diff --git a/NAMESPACE b/NAMESPACE
index 4823d8df..1752f215 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -119,6 +119,10 @@ export(ppc_bars)
 export(ppc_bars_data)
 export(ppc_bars_grouped)
 export(ppc_boxplot)
+export(ppc_calibration)
+export(ppc_calibration_grouped)
+export(ppc_calibration_overlay)
+export(ppc_calibration_overlay_grouped)
 export(ppc_data)
 export(ppc_dens)
 export(ppc_dens_overlay)
@@ -142,6 +146,8 @@ export(ppc_intervals_data)
 export(ppc_intervals_grouped)
 export(ppc_km_overlay)
 export(ppc_km_overlay_grouped)
+export(ppc_loo_calibration)
+export(ppc_loo_calibration_grouped)
 export(ppc_loo_intervals)
 export(ppc_loo_pit)
 export(ppc_loo_pit_data)
diff --git a/R/ppc-calibration.R b/R/ppc-calibration.R
new file mode 100644
index 00000000..c26d1202
--- /dev/null
+++ b/R/ppc-calibration.R
@@ -0,0 +1,224 @@
+# x' PPC calibration
+#'
+#' Assess the calibration of the predictive distributions `yrep` in relation to
+#' the data `y'.
+#' See the **Plot Descriptions** section, below, for details.
+#'
+#' @name PPC-calibration
+#' @family PPCs
+#'
+#' @template args-y-yrep
+#' @template args-group
+#'
+#' @template return-ggplot-or-data
+#'
+#' @section Plot Descriptions:
+#' \describe{
+#'   \item{`ppc_calibration()`,`ppc_calibration_grouped()`}{
+#'
+#'   },
+#'   \item{`ppc_calibration_overlay()`,`ppc_calibration_overlay_grouped()`}{
+#'
+#'   },
+#'   \item{`ppc_loo_calibration()`,`ppc_loo_calibration_grouped()`}{
+#'
+#'   }
+#' }
+#'
+#' @examples
+#' color_scheme_set("brightblue")
+#'
+#' # Make an example dataset of binary observations
+#' ymin <- range(example_y_data(), example_yrep_draws())[1]
+#' ymax <- range(example_y_data(), example_yrep_draws())[2]
+#' y <- rbinom(length(example_y_data()), 1, (example_y_data() - ymin) / (ymax - ymin))
+#' prep <- (example_yrep_draws() - ymin) / (ymax - ymin)
+#'
+#' ppc_calibration_overlay(y, prep[1:50, ])
+NULL
+
+
+#' @rdname PPC-calibration
+#' @export
+ppc_calibration_overlay <- function(
+    y, prep, ..., linewidth = 0.25, alpha = 0.5) {
+  check_ignored_arguments(...)
+  data <- .ppc_calibration_data(y, prep)
+  ggplot(data) +
+    geom_abline(color = "black", linetype = 2) +
+    geom_line(
+      aes(value, cep, group = rep_id, color = "yrep"),
+      linewidth = linewidth, alpha = alpha
+    ) +
+    scale_color_ppc() +
+    bayesplot_theme_get() +
+    legend_none() +
+    coord_equal(xlim = c(0, 1), ylim = c(0, 1), expand = FALSE) +
+    xlab("Predicted probability") +
+    ylab("Conditional event probability") +
+    NULL
+}
+
+#' @rdname PPC-calibration
+#' @export
+ppc_calibration_overlay_grouped <- function(
+    y, prep, group, ..., linewidth = 0.25, alpha = 0.7) {
+  check_ignored_arguments(...)
+  data <- .ppc_calibration_data(y, prep, group)
+  ggplot(data) +
+    geom_abline(color = "black", linetype = 2) +
+    geom_line(aes(value, cep, group = rep_id, color = "yrep"),
+      linewidth = linewidth, alpha = alpha
+    ) +
+    facet_wrap(vars(group)) +
+    scale_color_ppc() +
+    bayesplot_theme_get() +
+    legend_none() +
+    coord_equal(xlim = c(0, 1), ylim = c(0, 1), expand = FALSE) +
+    xlab("Predicted probability") +
+    ylab("Conditional event probability") +
+    NULL
+}
+
+#' @rdname PPC-calibration
+#' @export
+ppc_calibration <- function(
+    y, prep, prob = .95, show_mean = TRUE, ..., linewidth = 0.5, alpha = 0.7) {
+  check_ignored_arguments(...)
+  data <- .ppc_calibration_data(y, prep) %>%
+    group_by(y_id) %>%
+    summarise(
+      value = median(value),
+      lb = quantile(cep, .5 - .5 * prob),
+      ub = quantile(cep, .5 + .5 * prob),
+      cep = median(cep)
+    )
+
+  ggplot(data) +
+    aes(value, cep) +
+    geom_abline(color = "black", linetype = 2) +
+    geom_ribbon(aes(ymin = lb, ymax = ub, fill = "yrep"), alpha = alpha) +
+    geom_line(
+      aes(color = "y"),
+      linewidth = linewidth
+    ) +
+    scale_color_ppc() +
+    scale_fill_ppc() +
+    bayesplot_theme_get() +
+    legend_none() +
+    coord_equal(xlim = c(0, 1), ylim = c(0, 1), expand = FALSE) +
+    xlab("Predicted probability") +
+    ylab("Conditional event probability") +
+    NULL
+}
+
+#' @rdname PPC-calibration
+#' @export
+ppc_calibration_grouped <- function(
+    y, prep, group, prob = .95, show_mean = TRUE, ..., linewidth = 0.5, alpha = 0.7) {
+  check_ignored_arguments(...)
+  data <- .ppc_calibration_data(y, prep, group) %>%
+    group_by(group, y_id) %>%
+    summarise(
+      value = median(value),
+      lb = quantile(cep, .5 - .5 * prob),
+      ub = quantile(cep, .5 + .5 * prob),
+      cep = median(cep)
+    )
+
+  ggplot(data) +
+    aes(value, cep) +
+    geom_abline(color = "black", linetype = 2) +
+    geom_ribbon(aes(ymin = lb, ymax = ub, fill = "yrep"), alpha = alpha) +
+    geom_line(
+      aes(color = "y"),
+      linewidth = linewidth
+    ) +
+    facet_wrap(vars(group)) +
+    scale_color_ppc() +
+    scale_fill_ppc() +
+    bayesplot_theme_get() +
+    legend_none() +
+    coord_equal(xlim = c(0, 1), ylim = c(0, 1), expand = FALSE) +
+    xlab("Predicted probability") +
+    ylab("Conditional event probability") +
+    NULL
+}
+
+#' @rdname PPC-calibration
+#' @export
+ppc_loo_calibration <- function(
+    y, prep, lw, ..., linewidth = 0.25, alpha = 0.7) {
+  check_ignored_arguments(...)
+  data <- .ppc_calibration_data(y, prep)
+  ggplot(data) +
+    geom_abline(color = "black", linetype = 2) +
+    geom_line(
+      aes(value, cep, group = rep_id, color = "yrep"),
+      linewidth = linewidth, alpha = alpha
+    ) +
+    scale_color_ppc() +
+    bayesplot_theme_get() +
+    legend_none() +
+    coord_equal(xlim = c(0, 1), ylim = c(0, 1), expand = FALSE) +
+    xlab("Predicted probability") +
+    ylab("Conditional event probability") +
+    NULL
+}
+
+#' @rdname PPC-calibration
+#' @export
+ppc_loo_calibration_grouped <- function(
+    y, prep, group, lw, ..., linewidth = 0.25, alpha = 0.7) {
+  check_ignored_arguments(...)
+  data <- .ppc_calibration_data(y, prep, group)
+  ggplot(data) +
+    geom_abline(color = "black", linetype = 2) +
+    geom_line(aes(value, cep, group = rep_id, color = "yrep"),
+      linewidth = linewidth, alpha = alpha
+    ) +
+    facet_wrap(vars(group)) +
+    scale_color_ppc() +
+    bayesplot_theme_get() +
+    legend_none() +
+    coord_equal(xlim = c(0, 1), ylim = c(0, 1), expand = FALSE) +
+    xlab("Predicted probability") +
+    ylab("Conditional event probability") +
+    NULL
+}
+
+.ppc_calibration_data <- function(y, prep, group = NULL) {
+  y <- validate_y(y)
+  n_obs <- length(y)
+  prep <- validate_predictions(prep, n_obs)
+  if (any(prep > 1 | prep < 0)) {
+    stop("Values of ´prep´ should be predictive probabilities between 0 and 1.")
+  }
+  if (!is.null(group)) {
+    group <- validate_group(group, n_obs)
+  } else {
+    group <- rep(1, n_obs * nrow(prep))
+  }
+
+  if (requireNamespace("monotone", quietly = TRUE)) {
+    monotone <- monotone::monotone
+  } else {
+    monotone <- function(y) {
+      stats::isoreg(y)$yf
+    }
+  }
+  .ppd_data(prep, group = group) %>%
+    group_by(group, rep_id) %>%
+    mutate(
+      ord = order(value),
+      value = value[ord],
+      cep = monotone(y[ord])
+    ) |>
+    ungroup()
+}
+
+.loo_resample_data <- function(prep, lw, psis_object) {
+  lw <- .get_lw(lw, psis_object)
+  stopifnot(identical(dim(prep), dim(lw)))
+  # Work in progress here...
+}
diff --git a/man/PPC-calibration.Rd b/man/PPC-calibration.Rd
new file mode 100644
index 00000000..fbd9a0c3
--- /dev/null
+++ b/man/PPC-calibration.Rd
@@ -0,0 +1,115 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ppc-calibration.R
+\name{PPC-calibration}
+\alias{PPC-calibration}
+\alias{ppc_calibration_overlay}
+\alias{ppc_calibration_overlay_grouped}
+\alias{ppc_calibration}
+\alias{ppc_calibration_grouped}
+\alias{ppc_loo_calibration}
+\alias{ppc_loo_calibration_grouped}
+\title{Assess the calibration of the predictive distributions \code{yrep} in relation to
+the data `y'.
+See the \strong{Plot Descriptions} section, below, for details.}
+\usage{
+ppc_calibration_overlay(y, prep, ..., linewidth = 0.25, alpha = 0.5)
+
+ppc_calibration_overlay_grouped(
+  y,
+  prep,
+  group,
+  ...,
+  linewidth = 0.25,
+  alpha = 0.7
+)
+
+ppc_calibration(y, prep, prob = 0.95, ..., linewidth = 0.5, alpha = 0.7)
+
+ppc_calibration_grouped(
+  y,
+  prep,
+  group,
+  prob = 0.95,
+  ...,
+  linewidth = 0.5,
+  alpha = 0.7
+)
+
+ppc_loo_calibration(y, yrep, lw, ..., linewidth = 0.25, alpha = 0.7)
+
+ppc_loo_calibration_grouped(
+  y,
+  yrep,
+  group,
+  lw,
+  ...,
+  linewidth = 0.25,
+  alpha = 0.7
+)
+}
+\arguments{
+\item{y}{A vector of observations. See \strong{Details}.}
+
+\item{group}{A grouping variable of the same length as \code{y}.
+Will be coerced to \link[base:factor]{factor} if not already a factor.
+Each value in \code{group} is interpreted as the group level pertaining
+to the corresponding observation.}
+
+\item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior)
+predictive distribution. The number of rows, \code{S}, is the size of the
+posterior (or prior) sample used to generate \code{yrep}. The number of columns,
+\code{N} is the number of predicted observations (\code{length(y)}). The columns of
+\code{yrep} should be in the same order as the data points in \code{y} for the plots
+to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for
+additional advice specific to particular plots.}
+}
+\value{
+The plotting functions return a ggplot object that can be further
+customized using the \strong{ggplot2} package. The functions with suffix
+\verb{_data()} return the data that would have been drawn by the plotting
+function.
+}
+\description{
+Assess the calibration of the predictive distributions \code{yrep} in relation to
+the data `y'.
+See the \strong{Plot Descriptions} section, below, for details.
+}
+\section{Plot Descriptions}{
+
+\describe{
+\item{\code{ppc_calibration()},\code{ppc_calibration_grouped()}}{
+
+},
+\item{\code{ppc_calibration_overlay()},\code{ppc_calibration_overlay_grouped()}}{
+
+},
+\item{\code{ppc_loo_calibration()},\code{ppc_loo_calibration_grouped()}}{
+
+}
+}
+}
+
+\examples{
+color_scheme_set("brightblue")
+
+# Make an example dataset of binary observations
+ymin <- range(example_y_data(), example_yrep_draws())[1]
+ymax <- range(example_y_data(), example_yrep_draws())[2]
+y <- rbinom(length(example_y_data()), 1, (example_y_data() - ymin) / (ymax - ymin))
+prep <- (example_yrep_draws() - ymin) / (ymax - ymin)
+
+ppc_calibration_overlay(y, prep[1:50, ])
+}
+\seealso{
+Other PPCs: 
+\code{\link{PPC-censoring}},
+\code{\link{PPC-discrete}},
+\code{\link{PPC-distributions}},
+\code{\link{PPC-errors}},
+\code{\link{PPC-intervals}},
+\code{\link{PPC-loo}},
+\code{\link{PPC-overview}},
+\code{\link{PPC-scatterplots}},
+\code{\link{PPC-test-statistics}}
+}
+\concept{PPCs}
diff --git a/man/PPC-censoring.Rd b/man/PPC-censoring.Rd
index ddf011b4..30a7b78d 100644
--- a/man/PPC-censoring.Rd
+++ b/man/PPC-censoring.Rd
@@ -150,6 +150,7 @@ doi:10.1080/01621459.1958.10501452.
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-discrete}},
 \code{\link{PPC-distributions}},
 \code{\link{PPC-errors}},
diff --git a/man/PPC-discrete.Rd b/man/PPC-discrete.Rd
index 434ba7bd..4f2fcabd 100644
--- a/man/PPC-discrete.Rd
+++ b/man/PPC-discrete.Rd
@@ -216,6 +216,7 @@ Visualizing count data regressions using rootograms.
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-censoring}},
 \code{\link{PPC-distributions}},
 \code{\link{PPC-errors}},
diff --git a/man/PPC-distributions.Rd b/man/PPC-distributions.Rd
index 628a3ad5..991a9742 100644
--- a/man/PPC-distributions.Rd
+++ b/man/PPC-distributions.Rd
@@ -389,6 +389,7 @@ Press, London, third edition. (Ch. 6)
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-censoring}},
 \code{\link{PPC-discrete}},
 \code{\link{PPC-errors}},
diff --git a/man/PPC-errors.Rd b/man/PPC-errors.Rd
index 047590bf..c3196f26 100644
--- a/man/PPC-errors.Rd
+++ b/man/PPC-errors.Rd
@@ -227,6 +227,7 @@ Press, London, third edition. (Ch. 6)
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-censoring}},
 \code{\link{PPC-discrete}},
 \code{\link{PPC-distributions}},
diff --git a/man/PPC-intervals.Rd b/man/PPC-intervals.Rd
index ddac2ebc..369ab029 100644
--- a/man/PPC-intervals.Rd
+++ b/man/PPC-intervals.Rd
@@ -240,6 +240,7 @@ Press, London, third edition. (Ch. 6)
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-censoring}},
 \code{\link{PPC-discrete}},
 \code{\link{PPC-distributions}},
diff --git a/man/PPC-loo.Rd b/man/PPC-loo.Rd
index 10f29d8c..3113fab8 100644
--- a/man/PPC-loo.Rd
+++ b/man/PPC-loo.Rd
@@ -349,6 +349,7 @@ https://www.jstor.org/stable/2986005.
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-censoring}},
 \code{\link{PPC-discrete}},
 \code{\link{PPC-distributions}},
diff --git a/man/PPC-overview.Rd b/man/PPC-overview.Rd
index 16d3f0ff..f58465d2 100644
--- a/man/PPC-overview.Rd
+++ b/man/PPC-overview.Rd
@@ -124,6 +124,7 @@ Press, London, third edition. (Ch. 6)
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-censoring}},
 \code{\link{PPC-discrete}},
 \code{\link{PPC-distributions}},
diff --git a/man/PPC-scatterplots.Rd b/man/PPC-scatterplots.Rd
index 2c22bbd3..2e274273 100644
--- a/man/PPC-scatterplots.Rd
+++ b/man/PPC-scatterplots.Rd
@@ -155,6 +155,7 @@ Press, London, third edition. (Ch. 6)
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-censoring}},
 \code{\link{PPC-discrete}},
 \code{\link{PPC-distributions}},
diff --git a/man/PPC-test-statistics.Rd b/man/PPC-test-statistics.Rd
index 83883979..c9368794 100644
--- a/man/PPC-test-statistics.Rd
+++ b/man/PPC-test-statistics.Rd
@@ -213,6 +213,7 @@ Press, London, third edition. (Ch. 6)
 }
 \seealso{
 Other PPCs: 
+\code{\link{PPC-calibration}},
 \code{\link{PPC-censoring}},
 \code{\link{PPC-discrete}},
 \code{\link{PPC-distributions}},