `var1[1]` has 5 quantiles while `var2[1]`
-#'   has 7, then the only option would be to recycle everything, creating a
-#'   _very_ long result. By default, this would throw an error. But if this is
-#'   really the goal, then the error can be bypassed by setting this argument
-#'   to `TRUE`. The quantiles in the first selected column will vary the fastest.
+#'   were positions in the data frame. Note that only one variable
+#'   can be selected for this operation.
 #'
 #' @return An object of the same class as `.data`.
 #' @export
 #'
 #' @examples
-#' d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-#' d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
+#' d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+#' d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
 #' tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 #'
 #' pivot_quantiles_longer(tib, "d1")
 #' pivot_quantiles_longer(tib, dplyr::ends_with("1"))
-#' pivot_quantiles_longer(tib, d1, d2)
-pivot_quantiles_longer <- function(.data, ..., .ignore_length_check = FALSE) {
-  cols <- validate_pivot_quantiles(.data, ...)
-  .data <- .data %>% mutate(across(all_of(cols), nested_quantiles))
-  if (length(cols) > 1L) {
-    lengths_check <- .data %>%
-      dplyr::transmute(across(all_of(cols), ~ map_int(.x, vctrs::vec_size))) %>%
-      as.matrix() %>%
-      apply(1, function(x) dplyr::n_distinct(x) == 1L) %>%
-      all()
-    if (lengths_check) {
-      .data <- tidyr::unnest(.data, all_of(cols), names_sep = "_")
-    } else {
-      if (.ignore_length_check) {
-        for (col in cols) {
-          .data <- .data %>% tidyr::unnest(all_of(col), names_sep = "_")
-        }
-      } else {
-        cli_abort(paste(
-          "Some selected columns contain different numbers of quantiles.",
-          "The result would be a {.emph very} long {.cls tibble}.",
-          "To do this anyway, rerun with `.ignore_length_check = TRUE`."
-        ))
-      }
-    }
-  } else {
-    .data <- .data %>% tidyr::unnest(all_of(cols))
-  }
-  .data
+#' pivot_quantiles_longer(tib, d2)
+pivot_quantiles_longer <- function(.data, ...) {
+  col <- validate_pivot_quantiles(.data, ...)
+  .data$.row <- seq_len(vctrs::vec_size(.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)
 }
 
-#' Pivot columns containing `dist_quantile` wider
+#' Pivot a column containing `quantile_pred` wider
 #'
-#' Any selected columns that contain `dist_quantiles` will be "widened" with
+#' Any selected columns that contain `quantile_pred` will be "widened" with
 #' the "taus" (quantile) serving as names and the values in the data frame.
 #' When pivoting multiple columns, the original column name will be used as
 #' a prefix.
 #'
-#' @param .data A data frame, or a data frame extension such as a tibble or
-#'   epi_df.
-#' @param ... <[`tidy-select`][dplyr::dplyr_tidy_select]> One or more unquoted
-#'   expressions separated by commas. Variable names can be used as if they
-#'   were positions in the data frame, so expressions like `x:y` can
-#'   be used to select a range of variables.
+#' @inheritParams pivot_quantiles_longer
 #'
 #' @return An object of the same class as `.data`
 #' @export
 #'
 #' @examples
-#' d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-#' d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
-#' tib <- tibble::tibble(g = c("a", "b"), d1 = d1, d2 = d2)
+#' d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+#' d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
+#' tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 #'
-#' pivot_quantiles_wider(tib, c("d1", "d2"))
-#' pivot_quantiles_wider(tib, dplyr::starts_with("d"))
+#' pivot_quantiles_wider(tib, "d1")
+#' pivot_quantiles_wider(tib, dplyr::ends_with("2"))
 #' pivot_quantiles_wider(tib, d2)
 pivot_quantiles_wider <- function(.data, ...) {
-  cols <- validate_pivot_quantiles(.data, ...)
-  .data <- .data %>% mutate(across(all_of(cols), nested_quantiles))
-  checks <- map_lgl(cols, ~ diff(range(vctrs::list_sizes(.data[[.x]]))) == 0L)
-  if (!all(checks)) {
-    nms <- cols[!checks]
-    cli_abort(c(
-      "Quantiles must be the same length and have the same set of taus.",
-      i = "Check failed for variables(s) {.var {nms}}."
-    ))
-  }
-
-  # tidyr::pivot_wider can crash if there are duplicates, this generally won't
-  # happen in our context. To avoid, silently add an index column and remove it
-  # later
-  .hidden_index <- seq_len(nrow(.data))
-  .data <- tibble::add_column(.data, .hidden_index = .hidden_index)
-  if (length(cols) > 1L) {
-    for (col in cols) {
-      .data <- .data %>%
-        tidyr::unnest(all_of(col)) %>%
-        tidyr::pivot_wider(
-          names_from = "quantile_levels", values_from = "values",
-          names_prefix = paste0(col, "_")
-        )
-    }
-  } else {
-    .data <- .data %>%
-      tidyr::unnest(all_of(cols)) %>%
-      tidyr::pivot_wider(names_from = "quantile_levels", values_from = "values")
-  }
-  select(.data, -.hidden_index)
+  col <- validate_pivot_quantiles(.data, ...)
+  .data$.row <- seq_len(vctrs::vec_size(.data))
+  wide_tib <- as_tibble(.data[[col]]) %>%
+    tidyr::pivot_wider(names_from = .quantile_levels, values_from = .pred_quantile)
+  .data <- select(.data, !all_of(col))
+  left_join(.data, wide_tib, by = ".row") %>%
+    select(!.row)
 }
 
 pivot_quantiles <- function(.data, ...) {
@@ -149,14 +70,19 @@ pivot_quantiles <- function(.data, ...) {
   lifecycle::deprecate_stop(msg)
 }
 
-validate_pivot_quantiles <- function(.data, ...) {
+validate_pivot_quantiles <- function(.data, ..., call = caller_env()) {
   expr <- rlang::expr(c(...))
   cols <- names(tidyselect::eval_select(expr, .data))
-  dqs <- map_lgl(cols, ~ is_dist_quantiles(.data[[.x]]))
-  if (!all(dqs)) {
-    nms <- cols[!dqs]
+  if (length(cols) > 1L) {
+    cli_abort(
+      "Only one column can be pivotted. Can not pivot all of: {.var {cols}}.",
+      call = call
+    )
+  }
+  if (!inherits(.data[[cols]], "quantile_pred")) {
     cli_abort(
-      "Variables(s) {.var {nms}} are not `dist_quantiles`. Cannot pivot them."
+      "{.var {cols}} is not {.cls `quantile_pred`}. Cannot pivot it.",
+      call = call
     )
   }
   cols
diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 20379b46e..75c2da70c 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -1,5 +1,6 @@
 
 # placeholder to avoid errors, but not ideal
+#' @importFrom hardhat quantile_pred
 #' @export
 mean.quantile_pred <- function(x, na.rm = FALSE, ...) {
   median(x, ...)
@@ -46,6 +47,7 @@ quantile_internal <- function(x, tau_out, middle) {
 extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
   qvals_out <- rep(NA, length(tau_out))
   good <- !is.na(qvals)
+  if (!any(good)) return(qvals_out)
   qvals <- qvals[good]
   tau <- tau[good]
 
@@ -131,7 +133,7 @@ vec_math.quantile_pred <- function(.fn, .x, ...) {
   }
   quantile_levels <- .x %@% "quantile_levels"
   .x <- as.matrix(.x)
-  hardhat::quantile_pred(.fn(.x), quantile_levels)
+  quantile_pred(.fn(.x), quantile_levels)
 }
 
 #' @importFrom vctrs vec_arith vec_arith.numeric
@@ -145,14 +147,16 @@ vec_arith.quantile_pred <- function(op, x, y, ...) {
 #' @method vec_arith.quantile_pred numeric
 vec_arith.quantile_pred.numeric <- function(op, x, y, ...) {
   op_fn <- getExportedValue("base", op)
-  out <- op_fn(as.matrix(x), y)
-  hardhat::quantile_pred(out, x %@% "quantile_levels")
+  l <- vctrs::vec_recycle_common(x = x, y = y)
+  out <- op_fn(as.matrix(l$x), l$y)
+  quantile_pred(out, x %@% "quantile_levels")
 }
 
 #' @export
 #' @method vec_arith.numeric quantile_pred
 vec_arith.numeric.quantile_pred <- function(op, x, y, ...) {
   op_fn <- getExportedValue("base", op)
-  out <- op_fn(x, as.matrix(y))
-  hardhat::quantile_pred(out, y %@% "quantile_levels")
+  l <- vctrs::vec_recycle_common(x = x, y = y)
+  out <- op_fn(l$x, as.matrix(l$y))
+  quantile_pred(out, y %@% "quantile_levels")
 }
diff --git a/R/weighted_interval_score.R b/R/weighted_interval_score.R
index 567ae4e69..aa0c816a3 100644
--- a/R/weighted_interval_score.R
+++ b/R/weighted_interval_score.R
@@ -7,22 +7,21 @@
 #' al. (2020)](https://arxiv.org/abs/2005.12881) for discussion in the context
 #' of COVID-19 forecasting.
 #'
-#' @param x distribution. A vector of class distribution. Ideally, this vector
-#'   contains `dist_quantiles()`, though other distributions are supported when
-#'   `quantile_levels` is specified. See below.
+#' @param x A vector of class `quantile_pred`.
 #' @param actual double. Actual value(s)
 #' @param quantile_levels probabilities. If specified, the score will be
-#'   computed at this set of levels.
-#' @param na_handling character. Determines how `quantile_levels` without a
-#'   corresponding `value` are handled. For `"impute"`, missing values will be
+#'   computed at this set of levels. Otherwise, those present in `x` will be
+#'   used.
+#' @param na_handling character. Determines missing values are handled.
+#'   For `"impute"`, missing values will be
 #'   calculated if possible using the available quantiles. For `"drop"`,
 #'   explicitly missing values are ignored in the calculation of the score, but
 #'   implicitly missing values are imputed if possible.
 #'   For `"propogate"`, the resulting score will be `NA` if any missing values
-#'   exist in the original `quantile_levels`. Finally, if
+#'   exist. Finally, if
 #'   `quantile_levels` is specified, `"fail"` will result in
 #'   the score being `NA` when any required quantile levels (implicit or explicit)
-#'   are do not have corresponding values.
+#'   do not have corresponding values.
 #' @param ... not used
 #'
 #' @return a vector of nonnegative scores.
@@ -30,24 +29,23 @@
 #' @export
 #' @examples
 #' quantile_levels <- c(.2, .4, .6, .8)
-#' predq_1 <- 1:4 #
-#' predq_2 <- 8:11
-#' dstn <- dist_quantiles(list(predq_1, predq_2), quantile_levels)
+#' predq1 <- 1:4 #
+#' predq2 <- 8:11
+#' dstn <- quantile_pred(rbind(predq1, predq2), quantile_levels)
 #' actual <- c(3.3, 7.1)
 #' weighted_interval_score(dstn, actual)
 #' weighted_interval_score(dstn, actual, c(.25, .5, .75))
 #'
-#' library(distributional)
-#' dstn <- dist_normal(c(.75, 2))
-#' weighted_interval_score(dstn, 1, c(.25, .5, .75))
-#'
 #' # Missing value behaviours
-#' dstn <- dist_quantiles(c(1, 2, NA, 4), 1:4 / 5)
+#' dstn <- quantile_pred(matrix(c(1, 2, NA, 4), nrow = 1), 1:4 / 5)
 #' weighted_interval_score(dstn, 2.5)
 #' weighted_interval_score(dstn, 2.5, 1:9 / 10)
 #' weighted_interval_score(dstn, 2.5, 1:9 / 10, na_handling = "drop")
 #' weighted_interval_score(dstn, 2.5, na_handling = "propagate")
-#' weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10,
+#' weighted_interval_score(
+#'   quantile_pred(matrix(1:4, nrow = 1), 1:4 / 5),
+#'   actual = 2.5,
+#'   quantile_levels = 1:9 / 10,
 #'   na_handling = "fail"
 #' )
 #'
@@ -101,7 +99,7 @@ weighted_interval_score.quantile_pred <- function(
   }
   tau <- quantile_levels %||% old_quantile_levels
   x <- extrapolate_quantiles(x, tau, replace_na = (na_handling == "impute"))
-  x <- as.matrix(x)[, attr(x, "quantile_levels") %in% tau]
+  x <- as.matrix(x)[, attr(x, "quantile_levels") %in% tau, drop = FALSE]
   na_rm <- (na_handling == "drop")
   map2_dbl(vctrs::vec_chop(x), actual, ~ wis_one_quantile(.x, tau, .y, na_rm))
 }
diff --git a/man/extrapolate_quantiles.Rd b/man/extrapolate_quantiles.Rd
index 4b1d1282c..b645b85fa 100644
--- a/man/extrapolate_quantiles.Rd
+++ b/man/extrapolate_quantiles.Rd
@@ -7,37 +7,29 @@
 extrapolate_quantiles(x, probs, replace_na = TRUE, ...)
 }
 \arguments{
-\item{x}{a \code{distribution} vector}
+\item{x}{A vector of class \code{quantile_pred}.}
 
 \item{probs}{a vector of probabilities at which to calculate quantiles}
 
 \item{replace_na}{logical. If \code{x} contains \code{NA}'s, these are imputed if
-possible (if \code{TRUE}) or retained (if \code{FALSE}). This only effects
-elements of class \code{dist_quantiles}.}
+possible (if \code{TRUE}) or retained (if \code{FALSE}).}
 
 \item{...}{additional arguments passed on to the \code{quantile} method}
 }
 \value{
-a \code{distribution} vector containing \code{dist_quantiles}. Any elements
-of \code{x} which were originally \code{dist_quantiles} will now have a superset
+a \code{quantile_pred} vector. Each element
+of \code{x} will now have a superset
 of the original \code{quantile_values} (the union of those and \code{probs}).
 }
 \description{
-Summarize a distribution with a set of quantiles
+This function takes a \code{quantile_pred} vector and returns the same
+type of object, expanded to include
+\emph{additional} quantiles computed at \code{probs}. If you want behaviour more
+similar to \code{\link[stats:quantile]{stats::quantile()}}, then \code{quantile(x,...)} may be more
+appropriate.
 }
 \examples{
-library(distributional)
-dstn <- dist_normal(c(10, 2), c(5, 10))
-extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-
-dstn <- dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-# because this distribution is already quantiles, any extra quantiles are
-# appended
-extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
-
-dstn <- c(
-  dist_normal(c(10, 2), c(5, 10)),
-  dist_quantiles(list(1:4, 8:11), list(c(.2, .4, .6, .8)))
-)
-extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75))
+dstn <- quantile_dstn(rbind(1:4, 8:11), c(.2, .4, .6, .8))
+# extra quantiles are appended
+as.tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
 }
diff --git a/man/layer_cdc_flatline_quantiles.Rd b/man/layer_cdc_flatline_quantiles.Rd
index 35a6c03e8..12b173da1 100644
--- a/man/layer_cdc_flatline_quantiles.Rd
+++ b/man/layer_cdc_flatline_quantiles.Rd
@@ -62,7 +62,7 @@ an updated \code{frosting} postprocessor. Calling \code{\link[=predict]{predict(
 in an additional \verb{<list-col>} named \code{.pred_distn_all} containing 2-column
 \code{\link[tibble:tibble]{tibble::tibble()}}'s. For each
 desired combination of \code{key}'s, the tibble will contain one row per ahead
-with the associated \code{\link[=dist_quantiles]{dist_quantiles()}}.
+with the associated \code{\link[=quantile_pred]{quantile_pred()}}.
 }
 \description{
 This layer creates quantile forecasts by taking a sample from the
diff --git a/man/nested_quantiles.Rd b/man/nested_quantiles.Rd
deleted file mode 100644
index b1571450e..000000000
--- a/man/nested_quantiles.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/pivot_quantiles.R
-\name{nested_quantiles}
-\alias{nested_quantiles}
-\title{Turn a vector of quantile distributions into a list-col}
-\usage{
-nested_quantiles(x)
-}
-\arguments{
-\item{x}{a \code{distribution} containing \code{dist_quantiles}}
-}
-\value{
-a list-col
-}
-\description{
-Turn a vector of quantile distributions into a list-col
-}
-\examples{
-edf <- covid_case_death_rates[1:3, ]
-edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
-
-edf_nested <- edf \%>\% mutate(q = nested_quantiles(q))
-edf_nested \%>\% unnest(q)
-}
diff --git a/man/pivot_quantiles_longer.Rd b/man/pivot_quantiles_longer.Rd
index 9879d5d07..b872d0c97 100644
--- a/man/pivot_quantiles_longer.Rd
+++ b/man/pivot_quantiles_longer.Rd
@@ -2,41 +2,33 @@
 % Please edit documentation in R/pivot_quantiles.R
 \name{pivot_quantiles_longer}
 \alias{pivot_quantiles_longer}
-\title{Pivot columns containing \code{dist_quantile} longer}
+\title{Pivot a column containing \code{quantile_pred} longer}
 \usage{
-pivot_quantiles_longer(.data, ..., .ignore_length_check = FALSE)
+pivot_quantiles_longer(.data, ...)
 }
 \arguments{
 \item{.data}{A data frame, or a data frame extension such as a tibble or
 epi_df.}
 
-\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One or more unquoted
+\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One unquoted
 expressions separated by commas. Variable names can be used as if they
-were positions in the data frame, so expressions like \code{x:y} can
-be used to select a range of variables.}
-
-\item{.ignore_length_check}{If multiple columns are selected, as long as
-each row has contains the same number of quantiles, the result will be
-reasonable. But if, for example, \code{var1[1]} has 5 quantiles while \code{var2[1]}
-has 7, then the only option would be to recycle everything, creating a
-\emph{very} long result. By default, this would throw an error. But if this is
-really the goal, then the error can be bypassed by setting this argument
-to \code{TRUE}. The quantiles in the first selected column will vary the fastest.}
+were positions in the data frame. Note that only one variable
+can be selected for this operation.}
 }
 \value{
 An object of the same class as \code{.data}.
 }
 \description{
-Selected columns that contain \code{dist_quantiles} will be "lengthened" with
+A column that contains \code{quantile_pred} will be "lengthened" with
 the quantile levels serving as 1 column and the values as another. If
 multiple columns are selected, these will be prefixed with the column name.
 }
 \examples{
-d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
+d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
 tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 
 pivot_quantiles_longer(tib, "d1")
 pivot_quantiles_longer(tib, dplyr::ends_with("1"))
-pivot_quantiles_longer(tib, d1, d2)
+pivot_quantiles_longer(tib, d2)
 }
diff --git a/man/pivot_quantiles_wider.Rd b/man/pivot_quantiles_wider.Rd
index e477777ca..01f85a70a 100644
--- a/man/pivot_quantiles_wider.Rd
+++ b/man/pivot_quantiles_wider.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/pivot_quantiles.R
 \name{pivot_quantiles_wider}
 \alias{pivot_quantiles_wider}
-\title{Pivot columns containing \code{dist_quantile} wider}
+\title{Pivot a column containing \code{quantile_pred} wider}
 \usage{
 pivot_quantiles_wider(.data, ...)
 }
@@ -10,26 +10,26 @@ pivot_quantiles_wider(.data, ...)
 \item{.data}{A data frame, or a data frame extension such as a tibble or
 epi_df.}
 
-\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One or more unquoted
+\item{...}{<\code{\link[dplyr:dplyr_tidy_select]{tidy-select}}> One unquoted
 expressions separated by commas. Variable names can be used as if they
-were positions in the data frame, so expressions like \code{x:y} can
-be used to select a range of variables.}
+were positions in the data frame. Note that only one variable
+can be selected for this operation.}
 }
 \value{
 An object of the same class as \code{.data}
 }
 \description{
-Any selected columns that contain \code{dist_quantiles} will be "widened" with
+Any selected columns that contain \code{quantile_pred} will be "widened" with
 the "taus" (quantile) serving as names and the values in the data frame.
 When pivoting multiple columns, the original column name will be used as
 a prefix.
 }
 \examples{
-d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
-tib <- tibble::tibble(g = c("a", "b"), d1 = d1, d2 = d2)
+d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
+tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 
-pivot_quantiles_wider(tib, c("d1", "d2"))
-pivot_quantiles_wider(tib, dplyr::starts_with("d"))
+pivot_quantiles_wider(tib, "d1")
+pivot_quantiles_wider(tib, dplyr::ends_with("2"))
 pivot_quantiles_wider(tib, d2)
 }
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index eb715be63..973a85879 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -66,7 +66,7 @@ pl <- pl \%>\%
     x = x[length(x) - 20] + ahead / 100 * 2 * pi,
     ahead = NULL
   ) \%>\%
-  pivot_wider(names_from = quantile_levels, values_from = values)
+  pivot_wider(names_from = distn_quantile_levels, values_from = distn_value)
 plot(x, y, pch = 16, xlim = c(pi, 2 * pi), col = "lightgrey")
 curve(sin(x), add = TRUE)
 abline(v = fd, lty = 2)
diff --git a/man/weighted_interval_score.Rd b/man/weighted_interval_score.Rd
index 4b7c796ea..22a616b70 100644
--- a/man/weighted_interval_score.Rd
+++ b/man/weighted_interval_score.Rd
@@ -13,25 +13,24 @@ weighted_interval_score(
 )
 }
 \arguments{
-\item{x}{distribution. A vector of class distribution. Ideally, this vector
-contains \code{dist_quantiles()}, though other distributions are supported when
-\code{quantile_levels} is specified. See below.}
+\item{x}{A vector of class \code{quantile_pred}.}
 
 \item{actual}{double. Actual value(s)}
 
 \item{quantile_levels}{probabilities. If specified, the score will be
-computed at this set of levels.}
+computed at this set of levels. Otherwise, those present in \code{x} will be
+used.}
 
-\item{na_handling}{character. Determines how \code{quantile_levels} without a
-corresponding \code{value} are handled. For \code{"impute"}, missing values will be
+\item{na_handling}{character. Determines missing values are handled.
+For \code{"impute"}, missing values will be
 calculated if possible using the available quantiles. For \code{"drop"},
 explicitly missing values are ignored in the calculation of the score, but
 implicitly missing values are imputed if possible.
 For \code{"propogate"}, the resulting score will be \code{NA} if any missing values
-exist in the original \code{quantile_levels}. Finally, if
+exist. Finally, if
 \code{quantile_levels} is specified, \code{"fail"} will result in
 the score being \code{NA} when any required quantile levels (implicit or explicit)
-are do not have corresponding values.}
+do not have corresponding values.}
 
 \item{...}{not used}
 }
@@ -47,24 +46,23 @@ of COVID-19 forecasting.
 }
 \examples{
 quantile_levels <- c(.2, .4, .6, .8)
-predq_1 <- 1:4 #
-predq_2 <- 8:11
-dstn <- dist_quantiles(list(predq_1, predq_2), quantile_levels)
+predq1 <- 1:4 #
+predq2 <- 8:11
+dstn <- quantile_pred(rbind(predq1, predq2), quantile_levels)
 actual <- c(3.3, 7.1)
 weighted_interval_score(dstn, actual)
 weighted_interval_score(dstn, actual, c(.25, .5, .75))
 
-library(distributional)
-dstn <- dist_normal(c(.75, 2))
-weighted_interval_score(dstn, 1, c(.25, .5, .75))
-
 # Missing value behaviours
-dstn <- dist_quantiles(c(1, 2, NA, 4), 1:4 / 5)
+dstn <- quantile_pred(matrix(c(1, 2, NA, 4), nrow = 1), 1:4 / 5)
 weighted_interval_score(dstn, 2.5)
 weighted_interval_score(dstn, 2.5, 1:9 / 10)
 weighted_interval_score(dstn, 2.5, 1:9 / 10, na_handling = "drop")
 weighted_interval_score(dstn, 2.5, na_handling = "propagate")
-weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10,
+weighted_interval_score(
+  quantile_pred(matrix(1:4, nrow = 1), 1:4 / 5),
+  actual = 2.5,
+  quantile_levels = 1:9 / 10,
   na_handling = "fail"
 )
 
diff --git a/tests/testthat/_snaps/pivot_quantiles.md b/tests/testthat/_snaps/pivot_quantiles.md
index ca775a18f..13dd81916 100644
--- a/tests/testthat/_snaps/pivot_quantiles.md
+++ b/tests/testthat/_snaps/pivot_quantiles.md
@@ -3,47 +3,38 @@
     Code
       pivot_quantiles_wider(tib, a)
     Condition
-      Error in `UseMethod()`:
-      ! no applicable method for 'family' applied to an object of class "c('integer', 'numeric')"
+      Error in `pivot_quantiles_wider()`:
+      ! `a` is not <`quantile_pred`>. Cannot pivot it.
 
 ---
 
     Code
-      pivot_quantiles_wider(tib, c)
+      pivot_quantiles_wider(tib, d1, d2)
     Condition
-      Error in `validate_pivot_quantiles()`:
-      ! Variables(s) `c` are not `dist_quantiles`. Cannot pivot them.
+      Error in `pivot_quantiles_wider()`:
+      ! Only one column can be pivotted. Can not pivot all of: `d1` and `d2`.
 
 ---
 
     Code
-      pivot_quantiles_wider(tib, d1)
+      pivot_quantiles_longer(tib, d1, d2)
     Condition
-      Error in `pivot_quantiles_wider()`:
-      ! Quantiles must be the same length and have the same set of taus.
-      i Check failed for variables(s) `d1`.
+      Error in `pivot_quantiles_longer()`:
+      ! Only one column can be pivotted. Can not pivot all of: `d1` and `d2`.
 
 # quantile pivotting longer behaves
 
     Code
       pivot_quantiles_longer(tib, a)
     Condition
-      Error in `UseMethod()`:
-      ! no applicable method for 'family' applied to an object of class "c('integer', 'numeric')"
-
----
-
-    Code
-      pivot_quantiles_longer(tib, c)
-    Condition
-      Error in `validate_pivot_quantiles()`:
-      ! Variables(s) `c` are not `dist_quantiles`. Cannot pivot them.
+      Error in `pivot_quantiles_longer()`:
+      ! `a` is not <`quantile_pred`>. Cannot pivot it.
 
 ---
 
     Code
-      pivot_quantiles_longer(tib, d1, d3)
+      pivot_quantiles_longer(tib, d1, d2)
     Condition
       Error in `pivot_quantiles_longer()`:
-      ! Some selected columns contain different numbers of quantiles. The result would be a very long <tibble>. To do this anyway, rerun with `.ignore_length_check = TRUE`.
+      ! Only one column can be pivotted. Can not pivot all of: `d1` and `d2`.
 
diff --git a/tests/testthat/_snaps/wis-quantile_pred.md b/tests/testthat/_snaps/wis-quantile_pred.md
new file mode 100644
index 000000000..f13bd74db
--- /dev/null
+++ b/tests/testthat/_snaps/wis-quantile_pred.md
@@ -0,0 +1,16 @@
+# wis dispatches and produces the correct values
+
+    Code
+      weighted_interval_score(1:10, 10)
+    Condition
+      Error in `UseMethod()`:
+      ! no applicable method for 'weighted_interval_score' applied to an object of class "c('integer', 'numeric')"
+
+---
+
+    Code
+      weighted_interval_score(quantile_pred(rbind(1:4, 8:11), 1:4 / 5), 1:3)
+    Condition
+      Error in `weighted_interval_score.quantile_pred()`:
+      ! Assertion on 'actual' failed: Must have length 2, but has length 3.
+
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index 1736ded2d..2941037f5 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -23,11 +23,9 @@ test_that("Returns expected number or rows and columns", {
   expect_equal(nrow(p), 3L)
   expect_named(p, c("geo_value", "time_value", ".pred", ".pred_distn"))
 
-  nested <- p %>% dplyr::mutate(.quantiles = nested_quantiles(.pred_distn))
-  unnested <- nested %>% tidyr::unnest(.quantiles)
-
+  unnested <- p %>% pivot_quantiles_longer(.pred_distn)
   expect_equal(nrow(unnested), 12L)
-  expect_equal(unique(unnested$quantile_levels), c(.0275, .5, .8, .95))
+  expect_equal(unique(unnested$.pred_distn_quantile_level), c(.0275, 0.5, .8, .95))
 })
 
 
diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index 2ff43a165..64e8608b2 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -56,11 +56,10 @@ test_that("thresholds additional columns", {
   expect_equal(round(p$.pred, digits = 3), c(0.180, 0.180, 0.310))
   expect_named(p, c("geo_value", "time_value", ".pred", ".pred_distn"))
   p <- p %>%
-    dplyr::mutate(.quantiles = nested_quantiles(.pred_distn)) %>%
-    tidyr::unnest(.quantiles)
+    pivot_quantiles_longer(.pred_distn)
   expect_equal(
-    round(p$values, digits = 3),
-    c(0.180, 0.180, 0.31, 0.180, 0.180, .18, 0.310, .31, .31)
+    round(p$.pred_distn_value, digits = 3),
+    c(0.180, 0.180, 0.31, 0.180, .18, .18, .31, 0.310, .31)
   )
-  expect_equal(p$quantile_levels, rep(c(.1, 0.5, .9), times = 3))
+  expect_equal(p$.pred_distn_quantile_level, rep(c(.1, 0.5, .9), times = 3))
 })
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index 1639058e2..a4362cffb 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -1,76 +1,44 @@
 test_that("quantile pivotting wider behaves", {
   tib <- tibble::tibble(a = 1:5, b = 6:10)
   expect_snapshot(error = TRUE, pivot_quantiles_wider(tib, a))
-  tib$c <- rep(dist_normal(), 5)
-  expect_snapshot(error = TRUE, pivot_quantiles_wider(tib, c))
 
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:5, 1:4 / 5))
-  # different quantiles
-  tib <- tib[1:2, ]
-  tib$d1 <- d1
-  expect_snapshot(error = TRUE, pivot_quantiles_wider(tib, d1))
-
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 2:4 / 4))
-  tib$d1 <- d1
-  # would want to error (mismatched quantiles), but hard to check efficiently
-  expect_silent(pivot_quantiles_wider(tib, d1))
+  d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+  d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
+  tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-  d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
-  tib <- tibble::tibble(g = c("a", "b"), d1 = d1, d2 = d2)
+  # too many columns
+  expect_snapshot(error = TRUE, pivot_quantiles_wider(tib, d1, d2))
+  expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, d1, d2))
 
 
-  expect_length(pivot_quantiles_wider(tib, c("d1", "d2")), 7L)
-  expect_length(pivot_quantiles_wider(tib, tidyselect::starts_with("d")), 7L)
-  expect_length(pivot_quantiles_wider(tib, d2), 5L)
+  expect_length(pivot_quantiles_wider(tib, d1), 5L)
+  expect_length(pivot_quantiles_wider(tib, tidyselect::ends_with("1")), 5L)
+  expect_equal(vctrs::vec_size(pivot_quantiles_longer(tib, d2)), 6L)
 })
 
 test_that("pivotting wider still works if there are duplicates", {
   # previously this would produce a warning if pivotted because the
   # two rows of the result are identical
-  tb <- tibble(.pred = dist_quantiles(list(1:3, 1:3), list(c(.1, .5, .9))))
+  tb <- tibble(.pred = quantile_pred(rbind(1:3, 1:3), c(.1, .5, .9)))
   res <- tibble(`0.1` = c(1, 1), `0.5` = c(2, 2), `0.9` = c(3, 3))
-  expect_identical(tb %>% pivot_quantiles_wider(.pred), res)
+  expect_equal(tb %>% pivot_quantiles_wider(.pred), res)
 })
 
 
 test_that("quantile pivotting longer behaves", {
   tib <- tibble::tibble(a = 1:5, b = 6:10)
   expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, a))
-  tib$c <- rep(dist_normal(), 5)
-  expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, c))
-
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:5, 1:4 / 5))
-  # different quantiles
-  tib <- tib[1:2, ]
-  tib$d1 <- d1
-  expect_length(pivot_quantiles_longer(tib, d1), 5L)
-  expect_identical(nrow(pivot_quantiles_longer(tib, d1)), 7L)
-  expect_identical(pivot_quantiles_longer(tib, d1)$values, as.double(c(1:3, 2:5)))
-
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 2:4 / 4))
-  tib$d1 <- d1
-  expect_silent(pivot_quantiles_longer(tib, d1))
 
-  d1 <- c(dist_quantiles(1:3, 1:3 / 4), dist_quantiles(2:4, 1:3 / 4))
-  d2 <- c(dist_quantiles(2:4, 2:4 / 5), dist_quantiles(3:5, 2:4 / 5))
-  tib <- tibble::tibble(g = c("a", "b"), d1 = d1, d2 = d2)
+  d1 <- quantile_pred(rbind(1:3, 2:4), 1:3 / 4)
+  d2 <- quantile_pred(rbind(2:4, 3:5), 2:4 / 5)
+  tib <- tibble(g = c("a", "b"), d1 = d1, d2 = d2)
 
+  # too many columns
+  expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, d1, d2))
 
-  expect_length(pivot_quantiles_longer(tib, c("d1", "d2")), 5L)
-  expect_identical(nrow(pivot_quantiles_longer(tib, c("d1", "d2"))), 6L)
-  expect_silent(pivot_quantiles_longer(tib, tidyselect::starts_with("d")))
-  expect_length(pivot_quantiles_longer(tib, d2), 4L)
+  # different quantiles
+  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))
 
-  tib$d3 <- c(dist_quantiles(2:5, 2:5 / 6), dist_quantiles(3:6, 2:5 / 6))
-  # now the cols have different numbers of quantiles
-  expect_snapshot(error = TRUE, pivot_quantiles_longer(tib, d1, d3))
-  expect_length(
-    pivot_quantiles_longer(tib, d1, d3, .ignore_length_check = TRUE),
-    6L
-  )
-  expect_identical(
-    pivot_quantiles_longer(tib, d1, d3, .ignore_length_check = TRUE)$d1_values,
-    as.double(rep(c(1:3, 2:4), each = 4))
-  )
 })
diff --git a/tests/testthat/test-wis-dist-quantiles.R b/tests/testthat/test-wis-quantile_pred.R
similarity index 65%
rename from tests/testthat/test-wis-dist-quantiles.R
rename to tests/testthat/test-wis-quantile_pred.R
index 937793189..187344af9 100644
--- a/tests/testthat/test-wis-dist-quantiles.R
+++ b/tests/testthat/test-wis-quantile_pred.R
@@ -8,45 +8,38 @@ test_that("wis dispatches and produces the correct values", {
   actual <- 5
   expected <- c(wis_one_pred(q1, tau, actual), wis_one_pred(q2, tau, actual))
 
-  dstn <- dist_quantiles(list(q1, q2), tau)
+  dstn <- quantile_pred(rbind(q1, q2), tau)
   expect_equal(weighted_interval_score(dstn, actual), expected)
 
   # works with a single dstn
   q <- sort(10 * rexp(23))
   tau0 <- c(.01, .025, 1:19 / 20, .975, .99)
-  dst <- dist_quantiles(q, tau0)
+  dst <- quantile_pred(rbind(q), tau0)
   expect_equal(weighted_interval_score(dst, 10), wis_one_pred(q, tau0, 10))
 
   # returns NA when expected
-  dst <- dist_quantiles(rep(NA, 3), c(.2, .5, .95))
+  dst <- quantile_pred(rbind(rep(NA, 3)), c(.2, .5, .95))
   expect_true(is.na(weighted_interval_score(dst, 10)))
   expect_equal(
     weighted_interval_score(dstn, c(NA, actual)),
     c(NA, wis_one_pred(q2, tau, actual))
   )
 
-  # errors for non distributions
+  # errors for non quantile_pred
   expect_snapshot(error = TRUE, weighted_interval_score(1:10, 10))
-  expect_warning(w <- weighted_interval_score(dist_normal(1), 10))
-  expect_true(all(is.na(w)))
-  expect_warning(w <- weighted_interval_score(
-    c(dist_normal(), dist_quantiles(1:5, 1:5 / 6)),
-    10
-  ))
-  expect_equal(w, c(NA, wis_one_pred(1:5, 1:5 / 6, 10)))
 
   # errors if sizes don't match
   expect_snapshot(error = TRUE, weighted_interval_score(
-    dist_quantiles(list(1:4, 8:11), 1:4 / 5), # length 2
+    quantile_pred(rbind(1:4, 8:11), 1:4 / 5), # length 2
     1:3
   ))
 
   #' # Missing value behaviours
-  dstn <- dist_quantiles(c(1, 2, NA, 4), 1:4 / 5)
+  dstn <- quantile_pred(rbind(c(1, 2, NA, 4)), 1:4 / 5)
   expect_equal(weighted_interval_score(dstn, 2.5), 0.5)
   expect_equal(weighted_interval_score(dstn, 2.5, c(2, 4, 5, 6, 8) / 10), 0.4)
   expect_equal(
-    weighted_interval_score(dist_quantiles(c(1, 2, NA, 4), 1:4 / 5), 3, na_handling = "drop"),
+    weighted_interval_score(dstn, 3, na_handling = "drop"),
     2 / 3
   )
   expect_equal(
@@ -56,5 +49,9 @@ test_that("wis dispatches and produces the correct values", {
   expect_true(is.na(
     weighted_interval_score(dstn, 2.5, na_handling = "propagate")
   ))
-  weighted_interval_score(dist_quantiles(1:4, 1:4 / 5), 2.5, 1:9 / 10, na_handling = "fail")
+  expect_true(is.na(
+    weighted_interval_score(
+      quantile_pred(rbind(1:4), 1:4 / 5), 2.5, 1:9 / 10, na_handling = "fail"
+    )
+  ))
 })

From b7b441659a431ca5b26ee93e8c66861d614dc176 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Mon, 24 Feb 2025 18:01:51 -0800
Subject: [PATCH 07/22] tests pass, checks don't

---
 NAMESPACE                                   |   12 +-
 R/epipredict-package.R                      |    1 +
 R/extrapolate_quantiles.R                   |    4 +-
 R/layer_point_from_distn.R                  |    6 +-
 R/layer_threshold_preds.R                   |    1 +
 R/pivot_quantiles.R                         |   50 +
 R/quantile_pred-methods.R                   |  105 ++
 R/reexports-tidymodels.R                    |   37 +
 R/utils-misc.R                              |    2 +-
 man/dist_quantiles.Rd                       |   28 +
 man/extrapolate_quantiles.Rd                |    4 +-
 man/nested_quantiles.Rd                     |   31 +
 man/reexports.Rd                            |    7 +-
 tests/testthat/_snaps/dist_quantiles.md     |   56 -
 tests/testthat/_snaps/pivot_quantiles.md    |   18 +
 tests/testthat/_snaps/snapshots.md          | 1484 +++++++------------
 tests/testthat/_snaps/wis-dist-quantiles.md |   17 -
 tests/testthat/test-grf_quantiles.R         |    2 +-
 tests/testthat/test-pivot_quantiles.R       |    9 +
 tests/testthat/test-population_scaling.R    |   13 +-
 tests/testthat/test-step_adjust_latency.R   |    8 +-
 21 files changed, 829 insertions(+), 1066 deletions(-)
 create mode 100644 R/reexports-tidymodels.R
 create mode 100644 man/dist_quantiles.Rd
 create mode 100644 man/nested_quantiles.Rd
 delete mode 100644 tests/testthat/_snaps/dist_quantiles.md
 delete mode 100644 tests/testthat/_snaps/wis-dist-quantiles.md

diff --git a/NAMESPACE b/NAMESPACE
index 204175a04..4c43e2f45 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -119,6 +119,7 @@ S3method(vec_arith,quantile_pred)
 S3method(vec_arith.numeric,quantile_pred)
 S3method(vec_arith.quantile_pred,numeric)
 S3method(vec_math,quantile_pred)
+S3method(vec_proxy_equal,quantile_pred)
 S3method(weighted_interval_score,quantile_pred)
 export("%>%")
 export(Add_model)
@@ -137,7 +138,6 @@ export(arx_class_epi_workflow)
 export(arx_classifier)
 export(arx_fcast_epi_workflow)
 export(arx_forecaster)
-export(as_epi_df)
 export(as_tibble)
 export(autoplot)
 export(bake)
@@ -149,12 +149,14 @@ export(climate_args_list)
 export(climatological_forecaster)
 export(default_epi_recipe_blueprint)
 export(detect_layer)
+export(dist_quantiles)
 export(epi_recipe)
 export(epi_recipe_blueprint)
 export(epi_workflow)
 export(extract_argument)
 export(extract_frosting)
 export(extract_layers)
+export(extract_quantile_levels)
 export(extrapolate_quantiles)
 export(filter)
 export(fit)
@@ -182,6 +184,7 @@ export(layer_quantile_distn)
 export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
+export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
 export(pivot_longer)
@@ -189,6 +192,7 @@ export(pivot_quantiles_longer)
 export(pivot_quantiles_wider)
 export(pivot_wider)
 export(prep)
+export(quantile_pred)
 export(quantile_reg)
 export(rand_id)
 export(remove_epi_recipe)
@@ -276,6 +280,7 @@ importFrom(ggplot2,geom_linerange)
 importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
 importFrom(glue,glue)
+importFrom(hardhat,extract_quantile_levels)
 importFrom(hardhat,extract_recipe)
 importFrom(hardhat,quantile_pred)
 importFrom(hardhat,refresh_blueprint)
@@ -284,6 +289,7 @@ importFrom(lubridate,"%m-%")
 importFrom(lubridate,leap_year)
 importFrom(lubridate,month)
 importFrom(lubridate,yday)
+importFrom(lifecycle,deprecated)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)
 importFrom(recipes,bake)
@@ -330,8 +336,12 @@ importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)
+importFrom(vctrs,as_list_of)
+importFrom(vctrs,new_vctr)
 importFrom(vctrs,vec_arith)
 importFrom(vctrs,vec_arith.numeric)
 importFrom(vctrs,vec_cast)
 importFrom(vctrs,vec_math)
+importFrom(vctrs,vec_proxy_equal)
+importFrom(vctrs,vec_recycle_common)
 importFrom(workflows,extract_preprocessor)
diff --git a/R/epipredict-package.R b/R/epipredict-package.R
index 9ee832580..fae8c3c65 100644
--- a/R/epipredict-package.R
+++ b/R/epipredict-package.R
@@ -10,6 +10,7 @@
 #' @importFrom dplyr inner_join
 #' @importFrom dplyr summarize filter mutate select left_join rename ungroup
 #' @importFrom epiprocess growth_rate growth_rate_params is_epi_df
+#' @importFrom lifecycle deprecated
 #' @importFrom magrittr extract2
 #' @importFrom rlang := !! %||% as_function global_env set_names !!! caller_arg
 #' @importFrom rlang is_logical is_true inject enquo enquos expr sym arg_match
diff --git a/R/extrapolate_quantiles.R b/R/extrapolate_quantiles.R
index 82116c1d3..c7a9a3b6b 100644
--- a/R/extrapolate_quantiles.R
+++ b/R/extrapolate_quantiles.R
@@ -18,9 +18,9 @@
 #' @export
 #'
 #' @examples
-#' dstn <- quantile_dstn(rbind(1:4, 8:11), c(.2, .4, .6, .8))
+#' dstn <- quantile_pred(rbind(1:4, 8:11), c(.2, .4, .6, .8))
 #' # extra quantiles are appended
-#' as.tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
+#' as_tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
 extrapolate_quantiles <- function(x, probs, replace_na = TRUE, ...) {
   UseMethod("extrapolate_quantiles")
 }
diff --git a/R/layer_point_from_distn.R b/R/layer_point_from_distn.R
index 5af434892..5d293992f 100644
--- a/R/layer_point_from_distn.R
+++ b/R/layer_point_from_distn.R
@@ -78,10 +78,10 @@ layer_point_from_distn_new <- function(type, name, id) {
 slather.layer_point_from_distn <-
   function(object, components, workflow, new_data, ...) {
     dstn <- components$predictions$.pred
-    if (!inherits(dstn, "distribution")) {
-      rlang::warn(
+    if (!(inherits(dstn, "quantile_pred") | inherits(dstn, "distribution"))) {
+      cli_warn(
         c("`layer_point_from_distn` requires distributional predictions.",
-          i = "These are of class {class(dstn)}. Ignoring this layer."
+          i = "These are of class {.cls {class(dstn)}}. Ignoring this layer."
         )
       )
       return(components)
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index c0903ffda..9acf06725 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -63,6 +63,7 @@ layer_threshold_new <-
 
 # restrict various objects to the interval [lower, upper]
 #' @export
+#' @keywords internal
 snap <- function(x, lower, upper, ...) {
   UseMethod("snap")
 }
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index 37f08b46f..8e846d7e6 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -1,3 +1,53 @@
+#' Turn a vector of quantile distributions into a list-col
+#'
+#' `r lifecycle::badge("deprecated")`
+#'
+#' This function is deprecated. The recommended alternative is
+#' [hardhat::quantile_pred()] with [tibble::as_tibble()]
+
+#'
+#' @param x a `distribution` containing `dist_quantiles`
+#'
+#' @return a list-col
+#' @export
+#'
+#' @examples
+#' .pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+#' nested_quantiles(.pred_quantile)
+#'
+#' .pred_quantile %>%
+#'   as_tibble() %>%
+#'   tidyr::nest(.by = .row) %>%
+#'   dplyr::select(-.row)
+#'
+nested_quantiles <- function(x) {
+  lifecycle::deprecate_warn("0.1.11", "nested_quantiles()", "hardhat::quantile_pred()")
+  if (inherits(x, "distribution")) {
+    if (requireNamespace("distributional")) {
+      x <- vctrs::vec_data(x)
+      return(distributional:::dist_apply(x, .f = function(z) {
+        as_tibble(vctrs::vec_data(z)) %>%
+          mutate(across(everything(), as.double)) %>%
+          vctrs::list_of()
+      }))
+    } else {
+      cli_abort(c(
+        "`nested_quantiles()` is deprecated and the {.pkg distributional}",
+        `!` = "package is not installed.",
+        i = "See {.fn hardhat::quantile_pred}."
+      ))
+    }
+  }
+  if (inherits(x, "quantile_pred")) {
+    return(x %>% as_tibble() %>% tidyr::nest(.by = .row) %>%
+      dplyr::select(data))
+  }
+  cli_abort(c(
+    "`nested_quantiles()` is deprecated. See {.fn hardhat::quantile_pred}."
+  ))
+}
+
+
 #' Pivot a column containing `quantile_pred` longer
 #'
 #' A column that contains `quantile_pred` will be "lengthened" with
diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 75c2da70c..134308da2 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -1,3 +1,40 @@
+#' A distribution parameterized by a set of quantiles
+#'
+#' `r lifecycle::badge("deprecated")`
+#'
+#' This function is deprecated. The recommended alternative is
+#' [hardhat::quantile_pred()].
+#'
+#' @param values A vector (or list of vectors) of values.
+#' @param quantile_levels A vector (or list of vectors) of probabilities
+#'   corresponding to `values`.
+#'
+#' When creating multiple sets of `values`/`quantile_levels` resulting in
+#' different distributions, the sizes must match. See the examples below.
+#'
+#' @return A vector of class `"distribution"`.
+#'
+#' @export
+#' @keywords internal
+#'
+#' @importFrom vctrs as_list_of vec_recycle_common new_vctr
+dist_quantiles <- function(values, quantile_levels) {
+  lifecycle::deprecate_warn("0.1.11", "dist_quantiles()", "hardhat::quantile_pred()")
+  if (is.list(values)) {
+    n <- length(values)
+    values <- unlist(values)
+    return(quantile_pred(matrix(values, nrow = n, byrow = TRUE), quantile_levels))
+  } else if (is.matrix(values)) {
+    return(quantile_pred(values, quantile_levels))
+  } else if (is.vector(values)) {
+    return(quantile_pred(matrix(values, nrow = 1), quantile_levels))
+  }
+  cli_abort(c(
+    "`dist_quantiles()` is deprecated and the format of `values` could not",
+    `!` = "be automatically converted to work with the replacement.",
+    i = "See {.fn hardhat::quantile_pred}."
+  ))
+}
 
 # placeholder to avoid errors, but not ideal
 #' @importFrom hardhat quantile_pred
@@ -6,6 +43,74 @@ mean.quantile_pred <- function(x, na.rm = FALSE, ...) {
   median(x, ...)
 }
 
+# These next 3 functions should probably be added via PR to {hardhat}
+# Only the third is actually *needed* at the moment.
+# The second doesn't work correctly (not sure why), but leaving here for the
+# future.
+#
+# We only export the third.
+#
+# self-self method, should work only if attr(quantile_levels) are compatible
+# #' @importFrom vctrs vec_ptype2 vec_cast
+# #' @importFrom hardhat extract_quantile_levels
+# #' @export
+# #' @keywords internal
+# vec_ptype2.quantile_pred.quantile_pred <- function(
+#     x, y, ..., x_arg = "", y_arg = "", call = caller_env()
+# ) {
+#   if (all(extract_quantile_levels(y) %in% extract_quantile_levels(x))) {
+#     return(x)
+#   }
+#   if (all(extract_quantile_levels(x) %in% extract_quantile_levels(y))) {
+#     return(y)
+#   }
+#   vctrs::stop_incompatible_type(
+#     x, y, x_arg = x_arg, y_arg = y_arg,
+#     details = "`quantile_levels` must be compatible (a superset/subset relation)."
+#   )
+# }
+
+# currently doesn't work
+# #' @export
+# vec_cast.quantile_pred.quantile_pred <- function(
+#     x, to, ..., x_arg = caller_arg(x), to_arg = caller_arg(to),
+#     call = caller_env()
+# ) {
+#   to_ql <- extract_quantile_levels(to)
+#   x_ql <- extract_quantile_levels(x)
+#   x_in_to <- x_ql %in% to_ql
+#   to_in_x <- to_ql %in% x_ql
+#   if (all(x_in_to)) {
+#     mat <- matrix(NA, ncol = length(to_ql))
+#     mat[ , to_in_x] <- c(as.matrix(x))
+#   } else if (all(to_in_x)) {
+#     mat <- as.matrix(x)[ , x_in_to, drop = FALSE]
+#   } else {
+#     vctrs::stop_incompatible_type(
+#       x, to, x_arg = x_arg, y_arg = to_arg,
+#       details = "`quantile_levels` must be compatible (a superset/subset relation)."
+#     )
+#   }
+#   quantile_pred(mat, to_ql)
+# }
+
+
+# Convert the quantile_pred to a data frame (named with the .quantile_levels)
+# This powers vec_proxy_equal (and hence ==, !=, is.na, etc)
+# It also powers vec_proxy_compare, so, given matching colnames, these should
+# work out of the box.
+#
+#' @importFrom vctrs vec_proxy_equal
+#' @export
+vec_proxy_equal.quantile_pred <- function(x, ...) {
+  as_tibble(x) %>%
+    tidyr::pivot_wider(
+      names_from = .quantile_levels,
+      values_from = .pred_quantile
+    ) %>%
+    dplyr::select(-.row)
+}
+
 
 # quantiles by treating quantile_pred like a distribution -----------------
 
diff --git a/R/reexports-tidymodels.R b/R/reexports-tidymodels.R
new file mode 100644
index 000000000..00cd7e4fd
--- /dev/null
+++ b/R/reexports-tidymodels.R
@@ -0,0 +1,37 @@
+#' @importFrom generics fit
+#' @export
+generics::fit
+
+#' @importFrom generics forecast
+#' @export
+generics::forecast
+
+#' @importFrom recipes prep
+#' @export
+recipes::prep
+
+#' @importFrom recipes bake
+#' @export
+recipes::bake
+
+#' @importFrom recipes rand_id
+#' @export
+recipes::rand_id
+
+#' @importFrom tibble tibble as_tibble
+#' @export
+tibble::tibble
+
+#' @export
+tibble::as_tibble
+
+#' @importFrom generics tidy
+#' @export
+generics::tidy
+
+#' @importFrom hardhat quantile_pred extract_quantile_levels
+#' @export
+hardhat::quantile_pred
+
+#' @export
+hardhat::extract_quantile_levels
diff --git a/R/utils-misc.R b/R/utils-misc.R
index fec707913..bdb35a9eb 100644
--- a/R/utils-misc.R
+++ b/R/utils-misc.R
@@ -47,7 +47,7 @@ format_varnames <- function(x, empty = "*none*") {
   if (length(x) == 0L) {
     empty
   } else {
-    as.character(syms(x))
+    as.character(rlang::syms(x))
   }
 }
 
diff --git a/man/dist_quantiles.Rd b/man/dist_quantiles.Rd
new file mode 100644
index 000000000..1bfa437b0
--- /dev/null
+++ b/man/dist_quantiles.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/quantile_pred-methods.R
+\name{dist_quantiles}
+\alias{dist_quantiles}
+\title{A distribution parameterized by a set of quantiles}
+\usage{
+dist_quantiles(values, quantile_levels)
+}
+\arguments{
+\item{values}{A vector (or list of vectors) of values.}
+
+\item{quantile_levels}{A vector (or list of vectors) of probabilities
+corresponding to \code{values}.
+
+When creating multiple sets of \code{values}/\code{quantile_levels} resulting in
+different distributions, the sizes must match. See the examples below.}
+}
+\value{
+A vector of class \code{"distribution"}.
+}
+\description{
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
+}
+\details{
+This function is deprecated. The recommended alternative is
+\code{\link[hardhat:quantile_pred]{hardhat::quantile_pred()}}.
+}
+\keyword{internal}
diff --git a/man/extrapolate_quantiles.Rd b/man/extrapolate_quantiles.Rd
index b645b85fa..bd460dbe9 100644
--- a/man/extrapolate_quantiles.Rd
+++ b/man/extrapolate_quantiles.Rd
@@ -29,7 +29,7 @@ similar to \code{\link[stats:quantile]{stats::quantile()}}, then \code{quantile(
 appropriate.
 }
 \examples{
-dstn <- quantile_dstn(rbind(1:4, 8:11), c(.2, .4, .6, .8))
+dstn <- quantile_pred(rbind(1:4, 8:11), c(.2, .4, .6, .8))
 # extra quantiles are appended
-as.tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
+as_tibble(extrapolate_quantiles(dstn, probs = c(.25, 0.5, .75)))
 }
diff --git a/man/nested_quantiles.Rd b/man/nested_quantiles.Rd
new file mode 100644
index 000000000..4b666ee32
--- /dev/null
+++ b/man/nested_quantiles.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/pivot_quantiles.R
+\name{nested_quantiles}
+\alias{nested_quantiles}
+\title{Turn a vector of quantile distributions into a list-col}
+\usage{
+nested_quantiles(x)
+}
+\arguments{
+\item{x}{a \code{distribution} containing \code{dist_quantiles}}
+}
+\value{
+a list-col
+}
+\description{
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
+}
+\details{
+This function is deprecated. The recommended alternative is
+\code{\link[hardhat:quantile_pred]{hardhat::quantile_pred()}} with \code{\link[tibble:as_tibble]{tibble::as_tibble()}}
+}
+\examples{
+.pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+nested_quantiles(.pred_quantile)
+
+.pred_quantile \%>\%
+  as_tibble() \%>\%
+  tidyr::nest(.by = .row) \%>\%
+  dplyr::select(-.row)
+
+}
diff --git a/man/reexports.Rd b/man/reexports.Rd
index aedd9e46b..ab2cf17be 100644
--- a/man/reexports.Rd
+++ b/man/reexports.Rd
@@ -21,6 +21,10 @@
 \alias{pivot_longer}
 \alias{pivot_wider}
 \alias{unnest}
+\alias{as_tibble}
+\alias{tidy}
+\alias{quantile_pred}
+\alias{extract_quantile_levels}
 \title{Objects exported from other packages}
 \keyword{internal}
 \description{
@@ -36,10 +40,11 @@ below to see their documentation.
 
   \item{ggplot2}{\code{\link[ggplot2]{autoplot}}}
 
+  \item{hardhat}{\code{\link[hardhat:quantile_pred]{extract_quantile_levels}}, \code{\link[hardhat]{quantile_pred}}}
+
   \item{recipes}{\code{\link[recipes]{bake}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{rand_id}}}
 
   \item{tibble}{\code{\link[tibble]{as_tibble}}, \code{\link[tibble]{tibble}}}
 
   \item{tidyr}{\code{\link[tidyr]{pivot_longer}}, \code{\link[tidyr]{pivot_wider}}, \code{\link[tidyr]{unnest}}}
 }}
-
diff --git a/tests/testthat/_snaps/dist_quantiles.md b/tests/testthat/_snaps/dist_quantiles.md
deleted file mode 100644
index 1d626e089..000000000
--- a/tests/testthat/_snaps/dist_quantiles.md
+++ /dev/null
@@ -1,56 +0,0 @@
-# constructor returns reasonable quantiles
-
-    Code
-      new_quantiles(rnorm(5), c(-2, -1, 0, 1, 2))
-    Condition
-      Error in `new_quantiles()`:
-      ! `quantile_levels` must lie in [0, 1].
-
----
-
-    Code
-      new_quantiles(sort(rnorm(5)), sort(runif(2)))
-    Condition
-      Error in `new_quantiles()`:
-      ! length(values) == length(quantile_levels) is not TRUE
-
----
-
-    Code
-      new_quantiles(c(2, 1, 3, 4, 5), c(0.1, 0.1, 0.2, 0.5, 0.8))
-    Condition
-      Error in `new_quantiles()`:
-      ! !vctrs::vec_duplicate_any(quantile_levels) is not TRUE
-
----
-
-    Code
-      new_quantiles(c(2, 1, 3, 4, 5), c(0.1, 0.15, 0.2, 0.5, 0.8))
-    Condition
-      Error in `new_quantiles()`:
-      ! `values[order(quantile_levels)]` produces unsorted quantiles.
-
----
-
-    Code
-      new_quantiles(c(1, 2, 3), c(0.1, 0.2, 3))
-    Condition
-      Error in `new_quantiles()`:
-      ! `quantile_levels` must lie in [0, 1].
-
-# arithmetic works on quantiles
-
-    Code
-      sum(dstn)
-    Condition
-      Error in `mapply()`:
-      ! You can't perform arithmetic between two distributions like this.
-
----
-
-    Code
-      suppressWarnings(dstn + distributional::dist_normal())
-    Condition
-      Error:
-      ! non-numeric argument to binary operator
-
diff --git a/tests/testthat/_snaps/pivot_quantiles.md b/tests/testthat/_snaps/pivot_quantiles.md
index 13dd81916..ea027d50c 100644
--- a/tests/testthat/_snaps/pivot_quantiles.md
+++ b/tests/testthat/_snaps/pivot_quantiles.md
@@ -38,3 +38,21 @@
       Error in `pivot_quantiles_longer()`:
       ! Only one column can be pivotted. Can not pivot all of: `d1` and `d2`.
 
+# nested_quantiles is deprecated, but works where possible
+
+    Code
+      d <- dist_quantiles(list(1:4, 2:5), 1:4 / 5)
+    Condition
+      Warning:
+      `dist_quantiles()` was deprecated in epipredict 0.1.11.
+      i Please use `hardhat::quantile_pred()` instead.
+
+---
+
+    Code
+      o <- nested_quantiles(d)
+    Condition
+      Warning:
+      `nested_quantiles()` was deprecated in epipredict 0.1.11.
+      i Please use `hardhat::quantile_pred()` instead.
+
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 855cbb457..78f629171 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -2,30 +2,16 @@
 
     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(structure(list(values = c(0, 
-    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
-    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
-    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
-    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
-        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
-        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
-        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = 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("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    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, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
@@ -35,31 +21,18 @@
 
     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(structure(list(values = c(0.084583345, 
-    0.1073314, 0.1292864, 0.1393442, 0.149402, 0.171357, 0.194105055
-    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.048438145, 0.0711862, 0.0931412, 0.103199, 0.1132568, 
-        0.1352118, 0.157959855), quantile_levels = c(0.05, 0.1, 0.25, 
-        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.257363545, 
-    0.2801116, 0.3020666, 0.3121244, 0.3221822, 0.3441372, 0.366885255
-    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.367085245, 0.3898333, 0.4117883, 0.4218461, 
-        0.4319039, 0.4538589, 0.476606955), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.677223545, 0.6999716, 0.7219266, 0.7319844, 
-        0.7420422, 0.7639972, 0.786745255), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = 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("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-    "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(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, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -67,30 +40,16 @@
 
     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(structure(list(values = c(0, 
-    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
-    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
-    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
-    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
-        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
-        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
-        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = 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("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    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, 
     18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
@@ -100,30 +59,16 @@
 
     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(structure(list(values = c(0, 
-    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
-    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
-    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
-    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
-    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
-        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
-        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
-        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = 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("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    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, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
@@ -141,279 +86,160 @@
     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(structure(list(
-        values = c(0, 0, 0, 0.05519342, 0.082372705, 0.0936219, 0.1048711, 
-        0.1157573, 0.12317806, 0.1302723, 0.1353526, 0.1393442, 0.1433358, 
-        0.1484161, 0.15551034, 0.1629311, 0.1738173, 0.1850665, 0.196315695, 
-        0.22349498, 0.309768685, 0.3567520625, 0.439580229), quantile_levels = c(0.01, 
-        0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
-        0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
-        0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0.0335550493877939, 
+    4L, 5L, 1L, 2L, 3L, 4L, 5L), .pred_distn = structure(list(c(0, 
+    0, 0, 0.05519342, 0.082372705, 0.0936219, 0.1048711, 0.1157573, 
+    0.12317806, 0.1302723, 0.1353526, 0.1393442, 0.1433358, 0.1484161, 
+    0.15551034, 0.1629311, 0.1738173, 0.1850665, 0.196315695, 0.22349498, 
+    0.309768685, 0.3567520625, 0.439580229), c(0, 0, 0, 0, 0.0335550493877939, 
     0.0604073208819088, 0.0796881899581496, 0.0945180888333883, 0.107218788833388, 
     0.118830788833388, 0.129717088833388, 0.1393442, 0.148949488833388, 
     0.159110072060821, 0.171080110623306, 0.184009705322953, 0.19866346102411, 
     0.218798896615666, 0.250961850618106, 0.300471354816148, 0.368582781136862, 
-    0.43909595699107, 0.520101234797705), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0, 0.0310685196688967, 
+    0.43909595699107, 0.520101234797705), c(0, 0, 0, 0, 0, 0.0310685196688967, 
     0.0565901050435504, 0.0768417663716637, 0.0947104815343153, 0.110553706525765, 
     0.125192081534315, 0.1393442, 0.153133424194392, 0.167807181271713, 
     0.183769310145952, 0.202099979390294, 0.224139947221972, 0.252840918770688, 
     0.291417895572206, 0.341073550318203, 0.420604597710477, 0.494523225410904, 
-    0.573647294116801), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 
-    0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
-    0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0, 0.00623643594225938, 0.0360877950479505, 
-        0.0604332430739307, 0.0824028153516535, 0.102509343235732, 
-        0.121439405653606, 0.1393442, 0.15780837904264, 0.176333479766098, 
-        0.1971089199637, 0.219859545844459, 0.246500872561225, 0.279163385675357, 
-        0.320379296602716, 0.374497727839579, 0.458894379633346, 
-        0.535545067037845, 0.628776504364044), quantile_levels = c(0.01, 
-        0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 
-        0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
-        0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0, 0, 0.0192048017017668, 
-    0.0478501821296211, 0.0723167026720766, 0.0958385084225842, 0.11812331897399, 
-    0.1393442, 0.161074539705197, 0.184026763327133, 0.207844848454635, 
-    0.23407004803228, 0.265166265836908, 0.302137478236883, 0.346008752873429, 
-    0.403205598400084, 0.495260096430714, 0.574198142463125, 0.672941852619816
-    ), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 
-    0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
-    0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0, 0, 
-    0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343000000001, 
+    0.573647294116801), c(0, 0, 0, 0, 0, 0.00623643594225938, 0.0360877950479505, 
+    0.0604332430739307, 0.0824028153516535, 0.102509343235732, 0.121439405653606, 
+    0.1393442, 0.15780837904264, 0.176333479766098, 0.1971089199637, 
+    0.219859545844459, 0.246500872561225, 0.279163385675357, 0.320379296602716, 
+    0.374497727839579, 0.458894379633346, 0.535545067037845, 0.628776504364044
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+    0.0723167026720766, 0.0958385084225842, 0.11812331897399, 0.1393442, 
+    0.161074539705197, 0.184026763327133, 0.207844848454635, 0.23407004803228, 
+    0.265166265836908, 0.302137478236883, 0.346008752873429, 0.403205598400084, 
+    0.495260096430714, 0.574198142463125, 0.672941852619816), c(0, 
+    0, 0, 0, 0.016465765, 0.03549514, 0.05225675, 0.0644172, 0.0749343, 
     0.0847941, 0.0966258, 0.103199, 0.1097722, 0.1216039, 0.1314637, 
     0.1419808, 0.15414125, 0.17090286, 0.189932235, 0.22848398, 0.30542311, 
-    0.40216399, 0.512353658), quantile_levels = c(0.01, 0.025, 0.05, 
-    0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 
-    0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0, 0.00331296053340532, 0.0234804643776438, 
-        0.0414109089650896, 0.0579040140087902, 0.0738391473860739, 
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-        0.254338497855279, 0.307871753369934, 0.407530532639726, 
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-        0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 
-        0.99)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0, 0, 0, 0, 
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-    0.3669383199518, 0.476508917333523, 0.574293059865274, 0.69194511433946
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-    0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
-    0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0, 0, 
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-    0.41300665395314, 0.52870334697862, 0.634316186092986, 0.767614547228429
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-    0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
-    0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0, 0, 
-    0, 0, 0, 0, 0, 0, 0.0118725894981448, 0.0439446210512103, 0.0736366703227029, 
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-    0.452807955824158, 0.578141866759416, 0.690542571738594, 0.837295153768033
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-    0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
-    0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0, 0, 
-    0.0813658, 0.14899276, 0.1960782, 0.22542314, 0.2414296, 0.25890318, 
-    0.2747762, 0.2881148, 0.3027873, 0.3121244, 0.3214615, 0.336134, 
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-    0.542883, 0.682805397499999, 0.798878314999999), quantile_levels = c(0.01, 
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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("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0, 0.0706949, 
-    0.1267172, 0.1667331, 0.198582473624236, 0.225423180397104, 0.2494327, 
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-    19013, 19020, 19027, 18999, 19006, 19013, 19020, 19027, 18999, 
+    18992, 18992), class = "Date"), target_date = structure(c(18999, 
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     18999, 19006, 19013, 19020, 19027, 18999, 19006, 19013, 19020, 
-    19027), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df", 
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+    19020, 19027), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df", 
     "tbl", "data.frame"))
 
 ---
@@ -428,290 +254,167 @@
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     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), class = "Date"), target_date = structure(c(19006, 19013, 
-    19020, 19027, 19034, 19006, 19013, 19020, 19027, 19034, 19006, 
+    18992, 18992), class = "Date"), target_date = structure(c(19006, 
     19013, 19020, 19027, 19034, 19006, 19013, 19020, 19027, 19034, 
     19006, 19013, 19020, 19027, 19034, 19006, 19013, 19020, 19027, 
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+    19034, 19006, 19013, 19020, 19027, 19034, 19006, 19013, 19020, 
+    19027, 19034), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df", 
     "tbl", "data.frame"))
 
 ---
@@ -726,287 +429,180 @@
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-    0.528816855, 0.61252005, 0.6626973, 0.6816954, 0.697340875, 0.7085162, 
-    0.7152214, 0.7208091, 0.72745833, 0.7319844, 0.73651047, 0.7431597, 
-    0.7487474, 0.7554526, 0.766627925, 0.7822734, 0.8012715, 0.85144875, 
-    0.935151945, 1.0252810475, 1.12578085), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.276821846502455, 
-    0.354318476867519, 0.440270225449805, 0.533132934163242, 0.5900576, 
-    0.631102729748298, 0.660462274661497, 0.680831108876989, 0.696223359635746, 
-    0.7096337, 0.7219265, 0.7319844, 0.7431597, 0.7543351, 0.7677455, 
-    0.783391, 0.804046832839828, 0.833541896886769, 0.873735298798638, 
-    0.929106903073231, 1.02188617627186, 1.10971107833641, 1.18626816850867
-    ), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 
-    0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 
-    0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default", 
-    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.202265200637946, 
-    0.298325094034965, 0.380907645938709, 0.481339524857949, 0.543219696138311, 
-    0.589507953775938, 0.6258186, 0.654874580912809, 0.6783427, 0.6984583, 
-    0.715655544727447, 0.7319844, 0.7487473, 0.7666278, 0.785715489951649, 
-    0.8090941, 0.83815, 0.873623567291473, 0.920206978680437, 0.98231174201862, 
-    1.08425930872329, 1.16639411427812, 1.25926838507547), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.129193504425124, 
-    0.241744300793533, 0.331949483165032, 0.43649858695157, 0.504472062268773, 
-    0.556141464729147, 0.597172505336053, 0.631406591640416, 0.660898437441874, 
-    0.686684727470375, 0.709633972330423, 0.7319844, 0.753217699696647, 
-    0.77608746100351, 0.8012715950276, 0.830327492252422, 0.86464477397774, 
-    0.906319686121761, 0.956815387818928, 1.02495125855129, 1.13129413647201, 
-    1.21644533535035, 1.32424172966634), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.0667682979050189, 
-    0.189580042212397, 0.290485041721667, 0.402951609190092, 0.475328740486855, 
-    0.530590906520765, 0.575504908587586, 0.613421932920829, 0.647285177364573, 
-    0.678099283398734, 0.70593862799773, 0.7319844, 0.758701322488325, 
-    0.786639532920829, 0.816837200234752, 0.850627936753767, 0.888963924063491, 
-    0.933785069065791, 0.988913131611816, 1.06240172852619, 1.16959624730917, 
-    1.2662008825538, 1.38860505690239), quantile_levels = c(0.01, 
-    0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 
-    0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99
-    )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0, 0, 0.0419413650000001, 
-    0.09882005, 0.1230992, 0.14226962, 0.1600776, 0.1722416, 0.1800265, 
-    0.1880061, 0.1936501, 0.1975426, 0.2014351, 0.2070791, 0.2150587, 
-    0.2228436, 0.2350076, 0.25281558, 0.271986, 0.29626515, 0.353143835, 
-    0.4353357125, 0.545314878), quantile_levels = c(0.01, 0.025, 
-    0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 
-    0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0.0438463650372504, 0.0808594787511875, 
-        0.106995615813358, 0.127478232938079, 0.145480846633466, 
-        0.1610508, 0.17461199504795, 0.186668812203222, 0.1975426, 
-        0.208428571374764, 0.2204108, 0.233930283744537, 0.249894552784127, 
-        0.267362348440485, 0.288755575723157, 0.316120297580926, 
-        0.355450425419354, 0.443192503687136, 0.536871211931719, 
-        0.636344785545224), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0.00188932708477086, 0.0470905919531195, 
-        0.079226864399944, 0.105414109111591, 0.127225815559956, 
-        0.146699420891509, 0.164644114298843, 0.18142942603581, 0.1975426, 
-        0.213933119201142, 0.231001630488804, 0.24941229702312, 0.269578845560456, 
-        0.292362546530965, 0.319632071367214, 0.354433951358713, 
-        0.406915236639266, 0.506944745332152, 0.596044605353528, 
-        0.695533388807317), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0.0156342454546545, 0.0536811248488485, 
-        0.084228833507335, 0.110407751354614, 0.134410113872139, 
-        0.156669167575476, 0.177701902429674, 0.1975426, 0.217759024165492, 
-        0.238897316673167, 0.261484572608426, 0.286120039498095, 
-        0.313065324705997, 0.345395334882349, 0.386811116673167, 
-        0.44780805303823, 0.550781846423163, 0.644984940689833, 0.752937731654986
-        ), quantile_levels = c(0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 
-        0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 
-        0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0, 0, 0, 0, 0.0290260214229144, 0.0653218111708617, 
-        0.0966336637233373, 0.124670861123061, 0.149775978614687, 
-        0.174275935467055, 0.1975426, 0.221291415429954, 0.246723385601356, 
-        0.273144383515685, 0.30101566402084, 0.33204051788793, 0.369730347126771, 
-        0.416909038104281, 0.481925596660567, 0.58989871202142, 0.688635568252056, 
-        0.803906183401304), quantile_levels = c(0.01, 0.025, 0.05, 
-        0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 
-        0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", 
-    "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
-    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
+    ), class = c("quantile_pred", "vctrs_vctr", "list")), 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, 
-    18992), class = "Date"), target_date = structure(c(18997, 19002, 
-    19007, 19012, 19017, 18997, 19002, 19007, 19012, 19017, 18997, 
+    18992, 18992), class = "Date"), target_date = structure(c(18997, 
     19002, 19007, 19012, 19017, 18997, 19002, 19007, 19012, 19017, 
     18997, 19002, 19007, 19012, 19017, 18997, 19002, 19007, 19012, 
-    19017), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df", 
+    19017, 18997, 19002, 19007, 19012, 19017, 18997, 19002, 19007, 
+    19012, 19017), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df", 
     "tbl", "data.frame"))
 
 # arx_forecaster snapshots
@@ -1014,37 +610,23 @@
     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(
-        structure(list(values = c(0.171022956902288, 0.244945899624723, 
-        0.308032696431071, 0.353013358779435, 0.397994021127798, 
-        0.461080817934147, 0.535003760656582), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.46653503056773, 0.540457973290166, 0.603544770096514, 
-            0.648525432444877, 0.693506094793241, 0.756592891599589, 
-            0.830515834322024), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.485679887517181, 
+        c(0.171022956902288, 0.244945899624723, 0.308032696431071, 
+        0.353013358779435, 0.397994021127798, 0.461080817934147, 
+        0.535003760656582), c(0.46653503056773, 0.540457973290166, 
+        0.603544770096514, 0.648525432444877, 0.693506094793241, 
+        0.756592891599589, 0.830515834322024), c(0.485679887517181, 
         0.559602830239616, 0.622689627045964, 0.667670289394328, 
         0.712650951742692, 0.77573774854904, 0.849660691271475), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.959876988846753, 
-        1.03379993156919, 1.09688672837554, 1.1418673907239, 1.18684805307226, 
-        1.24993484987861, 1.32385779260105), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.64845829380644, 0.722381236528875, 0.785468033335223, 
-            0.830448695683587, 0.875429358031951, 0.938516154838299, 
-            1.01243909756073), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.147809030071502, 
-        0.221731972793937, 0.284818769600285, 0.329799431948649, 
-        0.374780094297013, 0.437866891103361, 0.511789833825796), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
-    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+        c(0.959876988846753, 1.03379993156919, 1.09688672837554, 
+        1.1418673907239, 1.18684805307226, 1.24993484987861, 1.32385779260105
+        ), c(0.64845829380644, 0.722381236528875, 0.785468033335223, 
+        0.830448695683587, 0.875429358031951, 0.938516154838299, 
+        1.01243909756073), c(0.147809030071502, 0.221731972793937, 
+        0.284818769600285, 0.329799431948649, 0.374780094297013, 
+        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, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
@@ -1053,35 +635,21 @@
     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(
-        structure(list(values = c(0.0961118191398634, 0.118312393281548, 
-        0.13840396557592, 0.149303403634373, 0.160202841692825, 0.180294413987198, 
-        0.202494988128882), quantile_levels = c(0.05, 0.1, 0.25, 
-        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.0865730800114383, 
-        0.108773654153123, 0.128865226447495, 0.139764664505948, 
-        0.1506641025644, 0.170755674858773, 0.192956249000457), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.279994736572136, 0.30219531071382, 0.322286883008193, 
-            0.333186321066645, 0.344085759125097, 0.36417733141947, 
-            0.386377905561154), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.417153993342634, 
+        c(0.0961118191398634, 0.118312393281548, 0.13840396557592, 
+        0.149303403634373, 0.160202841692825, 0.180294413987198, 
+        0.202494988128882), c(0.0865730800114383, 0.108773654153123, 
+        0.128865226447495, 0.139764664505948, 0.1506641025644, 0.170755674858773, 
+        0.192956249000457), c(0.279994736572136, 0.30219531071382, 
+        0.322286883008193, 0.333186321066645, 0.344085759125097, 
+        0.36417733141947, 0.386377905561154), c(0.417153993342634, 
         0.439354567484319, 0.459446139778691, 0.470345577837144, 
         0.481245015895596, 0.501336588189969, 0.523537162331653), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.672794520917498, 
-        0.694995095059183, 0.715086667353556, 0.725986105412008, 
-        0.73688554347046, 0.756977115764833, 0.779177689906517), 
-            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = 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("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+        c(0.672794520917498, 0.694995095059183, 0.715086667353556, 
+        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, 
     18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
@@ -1092,34 +660,20 @@
     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(
-        structure(list(values = c(0.136509784083987, 0.202348949370703, 
-        0.263837900408968, 0.303244704017742, 0.342651507626517, 
-        0.404140458664782, 0.469979623951498), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.430437098664042, 0.491926049702307, 
-            0.531332853311081, 0.570739656919856, 0.632228607958121, 
-            0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        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), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282475, 0.887794494569191, 0.949283445607456, 
-            0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
+        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), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.205999702578282, 0.267488653616547, 
-            0.306895457225321, 0.346302260834096, 0.407791211872361, 
-            0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+        0.895697751648679, 0.961536916935395), c(0.140160537291566, 
+        0.205999702578282, 0.267488653616547, 0.306895457225321, 
+        0.346302260834096, 0.407791211872361, 0.473630377159077)), 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, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
@@ -1130,34 +684,20 @@
     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(
-        structure(list(values = c(0.136509784083987, 0.202348949370703, 
-        0.263837900408968, 0.303244704017742, 0.342651507626517, 
-        0.404140458664782, 0.469979623951498), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.430437098664042, 0.491926049702307, 
-            0.531332853311081, 0.570739656919856, 0.632228607958121, 
-            0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        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), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282475, 0.887794494569191, 0.949283445607456, 
-            0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
+        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), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291566, 0.205999702578282, 0.267488653616547, 
-            0.306895457225321, 0.346302260834096, 0.407791211872361, 
-            0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25, 
-            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+        0.895697751648679, 0.961536916935395), c(0.140160537291566, 
+        0.205999702578282, 0.267488653616547, 0.306895457225321, 
+        0.346302260834096, 0.407791211872361, 0.473630377159077)), 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, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
diff --git a/tests/testthat/_snaps/wis-dist-quantiles.md b/tests/testthat/_snaps/wis-dist-quantiles.md
deleted file mode 100644
index fb9cfbdf6..000000000
--- a/tests/testthat/_snaps/wis-dist-quantiles.md
+++ /dev/null
@@ -1,17 +0,0 @@
-# wis dispatches and produces the correct values
-
-    Code
-      weighted_interval_score(1:10, 10)
-    Condition
-      Error in `weighted_interval_score()`:
-      ! Weighted interval score can only be calculated if `x`
-      has class <distribution>.
-
----
-
-    Code
-      weighted_interval_score(dist_quantiles(list(1:4, 8:11), 1:4 / 5), 1:3)
-    Condition
-      Error in `weighted_interval_score()`:
-      ! Can't recycle `x` (size 2) to match `actual` (size 3).
-
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index 5adbf6518..32f581d7a 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -78,7 +78,7 @@ test_that("quantile_rand_forest operates with arx_forecaster", {
 
   z <- arx_forecaster(df, "cases", "cases", spec2)
   expect_identical(
-    nested_quantiles(z$predictions$.pred_distn[1])[[1]]$quantile_levels,
+    hardhat::extract_quantile_levels(z$predictions$.pred_distn),
     c(.05, .1, 0.25, .5, 0.75, .9, .95)
   )
 })
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index a4362cffb..af6f82727 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -42,3 +42,12 @@ test_that("quantile pivotting longer behaves", {
   expect_identical(pivot_quantiles_longer(tib, d1)$d1_value, c(1:3, 2:4))
 
 })
+
+test_that("nested_quantiles is deprecated, but works where possible", {
+  expect_snapshot(d <- dist_quantiles(list(1:4, 2:5), 1:4 / 5))
+  expect_snapshot(o <- nested_quantiles(d))
+  res <- as_tibble(hardhat::quantile_pred(
+    matrix(c(1:4, 2:5), nrow = 2, byrow = TRUE), 1:4 / 5)
+  )
+  expect_identical(o |> mutate(.row = dplyr::row_number()) |> unnest(data), res)
+})
diff --git a/tests/testthat/test-population_scaling.R b/tests/testthat/test-population_scaling.R
index 6e4cd5df2..f2efde3c0 100644
--- a/tests/testthat/test-population_scaling.R
+++ b/tests/testthat/test-population_scaling.R
@@ -340,7 +340,6 @@ test_that("test joining by default columns with less common keys/classes", {
       pivot_quantiles_wider(.pred),
     dat1 %>%
       select(!"y") %>%
-      as_tibble() %>%
       mutate(`0.5` = c(2 * 5, 2 * 11))
   )
 
@@ -377,7 +376,6 @@ test_that("test joining by default columns with less common keys/classes", {
           pivot_quantiles_wider(.pred),
         dat1b %>%
           select(!"y") %>%
-          as_tibble() %>%
           # geo 1 scaling used for both:
           mutate(`0.5` = c(2 * 5, 2 * 5))
       ),
@@ -419,10 +417,10 @@ test_that("test joining by default columns with less common keys/classes", {
       expect_equal(
         # get_test_data doesn't work with non-`epi_df`s, so provide test data manually:
         predict(fit(ewf1b2, dat1b2), dat1b2) %>%
-          pivot_quantiles_wider(.pred),
+          pivot_quantiles_wider(.pred) %>%
+          as_tibble(),
         dat1b2 %>%
           select(!"y") %>%
-          as_tibble() %>%
           # geo 1 scaling used for both:
           mutate(`0.5` = c(2 * 5, 2 * 5)) %>%
           select(geo_value, age_group, time_value, `0.5`)
@@ -485,7 +483,8 @@ test_that("test joining by default columns with less common keys/classes", {
   )
   expect_equal(
     forecast(fit(ewf2, dat2)) %>%
-      pivot_quantiles_wider(.pred),
+      pivot_quantiles_wider(.pred) %>%
+      as_tibble(),
     dat2 %>%
       select(!"y") %>%
       as_tibble() %>%
@@ -522,7 +521,6 @@ test_that("test joining by default columns with less common keys/classes", {
           pivot_quantiles_wider(.pred),
         dat2b %>%
           select(!"y") %>%
-          as_tibble() %>%
           mutate(`0.5` = c(2 * 5, 2 * 11))
       ),
       class = "epipredict__step_population_scaling__default_by_missing_suggested_keys"
@@ -559,8 +557,7 @@ test_that("test joining by default columns with less common keys/classes", {
     # slightly edited copy-pasta due to test time selection:
     dat3 %>%
       select(!"y") %>%
-      as_tibble() %>%
-      slice_max(by = geo_value, time_value) %>%
+      dplyr::slice_max(by = geo_value, time_value) %>%
       mutate(`0.5` = 2 * 11)
   )
 
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 1e9127bbe..9a6910533 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -13,7 +13,6 @@ x <- tibble(
 ) %>%
   as_epi_df(as_of = as.POSIXct("2024-09-17"))
 max_time <- max(x$time_value)
-class(attributes(x)$metadata$as_of)
 as_of <- attributes(x)$metadata$as_of
 ahead <- 7
 latency <- 5
@@ -241,7 +240,12 @@ test_that("epi_adjust_latency extends multiple aheads", {
   # the as_of on x is today's date, which is >970 days in the future
   # also, there's no data >970 days in the past, so it gets an error trying to
   # fit on no data
-  expect_error(expect_warning(fit3 <- fit(epi_wf, data = x), class = "epipredict__prep.step_latency__very_large_latency"), class = "simpleError")
+  expect_error(
+    expect_warning(
+      fit3 <- fit(epi_wf, data = x),
+      class = "epipredict__prep.step_latency__very_large_latency"),
+    class = "simpleError"
+  )
   # real date example
   fit3 <- fit(epi_wf, data = real_x)
   expect_equal(

From b1d1630432ce11894fa0eb97fbfaec7a1fc4b744 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 25 Feb 2025 12:00:47 -0800
Subject: [PATCH 08/22] pass local checks

---
 NAMESPACE                      |  1 -
 R/make_smooth_quantile_reg.R   | 10 ++++------
 R/weighted_interval_score.R    |  6 +++---
 man/smooth_quantile_reg.Rd     | 10 ++++------
 man/weighted_interval_score.Rd |  6 +++---
 5 files changed, 14 insertions(+), 19 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 4c43e2f45..4bb3d6fe1 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -202,7 +202,6 @@ export(rename)
 export(select)
 export(slather)
 export(smooth_quantile_reg)
-export(snap)
 export(step_adjust_latency)
 export(step_climate)
 export(step_epi_ahead)
diff --git a/R/make_smooth_quantile_reg.R b/R/make_smooth_quantile_reg.R
index 1ca019f75..03d4e19af 100644
--- a/R/make_smooth_quantile_reg.R
+++ b/R/make_smooth_quantile_reg.R
@@ -41,13 +41,11 @@
 #' )
 #' pl <- pl %>%
 #'   unnest(.pred) %>%
-#'   mutate(distn = nested_quantiles(distn)) %>%
-#'   unnest(distn) %>%
+#'   pivot_quantiles_wider(distn) %>%
 #'   mutate(
 #'     x = x[length(x) - 20] + ahead / 100 * 2 * pi,
 #'     ahead = NULL
-#'   ) %>%
-#'   pivot_wider(names_from = distn_quantile_levels, values_from = distn_value)
+#'   )
 #' plot(x, y, pch = 16, xlim = c(pi, 2 * pi), col = "lightgrey")
 #' curve(sin(x), add = TRUE)
 #' abline(v = fd, lty = 2)
@@ -57,11 +55,11 @@
 #'
 #' library(ggplot2)
 #' ggplot(data.frame(x = x, y = y), aes(x)) +
-#'   geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "lightblue") +
+#'   geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "cornflowerblue") +
 #'   geom_point(aes(y = y), colour = "grey") + # observed data
 #'   geom_function(fun = sin, colour = "black") + # truth
 #'   geom_vline(xintercept = fd, linetype = "dashed") + # end of training data
-#'   geom_line(data = pl, aes(y = `0.5`), colour = "red") + # median prediction
+#'   geom_line(data = pl, aes(y = `0.5`), colour = "orange") + # median prediction
 #'   theme_bw() +
 #'   coord_cartesian(xlim = c(0, NA)) +
 #'   ylab("y")
diff --git a/R/weighted_interval_score.R b/R/weighted_interval_score.R
index aa0c816a3..47187fe56 100644
--- a/R/weighted_interval_score.R
+++ b/R/weighted_interval_score.R
@@ -52,13 +52,13 @@
 #'
 #' # Using some actual forecasts --------
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' training <- covid_case_death_rates %>%
 #'   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 #' preds <- flatline_forecaster(
-#'   jhu, "death_rate",
+#'   training, "death_rate",
 #'   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 #' )$predictions
-#' actuals <- case_death_rate_subset %>%
+#' actuals <- covid_case_death_rates %>%
 #'   filter(time_value == as.Date("2021-12-01") + 7) %>%
 #'   select(geo_value, time_value, actual = death_rate)
 #' preds <- left_join(preds, actuals,
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index 973a85879..5d401c795 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -60,13 +60,11 @@ pl <- predict(
 )
 pl <- pl \%>\%
   unnest(.pred) \%>\%
-  mutate(distn = nested_quantiles(distn)) \%>\%
-  unnest(distn) \%>\%
+  pivot_quantiles_wider(distn) \%>\%
   mutate(
     x = x[length(x) - 20] + ahead / 100 * 2 * pi,
     ahead = NULL
-  ) \%>\%
-  pivot_wider(names_from = distn_quantile_levels, values_from = distn_value)
+  )
 plot(x, y, pch = 16, xlim = c(pi, 2 * pi), col = "lightgrey")
 curve(sin(x), add = TRUE)
 abline(v = fd, lty = 2)
@@ -76,11 +74,11 @@ lines(pl$x, pl$`0.5`, col = "red")
 
 library(ggplot2)
 ggplot(data.frame(x = x, y = y), aes(x)) +
-  geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "lightblue") +
+  geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "cornflowerblue") +
   geom_point(aes(y = y), colour = "grey") + # observed data
   geom_function(fun = sin, colour = "black") + # truth
   geom_vline(xintercept = fd, linetype = "dashed") + # end of training data
-  geom_line(data = pl, aes(y = `0.5`), colour = "red") + # median prediction
+  geom_line(data = pl, aes(y = `0.5`), colour = "orange") + # median prediction
   theme_bw() +
   coord_cartesian(xlim = c(0, NA)) +
   ylab("y")
diff --git a/man/weighted_interval_score.Rd b/man/weighted_interval_score.Rd
index 22a616b70..ef09d4da1 100644
--- a/man/weighted_interval_score.Rd
+++ b/man/weighted_interval_score.Rd
@@ -69,13 +69,13 @@ weighted_interval_score(
 
 # Using some actual forecasts --------
 library(dplyr)
-jhu <- case_death_rate_subset \%>\%
+training <- covid_case_death_rates \%>\%
   filter(time_value >= "2021-10-01", time_value <= "2021-12-01")
 preds <- flatline_forecaster(
-  jhu, "death_rate",
+  training, "death_rate",
   flatline_args_list(quantile_levels = c(.01, .025, 1:19 / 20, .975, .99))
 )$predictions
-actuals <- case_death_rate_subset \%>\%
+actuals <- covid_case_death_rates \%>\%
   filter(time_value == as.Date("2021-12-01") + 7) \%>\%
   select(geo_value, time_value, actual = death_rate)
 preds <- left_join(preds, actuals,

From e049766b6c58f8d7c751febd2baa9cb7c44444a7 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 25 Feb 2025 12:05:03 -0800
Subject: [PATCH 09/22] bump news

---
 NEWS.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 9c30f2cf7..aaa0af2c0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -17,6 +17,9 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
   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 
+  with `hardhat::quantile_pred()`. Some associated functions are deprecated with
+  `lifecycle` messages.
 
 ## Improvements
 
@@ -28,6 +31,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Add `reference_date` as an argument to `epi_recipe()`
 - Add `step_climate()` to create "climate" predictor in forecast workflows
 - Add `climatological_forecaster()` to automatically create climate baselines
+- Replace `dist_quantiles()` with `hardhat::quantile_pred()`
 
 ## Bug fixes
 

From 893a52fbcd4223d5332fd5200a629c30116ee8f4 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 25 Feb 2025 12:21:02 -0800
Subject: [PATCH 10/22] style: fu

---
 R/layer_residual_quantiles.R              |  5 +++--
 R/quantile_pred-methods.R                 | 25 +++++++++++++----------
 tests/testthat/test-pivot_quantiles.R     |  5 ++---
 tests/testthat/test-quantile_pred.R       |  1 -
 tests/testthat/test-step_adjust_latency.R |  3 ++-
 tests/testthat/test-wis-quantile_pred.R   |  3 ++-
 6 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index 0517a0ffd..b82d6ab91 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -126,10 +126,11 @@ slather.layer_residual_quantiles <-
 
     r <- r %>%
       summarize(dstn = quantile_pred(matrix(quantile(
-        c(.resid, s * .resid), probs = object$quantile_levels, na.rm = TRUE
+        c(.resid, s * .resid),
+        probs = object$quantile_levels, na.rm = TRUE
       ), nrow = 1), quantile_levels = object$quantile_levels))
     # Check for NA
-    if (anyNA(as.matrix(r$dstn)))  {
+    if (anyNA(as.matrix(r$dstn))) {
       cli_abort(c(
         "Residual quantiles could not be calculated due to missing residuals.",
         i = "This may be due to `n_train` < `ahead` in your {.cls epi_recipe}."
diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 134308da2..6694a0146 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -133,7 +133,7 @@ quantile_internal <- function(x, tau_out, middle) {
   # short circuit if we aren't actually extrapolating
   # matches to ~15 decimals
   if (all(tau_out %in% tau) && !anyNA(qvals)) {
-    return(qvals[ , match(tau_out, tau), drop = FALSE])
+    return(qvals[, match(tau_out, tau), drop = FALSE])
   }
   if (length(tau) < 2) {
     cli_abort(paste(
@@ -152,7 +152,9 @@ quantile_internal <- function(x, tau_out, middle) {
 extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
   qvals_out <- rep(NA, length(tau_out))
   good <- !is.na(qvals)
-  if (!any(good)) return(qvals_out)
+  if (!any(good)) {
+    return(qvals_out)
+  }
   qvals <- qvals[good]
   tau <- tau[good]
 
@@ -169,21 +171,22 @@ extrapolate_quantiles_single <- function(qvals, tau, tau_out, middle) {
 
   if (middle == "cubic") {
     method <- "cubic"
-    result <- tryCatch({
-      Q <- stats::splinefun(tau, qvals, method = "hyman")
-      quartiles <- Q(c(.25, .5, .75))
-    },
-    error = function(e) {
-      return(NA)
-    })
+    result <- tryCatch(
+      {
+        Q <- stats::splinefun(tau, qvals, method = "hyman")
+        quartiles <- Q(c(.25, .5, .75))
+      },
+      error = function(e) {
+        return(NA)
+      }
+    )
   }
   if (middle == "linear" || any(is.na(result))) {
     method <- "linear"
     quartiles <- stats::approx(tau, qvals, c(.25, .5, .75))$y
   }
   if (any(indm)) {
-    qvals_out[indm] <- switch(
-      method,
+    qvals_out[indm] <- switch(method,
       linear = stats::approx(tau, qvals, tau_out[indm])$y,
       cubic = Q(tau_out[indm])
     )
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index af6f82727..39db31b2f 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -40,14 +40,13 @@ 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))
-
 })
 
 test_that("nested_quantiles is deprecated, but works where possible", {
   expect_snapshot(d <- dist_quantiles(list(1:4, 2:5), 1:4 / 5))
   expect_snapshot(o <- nested_quantiles(d))
   res <- as_tibble(hardhat::quantile_pred(
-    matrix(c(1:4, 2:5), nrow = 2, byrow = TRUE), 1:4 / 5)
-  )
+    matrix(c(1:4, 2:5), nrow = 2, byrow = TRUE), 1:4 / 5
+  ))
   expect_identical(o |> mutate(.row = dplyr::row_number()) |> unnest(data), res)
 })
diff --git a/tests/testthat/test-quantile_pred.R b/tests/testthat/test-quantile_pred.R
index 90710c6e1..70d7c71a5 100644
--- a/tests/testthat/test-quantile_pred.R
+++ b/tests/testthat/test-quantile_pred.R
@@ -78,7 +78,6 @@ test_that("unary math works on quantiles", {
     1:4 / 5
   )
   expect_identical(log(dstn), dstn2)
-
 })
 
 test_that("arithmetic works on quantiles", {
diff --git a/tests/testthat/test-step_adjust_latency.R b/tests/testthat/test-step_adjust_latency.R
index 9a6910533..80e31dc17 100644
--- a/tests/testthat/test-step_adjust_latency.R
+++ b/tests/testthat/test-step_adjust_latency.R
@@ -243,7 +243,8 @@ test_that("epi_adjust_latency extends multiple aheads", {
   expect_error(
     expect_warning(
       fit3 <- fit(epi_wf, data = x),
-      class = "epipredict__prep.step_latency__very_large_latency"),
+      class = "epipredict__prep.step_latency__very_large_latency"
+    ),
     class = "simpleError"
   )
   # real date example
diff --git a/tests/testthat/test-wis-quantile_pred.R b/tests/testthat/test-wis-quantile_pred.R
index 187344af9..a51a67352 100644
--- a/tests/testthat/test-wis-quantile_pred.R
+++ b/tests/testthat/test-wis-quantile_pred.R
@@ -51,7 +51,8 @@ test_that("wis dispatches and produces the correct values", {
   ))
   expect_true(is.na(
     weighted_interval_score(
-      quantile_pred(rbind(1:4), 1:4 / 5), 2.5, 1:9 / 10, na_handling = "fail"
+      quantile_pred(rbind(1:4), 1:4 / 5), 2.5, 1:9 / 10,
+      na_handling = "fail"
     )
   ))
 })

From e8d4e57dd019fe9d9d28495ad63ae6a242959034 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Tue, 25 Feb 2025 18:49:35 -0800
Subject: [PATCH 11/22] bump hh version to release

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 01e07520d..af8e94595 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -35,7 +35,7 @@ Imports:
     generics,
     ggplot2,
     glue,
-    hardhat (>= 1.4.0.9002),
+    hardhat (>= 1.4.1),
     lifecycle,
     lubridate,
     magrittr,

From 3895f1355923e487f4caaedfa4f71923cd45b756 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 13:44:35 -0800
Subject: [PATCH 12/22] allow for quantile() to threshold to an interval

---
 R/quantile_pred-methods.R     | 54 ++++++++++++++++++++++++++---
 man/quantile.quantile_pred.Rd | 64 +++++++++++++++++++++++++++++++++++
 2 files changed, 113 insertions(+), 5 deletions(-)
 create mode 100644 man/quantile.quantile_pred.Rd

diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 6694a0146..7548b58eb 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -115,14 +115,58 @@ vec_proxy_equal.quantile_pred <- function(x, ...) {
 # quantiles by treating quantile_pred like a distribution -----------------
 
 
+#' Quantiles from a distribution
+#'
+#' Given a [hardhat::quantile_pred] object, users may wish to compute additional
+#' `quantile_levels` that are not part of the object. This function attempts
+#' to estimate these quantities under some assumptions. Interior probabilities,
+#' those contained within, existing probabilities are interpolated in a manner
+#' controled by the `middle` argument. Those outside existing probabilities
+#' are extrapolated under the assumption that the tails of the distribution
+#' decays exponentially. Optionally, one may constrain _all_ quantiles to be
+#' within some support (say, `[0, Inf)`).
+#'
+#' @inheritParams stats::quantile
+#' @param ... unused
+#' @param lower Scalar. Optional lower bound.
+#' @param upper Scalar. Optional upper bound.
+#' @param middle Controls how extrapolation to "interior" probabilities is
+#'   performed. "cubic" attempts to use [stats::splinefun()] while "linear"
+#'   uses [stats::approx()]. The "linear" method is used as a fallback if
+#'   "cubic" should fail for some reason.
+#'
+#' @returns a matrix with one row for each entry in `x` and one column for each
+#'   value in `probs`. If either has length 1, a vector.
+#' @seealso [extrapolate_quantiles()]
 #' @export
 #' @importFrom stats quantile
-quantile.quantile_pred <- function(x, p, na.rm = FALSE, ...,
-                                   middle = c("cubic", "linear")) {
-  arg_is_probabilities(p)
-  p <- sort(p)
+#'
+#' @examples
+#' qp <- quantile_pred(matrix(1:8, nrow = 2, byrow = TRUE), 1:4 / 5)
+#' quantile(qp)
+#' quantile(qp, lower = 0)
+#' quantile(qp, probs = 0.5)
+#' quantile(qp, probs = 1:9 / 10)
+quantile.quantile_pred <- function(x,
+                                   probs = seq(0, 1, 0.25),
+                                   na.rm = FALSE,
+                                   lower = -Inf,
+                                   upper = Inf,
+                                   middle = c("cubic", "linear"),
+                                   ...
+) {
+  arg_is_probabilities(probs)
+  arg_is_scalar(lower, upper, na.rm)
+  arg_is_numeric(lower, upper)
+  arg_is_lgl(na.rm)
+
+  if (lower > upper) {
+    cli_abort("`lower` must be less than `upper`.")
+  }
+
+  if (is.unsorted(probs)) probs <- sort(probs)
   middle <- rlang::arg_match(middle)
-  quantile_internal(x, p, middle)
+  drop(snap(quantile_internal(x, probs, middle), lower, upper))
 }
 
 
diff --git a/man/quantile.quantile_pred.Rd b/man/quantile.quantile_pred.Rd
new file mode 100644
index 000000000..c83e629b2
--- /dev/null
+++ b/man/quantile.quantile_pred.Rd
@@ -0,0 +1,64 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/quantile_pred-methods.R
+\name{quantile.quantile_pred}
+\alias{quantile.quantile_pred}
+\title{Quantiles from a distribution}
+\usage{
+\method{quantile}{quantile_pred}(
+  x,
+  probs = seq(0, 1, 0.25),
+  na.rm = FALSE,
+  lower = -Inf,
+  upper = Inf,
+  middle = c("cubic", "linear"),
+  ...
+)
+}
+\arguments{
+\item{x}{numeric vector whose sample quantiles are wanted, or an
+    object of a class for which a method has been defined (see also
+    \sQuote{details}). \code{\link{NA}} and \code{NaN} values are not
+    allowed in numeric vectors unless \code{na.rm} is \code{TRUE}.}
+
+\item{probs}{numeric vector of probabilities with values in
+    \eqn{[0,1]}.  (Values up to \samp{2e-14} outside that
+    range are accepted and moved to the nearby endpoint.)}
+
+\item{na.rm}{logical; if true, any \code{\link{NA}} and \code{NaN}'s
+    are removed from \code{x} before the quantiles are computed.}
+
+\item{lower}{Scalar. Optional lower bound.}
+
+\item{upper}{Scalar. Optional upper bound.}
+
+\item{middle}{Controls how extrapolation to "interior" probabilities is
+performed. "cubic" attempts to use \code{\link[stats:splinefun]{stats::splinefun()}} while "linear"
+uses \code{\link[stats:approxfun]{stats::approx()}}. The "linear" method is used as a fallback if
+"cubic" should fail for some reason.}
+
+\item{...}{unused}
+}
+\value{
+a matrix with one row for each entry in \code{x} and one column for each
+value in \code{probs}. If either has length 1, a vector.
+}
+\description{
+Given a \link[hardhat:quantile_pred]{hardhat::quantile_pred} object, users may wish to compute additional
+\code{quantile_levels} that are not part of the object. This function attempts
+to estimate these quantities under some assumptions. Interior probabilities,
+those contained within, existing probabilities are interpolated in a manner
+controled by the \code{middle} argument. Those outside existing probabilities
+are extrapolated under the assumption that the tails of the distribution
+decays exponentially. Optionally, one may constrain \emph{all} quantiles to be
+within some support (say, \verb{[0, Inf)}).
+}
+\examples{
+qp <- quantile_pred(matrix(1:8, nrow = 2, byrow = TRUE), 1:4 / 5)
+quantile(qp)
+quantile(qp, lower = 0)
+quantile(qp, probs = 0.5)
+quantile(qp, probs = 1:9 / 10)
+}
+\seealso{
+\code{\link[=extrapolate_quantiles]{extrapolate_quantiles()}}
+}

From fb1669967c802cd3d9b69cdb73aafd36bce58cbb Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 14:00:25 -0800
Subject: [PATCH 13/22] closes #434

---
 NEWS.md                       | 1 +
 R/quantile_pred-methods.R     | 4 ++--
 man/quantile.quantile_pred.Rd | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index aaa0af2c0..13a9d93b3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -32,6 +32,7 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Add `step_climate()` to create "climate" predictor in forecast workflows
 - Add `climatological_forecaster()` to automatically create climate baselines
 - Replace `dist_quantiles()` with `hardhat::quantile_pred()`
+- Allow `quantile()` to threshold to an interval if desired (#434)
 
 ## Bug fixes
 
diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 7548b58eb..16b30f941 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -136,7 +136,7 @@ vec_proxy_equal.quantile_pred <- function(x, ...) {
 #'   "cubic" should fail for some reason.
 #'
 #' @returns a matrix with one row for each entry in `x` and one column for each
-#'   value in `probs`. If either has length 1, a vector.
+#'   value in `probs`
 #' @seealso [extrapolate_quantiles()]
 #' @export
 #' @importFrom stats quantile
@@ -166,7 +166,7 @@ quantile.quantile_pred <- function(x,
 
   if (is.unsorted(probs)) probs <- sort(probs)
   middle <- rlang::arg_match(middle)
-  drop(snap(quantile_internal(x, probs, middle), lower, upper))
+  snap(quantile_internal(x, probs, middle), lower, upper)
 }
 
 
diff --git a/man/quantile.quantile_pred.Rd b/man/quantile.quantile_pred.Rd
index c83e629b2..246a3e987 100644
--- a/man/quantile.quantile_pred.Rd
+++ b/man/quantile.quantile_pred.Rd
@@ -40,7 +40,7 @@ uses \code{\link[stats:approxfun]{stats::approx()}}. The "linear" method is used
 }
 \value{
 a matrix with one row for each entry in \code{x} and one column for each
-value in \code{probs}. If either has length 1, a vector.
+value in \code{probs}
 }
 \description{
 Given a \link[hardhat:quantile_pred]{hardhat::quantile_pred} object, users may wish to compute additional

From 297a65dcb887033bd4bf0cfb9ebfdefde0a32f97 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 14:05:20 -0800
Subject: [PATCH 14/22] excise lingering distributional internals

---
 DESCRIPTION                   |  1 -
 NAMESPACE                     |  2 -
 R/layer_predictive_distn.R    | 93 ++---------------------------------
 R/layer_quantile_distn.R      | 12 +----
 R/pivot_quantiles.R           | 32 +++---------
 man/layer_predictive_distn.Rd | 24 ++-------
 man/nested_quantiles.Rd       |  6 +--
 7 files changed, 17 insertions(+), 153 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index af8e94595..1ce1f8ef1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -50,7 +50,6 @@ Imports:
     workflows (>= 1.0.0)
 Suggests:
     data.table,
-    distributional,
     epidatr (>= 1.0.0),
     fs,
     grf,
diff --git a/NAMESPACE b/NAMESPACE
index 4bb3d6fe1..3c59dcb06 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -78,7 +78,6 @@ S3method(print,layer_naomit)
 S3method(print,layer_point_from_distn)
 S3method(print,layer_population_scaling)
 S3method(print,layer_predict)
-S3method(print,layer_predictive_distn)
 S3method(print,layer_quantile_distn)
 S3method(print,layer_residual_quantiles)
 S3method(print,layer_threshold)
@@ -104,7 +103,6 @@ S3method(slather,layer_naomit)
 S3method(slather,layer_point_from_distn)
 S3method(slather,layer_population_scaling)
 S3method(slather,layer_predict)
-S3method(slather,layer_predictive_distn)
 S3method(slather,layer_quantile_distn)
 S3method(slather,layer_residual_quantiles)
 S3method(slather,layer_threshold)
diff --git a/R/layer_predictive_distn.R b/R/layer_predictive_distn.R
index 8ce55e1c3..824593f8d 100644
--- a/R/layer_predictive_distn.R
+++ b/R/layer_predictive_distn.R
@@ -1,5 +1,7 @@
 #' Returns predictive distributions
 #'
+#' `r lifecycle::badge("deprecated")`
+#'
 #' This function calculates an _approximation_ to a parametric predictive
 #' distribution. Predictive distributions from linear models require
 #' `x* (X'X)^{-1} x*`
@@ -16,102 +18,13 @@
 #'
 #' @return an updated `frosting` postprocessor with additional columns of the
 #'   residual quantiles added to the prediction
-
 #' @export
 #'
-#' @examples
-#' library(dplyr)
-#' jhu <- covid_case_death_rates %>%
-#'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
-#'
-#' r <- epi_recipe(jhu) %>%
-#'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
-#'   step_epi_ahead(death_rate, ahead = 7) %>%
-#'   step_epi_naomit()
-#'
-#' wf <- epi_workflow(r, linear_reg()) %>% fit(jhu)
-#'
-#' f <- frosting() %>%
-#'   layer_predict() %>%
-#'   layer_predictive_distn() %>%
-#'   layer_naomit(.pred)
-#' wf1 <- wf %>% add_frosting(f)
-#'
-#' p <- forecast(wf1)
-#' p
 layer_predictive_distn <- function(frosting,
                                    ...,
                                    dist_type = c("gaussian", "student_t"),
                                    truncate = c(-Inf, Inf),
                                    name = ".pred_distn",
                                    id = rand_id("predictive_distn")) {
-  if (!requireNamespace("distributional", quietly = TRUE)) {
-    cli_abort(paste(
-      "You must install the {.pkg distributional} package for",
-      "this functionality."
-    ))
-  }
-  rlang::check_dots_empty()
-  arg_is_chr_scalar(name, id)
-  dist_type <- match.arg(dist_type)
-  stopifnot(
-    length(truncate) == 2L, is.numeric(truncate), truncate[1] < truncate[2]
-  )
-
-  add_layer(
-    frosting,
-    layer_predictive_distn_new(
-      dist_type = dist_type,
-      truncate = truncate,
-      name = name,
-      id = id
-    )
-  )
-}
-
-layer_predictive_distn_new <- function(dist_type, truncate, name, id) {
-  layer("predictive_distn",
-    dist_type = dist_type, truncate = truncate,
-    name = name, id = id
-  )
-}
-
-#' @export
-slather.layer_predictive_distn <-
-  function(object, components, workflow, new_data, ...) {
-    the_fit <- workflows::extract_fit_parsnip(workflow)
-    rlang::check_dots_empty()
-
-    m <- components$predictions$.pred
-    r <- grab_residuals(the_fit, components)
-    df <- the_fit$df.residual
-    n <- sum(!is.na(r))
-    papprox <- ncol(components$mold$predictors) + 1
-    if (is.null(df)) df <- n - papprox
-    mse <- sum(r^2, na.rm = TRUE) / df
-    s <- sqrt(mse * (1 + papprox / df)) # E[x (X'X)^1 x] if E[X'X] ~= (n-p) I
-    dstn <- switch(object$dist_type,
-      gaussian = distributional::dist_normal(m, s),
-      student_t = distributional::dist_student_t(df, m, s)
-    )
-    truncate <- object$truncate
-    if (!all(is.infinite(truncate))) {
-      dstn <- distributional::dist_truncated(dstn, truncate[1], truncate[2])
-    }
-    dstn <- tibble(dstn = dstn)
-    dstn <- check_pname(dstn, components$predictions, object)
-    components$predictions <- mutate(components$predictions, !!!dstn)
-    components
-  }
-
-#' @export
-print.layer_predictive_distn <- function(
-    x, width = max(20, options()$width - 30), ...) {
-  title <- "Creating approximate predictive intervals"
-  td <- "<calculated>"
-  td <- rlang::enquos(td)
-  print_layer(td,
-    title = title, width = width, conjunction = "type",
-    extra_text = x$dist_type
-  )
+  lifecycle::deprecate_stop("0.1.11", "layer_predictive_distn()", "layer_residual_quantiles()")
 }
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index 67fbfa28c..c6bc217f3 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -78,20 +78,12 @@ layer_quantile_distn_new <- function(quantile_levels, truncate, name, id) {
 slather.layer_quantile_distn <-
   function(object, components, workflow, new_data, ...) {
     dstn <- components$predictions$.pred
-    is_supported <- inherits(dstn, "distribution") ||
-      inherits(dstn, "quantile_pred")
-    if (!is_supported) {
+    if (!inherits(dstn, "quantile_pred")) {
       cli_abort(
-        "`layer_quantile_distn()` requires distributional or quantile
+        "`layer_quantile_distn()` requires or quantile
         predictions. These are of class {.cls {class(dstn)}}."
       )
     }
-    if (inherits(dstn, "distribution") && !requireNamespace("distributional", quietly = TRUE)) {
-      cli_abort(
-        "You must install the {.pkg distributional} package for this
-        functionality."
-      )
-    }
     rlang::check_dots_empty()
 
     dstn <- quantile_pred(
diff --git a/R/pivot_quantiles.R b/R/pivot_quantiles.R
index 8e846d7e6..e4b5b3320 100644
--- a/R/pivot_quantiles.R
+++ b/R/pivot_quantiles.R
@@ -12,39 +12,23 @@
 #' @export
 #'
 #' @examples
-#' .pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
-#' nested_quantiles(.pred_quantile)
+#' pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+#' nested_quantiles(pred_quantile)
 #'
-#' .pred_quantile %>%
+#' pred_quantile %>%
 #'   as_tibble() %>%
 #'   tidyr::nest(.by = .row) %>%
 #'   dplyr::select(-.row)
 #'
 nested_quantiles <- function(x) {
   lifecycle::deprecate_warn("0.1.11", "nested_quantiles()", "hardhat::quantile_pred()")
-  if (inherits(x, "distribution")) {
-    if (requireNamespace("distributional")) {
-      x <- vctrs::vec_data(x)
-      return(distributional:::dist_apply(x, .f = function(z) {
-        as_tibble(vctrs::vec_data(z)) %>%
-          mutate(across(everything(), as.double)) %>%
-          vctrs::list_of()
-      }))
-    } else {
-      cli_abort(c(
-        "`nested_quantiles()` is deprecated and the {.pkg distributional}",
-        `!` = "package is not installed.",
-        i = "See {.fn hardhat::quantile_pred}."
-      ))
-    }
-  }
   if (inherits(x, "quantile_pred")) {
     return(x %>% as_tibble() %>% tidyr::nest(.by = .row) %>%
       dplyr::select(data))
   }
-  cli_abort(c(
+  cli_abort(
     "`nested_quantiles()` is deprecated. See {.fn hardhat::quantile_pred}."
-  ))
+  )
 }
 
 
@@ -113,11 +97,7 @@ pivot_quantiles_wider <- function(.data, ...) {
 }
 
 pivot_quantiles <- function(.data, ...) {
-  msg <- c(
-    "{.fn pivot_quantiles} was deprecated in {.pkg epipredict} 0.0.6",
-    i = "Please use {.fn pivot_quantiles_wider} instead."
-  )
-  lifecycle::deprecate_stop(msg)
+  lifecycle::deprecate_stop("0.0.6", "pivot_quantiles()", "pivot_quantiles_wider()")
 }
 
 validate_pivot_quantiles <- function(.data, ..., call = caller_env()) {
diff --git a/man/layer_predictive_distn.Rd b/man/layer_predictive_distn.Rd
index 3bd95425b..0e4b17cdb 100644
--- a/man/layer_predictive_distn.Rd
+++ b/man/layer_predictive_distn.Rd
@@ -31,6 +31,9 @@ an updated \code{frosting} postprocessor with additional columns of the
 residual quantiles added to the prediction
 }
 \description{
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
+}
+\details{
 This function calculates an \emph{approximation} to a parametric predictive
 distribution. Predictive distributions from linear models require
 \verb{x* (X'X)^\{-1\} x*}
@@ -38,24 +41,3 @@ along with the degrees of freedom. This function approximates both. It
 should be reasonably accurate for models fit using \code{lm} when the new point
 \verb{x*} isn't too far from the bulk of the data.
 }
-\examples{
-library(dplyr)
-jhu <- covid_case_death_rates \%>\%
-  filter(time_value > "2021-11-01", geo_value \%in\% c("ak", "ca", "ny"))
-
-r <- epi_recipe(jhu) \%>\%
-  step_epi_lag(death_rate, lag = c(0, 7, 14)) \%>\%
-  step_epi_ahead(death_rate, ahead = 7) \%>\%
-  step_epi_naomit()
-
-wf <- epi_workflow(r, linear_reg()) \%>\% fit(jhu)
-
-f <- frosting() \%>\%
-  layer_predict() \%>\%
-  layer_predictive_distn() \%>\%
-  layer_naomit(.pred)
-wf1 <- wf \%>\% add_frosting(f)
-
-p <- forecast(wf1)
-p
-}
diff --git a/man/nested_quantiles.Rd b/man/nested_quantiles.Rd
index 4b666ee32..c8c05d94f 100644
--- a/man/nested_quantiles.Rd
+++ b/man/nested_quantiles.Rd
@@ -20,10 +20,10 @@ This function is deprecated. The recommended alternative is
 \code{\link[hardhat:quantile_pred]{hardhat::quantile_pred()}} with \code{\link[tibble:as_tibble]{tibble::as_tibble()}}
 }
 \examples{
-.pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
-nested_quantiles(.pred_quantile)
+pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+nested_quantiles(pred_quantile)
 
-.pred_quantile \%>\%
+pred_quantile \%>\%
   as_tibble() \%>\%
   tidyr::nest(.by = .row) \%>\%
   dplyr::select(-.row)

From 65b2ea4d80df65f6ba82b66e78ba42529893bd68 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 14:28:13 -0800
Subject: [PATCH 15/22] unnecessary lazydata (no data)

---
 DESCRIPTION | 1 -
 1 file changed, 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1ce1f8ef1..0d5398444 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -76,6 +76,5 @@ Remotes:
 Config/Needs/website: cmu-delphi/delphidocs
 Config/testthat/edition: 3
 Encoding: UTF-8
-LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2

From c7f492f420cd3c843c4998b1b24e8ae581bff335 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 14:31:36 -0800
Subject: [PATCH 16/22] style

---
 R/quantile_pred-methods.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 16b30f941..84412e4dd 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -153,8 +153,7 @@ quantile.quantile_pred <- function(x,
                                    lower = -Inf,
                                    upper = Inf,
                                    middle = c("cubic", "linear"),
-                                   ...
-) {
+                                   ...) {
   arg_is_probabilities(probs)
   arg_is_scalar(lower, upper, na.rm)
   arg_is_numeric(lower, upper)

From cc405c729407a9da01eca16d821c2e3a9b65a48b Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 26 Feb 2025 15:30:39 -0800
Subject: [PATCH 17/22] bump version number

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 0d5398444..8ced21d2d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.11
+Version: 0.1.12
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),

From 161feb785288e50bc9391a76215ebf7216080ee5 Mon Sep 17 00:00:00 2001
From: "Daniel J. McDonald" <dajmcdon@gmail.com>
Date: Wed, 5 Mar 2025 11:07:49 -0800
Subject: [PATCH 18/22] missing reexports

---
 R/reexports.R | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/R/reexports.R b/R/reexports.R
index 2cc7aa9fa..4166b6e0c 100644
--- a/R/reexports.R
+++ b/R/reexports.R
@@ -65,3 +65,10 @@ tidyr::pivot_wider
 #' @importFrom tidyr unnest
 #' @export
 tidyr::unnest
+
+#' @importFrom hardhat quantile_pred extract_quantile_levels
+#' @export
+hardhat::quantile_pred
+
+#' @export
+hardhat::extract_quantile_levels

From c36b2421971acf7e3660c870881bb007ab55706a Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Tue, 11 Mar 2025 18:24:21 -0500
Subject: [PATCH 19/22] minor docs, testing multiple pivot_longers

---
 R/quantile_pred-methods.R             | 2 +-
 man/quantile.quantile_pred.Rd         | 2 +-
 tests/testthat/test-pivot_quantiles.R | 5 +++++
 3 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/R/quantile_pred-methods.R b/R/quantile_pred-methods.R
index 84412e4dd..293fad902 100644
--- a/R/quantile_pred-methods.R
+++ b/R/quantile_pred-methods.R
@@ -120,7 +120,7 @@ vec_proxy_equal.quantile_pred <- function(x, ...) {
 #' Given a [hardhat::quantile_pred] object, users may wish to compute additional
 #' `quantile_levels` that are not part of the object. This function attempts
 #' to estimate these quantities under some assumptions. Interior probabilities,
-#' those contained within, existing probabilities are interpolated in a manner
+#' those contained within existing probabilities are interpolated in a manner
 #' controled by the `middle` argument. Those outside existing probabilities
 #' are extrapolated under the assumption that the tails of the distribution
 #' decays exponentially. Optionally, one may constrain _all_ quantiles to be
diff --git a/man/quantile.quantile_pred.Rd b/man/quantile.quantile_pred.Rd
index 246a3e987..1c23daed4 100644
--- a/man/quantile.quantile_pred.Rd
+++ b/man/quantile.quantile_pred.Rd
@@ -46,7 +46,7 @@ value in \code{probs}
 Given a \link[hardhat:quantile_pred]{hardhat::quantile_pred} object, users may wish to compute additional
 \code{quantile_levels} that are not part of the object. This function attempts
 to estimate these quantities under some assumptions. Interior probabilities,
-those contained within, existing probabilities are interpolated in a manner
+those contained within existing probabilities are interpolated in a manner
 controled by the \code{middle} argument. Those outside existing probabilities
 are extrapolated under the assumption that the tails of the distribution
 decays exponentially. Optionally, one may constrain \emph{all} quantiles to be
diff --git a/tests/testthat/test-pivot_quantiles.R b/tests/testthat/test-pivot_quantiles.R
index 39db31b2f..d32c16499 100644
--- a/tests/testthat/test-pivot_quantiles.R
+++ b/tests/testthat/test-pivot_quantiles.R
@@ -22,6 +22,11 @@ test_that("pivotting wider still works if there are duplicates", {
   tb <- tibble(.pred = quantile_pred(rbind(1:3, 1:3), c(.1, .5, .9)))
   res <- tibble(`0.1` = c(1, 1), `0.5` = c(2, 2), `0.9` = c(3, 3))
   expect_equal(tb %>% pivot_quantiles_wider(.pred), res)
+  res_longer <- tibble(
+    .pred_value = rep(1:3, 2),
+    .pred_quantile_level = rep(c(0.1, 0.5, 0.9), 2)
+  )
+  expect_equal(tb %>% pivot_quantiles_longer(.pred), res_longer)
 })
 
 

From 6c5e70f6779c339609a8b4c8953961faf73d40fe Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 12 Mar 2025 16:18:39 -0500
Subject: [PATCH 20/22] updating for climatological

---
 NAMESPACE                          |    5 +-
 R/climatological_forecaster.R      |    2 +-
 R/layer_threshold_preds.R          |    6 +-
 man/reexports.Rd                   |   32 +-
 tests/testthat/_snaps/snapshots.md | 1291 +++++++---------------------
 5 files changed, 356 insertions(+), 980 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 3c59dcb06..939a1f01a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -136,6 +136,7 @@ export(arx_class_epi_workflow)
 export(arx_classifier)
 export(arx_fcast_epi_workflow)
 export(arx_forecaster)
+export(as_epi_df)
 export(as_tibble)
 export(autoplot)
 export(bake)
@@ -182,6 +183,7 @@ export(layer_quantile_distn)
 export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
+export(mutate)
 export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
@@ -200,6 +202,7 @@ export(rename)
 export(select)
 export(slather)
 export(smooth_quantile_reg)
+export(snap)
 export(step_adjust_latency)
 export(step_climate)
 export(step_epi_ahead)
@@ -282,11 +285,11 @@ importFrom(hardhat,extract_recipe)
 importFrom(hardhat,quantile_pred)
 importFrom(hardhat,refresh_blueprint)
 importFrom(hardhat,run_mold)
+importFrom(lifecycle,deprecated)
 importFrom(lubridate,"%m-%")
 importFrom(lubridate,leap_year)
 importFrom(lubridate,month)
 importFrom(lubridate,yday)
-importFrom(lifecycle,deprecated)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)
 importFrom(recipes,bake)
diff --git a/R/climatological_forecaster.R b/R/climatological_forecaster.R
index 7df853252..e29d7d4a3 100644
--- a/R/climatological_forecaster.R
+++ b/R/climatological_forecaster.R
@@ -148,7 +148,7 @@ climatological_forecaster <- function(epi_data,
       .by = all_of(args_list$quantile_by_key)
     ) %>%
     rename(.pred_distn = climate_pred) %>%
-    mutate(.pred_distn = dist_quantiles(.pred_distn, args_list$quantile_levels))
+    mutate(.pred_distn = hardhat::quantile_pred(do.call(rbind, .pred_distn), args_list$quantile_levels))
   # combine them together
   climate_table <- climate_center %>%
     left_join(climate_quantiles, by = c(".idx", args_list$quantile_by_key)) %>%
diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 9acf06725..2f1720301 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -61,7 +61,11 @@ layer_threshold_new <-
 
 
 
-# restrict various objects to the interval [lower, upper]
+#' restrict various objects to the interval [lower, upper]
+#' @param x the object to restrict
+#' @param lower numeric, the lower bound
+#' @param upper numeric, the upper bound
+#' @param ... unused
 #' @export
 #' @keywords internal
 snap <- function(x, lower, upper, ...) {
diff --git a/man/reexports.Rd b/man/reexports.Rd
index ab2cf17be..8d3e360f4 100644
--- a/man/reexports.Rd
+++ b/man/reexports.Rd
@@ -1,30 +1,29 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/autoplot.R, R/reexports.R
+% Please edit documentation in R/autoplot.R, R/reexports-tidymodels.R,
+%   R/reexports.R
 \docType{import}
 \name{reexports}
 \alias{reexports}
 \alias{autoplot}
-\alias{filter}
-\alias{mutate}
-\alias{rename}
-\alias{select}
-\alias{as_epi_df}
-\alias{key_colnames}
 \alias{fit}
 \alias{forecast}
-\alias{tidy}
 \alias{prep}
 \alias{bake}
 \alias{rand_id}
-\alias{as_tibble}
 \alias{tibble}
-\alias{pivot_longer}
-\alias{pivot_wider}
-\alias{unnest}
 \alias{as_tibble}
 \alias{tidy}
 \alias{quantile_pred}
 \alias{extract_quantile_levels}
+\alias{filter}
+\alias{mutate}
+\alias{rename}
+\alias{select}
+\alias{as_epi_df}
+\alias{key_colnames}
+\alias{pivot_longer}
+\alias{pivot_wider}
+\alias{unnest}
 \title{Objects exported from other packages}
 \keyword{internal}
 \description{
@@ -36,15 +35,16 @@ below to see their documentation.
 
   \item{epiprocess}{\code{\link[epiprocess:epi_df]{as_epi_df}}, \code{\link[epiprocess]{key_colnames}}}
 
-  \item{generics}{\code{\link[generics]{fit}}, \code{\link[generics]{forecast}}, \code{\link[generics]{tidy}}}
+  \item{generics}{\code{\link[generics]{fit}}, \code{\link[generics]{fit}}, \code{\link[generics]{forecast}}, \code{\link[generics]{forecast}}, \code{\link[generics]{tidy}}, \code{\link[generics]{tidy}}}
 
   \item{ggplot2}{\code{\link[ggplot2]{autoplot}}}
 
-  \item{hardhat}{\code{\link[hardhat:quantile_pred]{extract_quantile_levels}}, \code{\link[hardhat]{quantile_pred}}}
+  \item{hardhat}{\code{\link[hardhat:quantile_pred]{extract_quantile_levels}}, \code{\link[hardhat:quantile_pred]{extract_quantile_levels}}, \code{\link[hardhat]{quantile_pred}}, \code{\link[hardhat]{quantile_pred}}}
 
-  \item{recipes}{\code{\link[recipes]{bake}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{rand_id}}}
+  \item{recipes}{\code{\link[recipes]{bake}}, \code{\link[recipes]{bake}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{prep}}, \code{\link[recipes]{rand_id}}, \code{\link[recipes]{rand_id}}}
 
-  \item{tibble}{\code{\link[tibble]{as_tibble}}, \code{\link[tibble]{tibble}}}
+  \item{tibble}{\code{\link[tibble]{as_tibble}}, \code{\link[tibble]{as_tibble}}, \code{\link[tibble]{tibble}}, \code{\link[tibble]{tibble}}}
 
   \item{tidyr}{\code{\link[tidyr]{pivot_longer}}, \code{\link[tidyr]{pivot_wider}}, \code{\link[tidyr]{unnest}}}
 }}
+
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 78f629171..b2972a8a0 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -843,136 +843,61 @@
     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(structure(list(values = c(55.01884375, 
-        61.9936534, 76.8558811, 85.8705552, 95.7238376875, 107.595355333333, 
-        124.8294820725), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(22.4852324, 
-        29.46004205, 44.32226975, 53.33694385, 63.1902263375, 75.0617439833333, 
-        92.2958707225), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(33.03575305, 
-        40.0105627, 54.8727904, 63.8874645, 73.7407469875, 85.6122646333333, 
-        102.8463913725), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(31.00572905, 
-        37.9805387, 52.8427664, 61.8574405, 71.7107229875, 83.5822406333333, 
-        100.8163673725), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(36.34589965, 
+        .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), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(27.4126191, 
-        34.38742875, 49.24965645, 58.26433055, 68.1176130375, 79.9891306833333, 
-        97.2232574225), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(59.1059257625, 
-        65.4267824, 80.893949775, 88.512741775, 98.9851218875, 111.446583986667, 
-        128.951180125), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(26.1435490625, 
+        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), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(36.8149959625, 
-        43.1358526, 58.603019975, 66.221811975, 76.6941920875, 89.1556541866667, 
-        106.660250325), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(41.8898725625, 
-        48.2107292, 63.677896575, 71.296688575, 81.7690686875, 94.2305307866667, 
-        111.735126925), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(37.4322168625, 
+        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), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(34.2319594625, 
-        40.5528161, 56.019983475, 63.638775475, 74.1111555875, 86.5726176866667, 
-        104.077213825), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 
-        0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
-        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(76.597008525, 
-        83.6285187166667, 89.8466094166667, 96.39787945, 106.5883326375, 
-        118.488928483333, 139.8972249625), quantile_levels = c(0.05, 
-        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(36.051994425, 43.0835046166667, 49.3015953166667, 
-            55.85286535, 66.0433185375, 77.9439143833333, 99.3522108625
-            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(48.463300925, 55.4948111166667, 
+        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, 
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@@ -990,120 +915,47 @@
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+        16573.6666666667, 17672, 19117.3333333333, 21921.5333333333, 
+        24647), c(6569.2, 7244.53333333333, 8381.66666666667, 9484, 
+        10946.6666666667, 12994.3333333333, 14599.6), c(2365.2, 3153.8, 
+        4329.33333333333, 5343, 6039.66666666667, 6603.53333333333, 
+        6695.8), c(8524, 8743.66666666667, 9836, 11120, 12910.3333333333, 
+        13495.4666666667, 14369), c(2761.6, 2999, 4092, 4438, 5124.66666666667, 
+        5675.53333333333, 6811.8), c(4336.6, 6789.13333333334, 13394, 
+        17898, 20165.6666666667, 25111.7333333333, 27349.4), c(13648.4, 
+        14721.3333333333, 15978.6666666667, 17077, 18266, 19933.0666666667, 
+        22864), c(5754.2, 6429.53333333333, 7566.66666666667, 8669, 
+        10050.3333333333, 10882.4, 13120.6), c(2361.2, 3149.8, 4325.33333333333, 
+        5270, 5878.66666666667, 6580.4, 6683.8), c(8434, 8653.66666666667, 
+        9746, 11030, 12458, 13329.8, 13463.8), c(2669.6, 2907, 4000, 
+        4346, 5032.66666666667, 5583.53333333333, 6719.8), c(3177.6, 
+        5630.13333333334, 12235, 16653, 18812.6666666667, 23952.7333333333, 
+        26190.4)), 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, 
+    -186L), class = c("tbl_df", "tbl", "data.frame"))
 

From c3f0c776a0992a312e271cc115a5e8031814547d Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 12 Mar 2025 16:35:12 -0500
Subject: [PATCH 21/22] neglected docs

---
 man/epiweek_leap.Rd | 12 ++++++++++++
 man/isoweek_leap.Rd | 12 ++++++++++++
 man/snap.Rd         | 21 +++++++++++++++++++++
 man/yday_leap.Rd    | 14 ++++++++++++++
 4 files changed, 59 insertions(+)
 create mode 100644 man/epiweek_leap.Rd
 create mode 100644 man/isoweek_leap.Rd
 create mode 100644 man/snap.Rd
 create mode 100644 man/yday_leap.Rd

diff --git a/man/epiweek_leap.Rd b/man/epiweek_leap.Rd
new file mode 100644
index 000000000..4fecbf2ca
--- /dev/null
+++ b/man/epiweek_leap.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate.R
+\name{epiweek_leap}
+\alias{epiweek_leap}
+\title{epiweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap}
+\usage{
+epiweek_leap(time_value)
+}
+\description{
+epiweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap
+}
+\keyword{internal}
diff --git a/man/isoweek_leap.Rd b/man/isoweek_leap.Rd
new file mode 100644
index 000000000..355d27b1e
--- /dev/null
+++ b/man/isoweek_leap.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate.R
+\name{isoweek_leap}
+\alias{isoweek_leap}
+\title{isoweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap}
+\usage{
+isoweek_leap(time_value)
+}
+\description{
+isoweek, but it assigns week 53 the value of 999 instead so it mirrors the assignments in yday_leap
+}
+\keyword{internal}
diff --git a/man/snap.Rd b/man/snap.Rd
new file mode 100644
index 000000000..2f5ca57d5
--- /dev/null
+++ b/man/snap.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/layer_threshold_preds.R
+\name{snap}
+\alias{snap}
+\title{restrict various objects to the interval \link{lower, upper}}
+\usage{
+snap(x, lower, upper, ...)
+}
+\arguments{
+\item{x}{the object to restrict}
+
+\item{lower}{numeric, the lower bound}
+
+\item{upper}{numeric, the upper bound}
+
+\item{...}{unused}
+}
+\description{
+restrict various objects to the interval \link{lower, upper}
+}
+\keyword{internal}
diff --git a/man/yday_leap.Rd b/man/yday_leap.Rd
new file mode 100644
index 000000000..1dc873a38
--- /dev/null
+++ b/man/yday_leap.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/step_climate.R
+\name{yday_leap}
+\alias{yday_leap}
+\title{a function that assigns Feb 29th to 999, and aligns all other dates the same
+number in the year, regardless of whether it's a leap year}
+\usage{
+yday_leap(time_value)
+}
+\description{
+a function that assigns Feb 29th to 999, and aligns all other dates the same
+number in the year, regardless of whether it's a leap year
+}
+\keyword{internal}

From 8387aa2633dad5a6d985ef8617ff0bfe1af8a31e Mon Sep 17 00:00:00 2001
From: dsweber2 <david.weber2@pm.me>
Date: Wed, 12 Mar 2025 16:50:26 -0500
Subject: [PATCH 22/22] no accidental link

---
 R/layer_threshold_preds.R | 2 +-
 man/snap.Rd               | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/layer_threshold_preds.R b/R/layer_threshold_preds.R
index 2f1720301..2869fff07 100644
--- a/R/layer_threshold_preds.R
+++ b/R/layer_threshold_preds.R
@@ -61,7 +61,7 @@ layer_threshold_new <-
 
 
 
-#' restrict various objects to the interval [lower, upper]
+#' restrict various objects to the interval \[lower, upper\]
 #' @param x the object to restrict
 #' @param lower numeric, the lower bound
 #' @param upper numeric, the upper bound
diff --git a/man/snap.Rd b/man/snap.Rd
index 2f5ca57d5..abe4421e8 100644
--- a/man/snap.Rd
+++ b/man/snap.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/layer_threshold_preds.R
 \name{snap}
 \alias{snap}
-\title{restrict various objects to the interval \link{lower, upper}}
+\title{restrict various objects to the interval [lower, upper]}
 \usage{
 snap(x, lower, upper, ...)
 }
@@ -16,6 +16,6 @@ snap(x, lower, upper, ...)
 \item{...}{unused}
 }
 \description{
-restrict various objects to the interval \link{lower, upper}
+restrict various objects to the interval [lower, upper]
 }
 \keyword{internal}