diff --git a/NAMESPACE b/NAMESPACE
index 0436c4cb..7d557ce7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -126,6 +126,7 @@ export(ppc_stat_freqpoly_grouped)
 export(ppc_stat_grouped)
 export(ppc_violin_grouped)
 export(rhat)
+export(sbc_hist)
 export(scatter_style_np)
 export(theme_default)
 export(trace_style_np)
diff --git a/R/sbc.R b/R/sbc.R
new file mode 100644
index 00000000..65558848
--- /dev/null
+++ b/R/sbc.R
@@ -0,0 +1,188 @@
+#' Diagnostics for Simulation Based Calibration
+#'
+#' @export
+#' @param ranks A list of matrices. See **Details**.
+#' @param thin An integer indicating the thinning interval to use when plotting
+#'   so that the histograms consist of (close to) independent realizations. Set
+#'   the `thin` argument such that the resulting number of draws approximately
+#'   matches the effective sample size.
+#' @param per_bin An integer indicating the number of histogram entries to
+#'   combine into a single bar. For best results, one plus the number of draws
+#'   from the posterior should be evenly divisible by the number of histogram
+#'   bins after thinning. For example, if there are `511` posterior draws (per
+#'   matrix in `ranks`) and `thin=4`, then after thinning there will be `128`
+#'   draws. If `per_bin=4`, then `128/4=32` histogram bars will be drawn.
+#' @param worst How many parameters to show, or `NA` to plot all parameters. If
+#'   `worst` is not `NA` then parameters are ordered by the degree of
+#'   non-uniformity so, for example, `worst = 10` means to plot only the `10`
+#'   worst parameters.
+#' @param prob The size of the interval plotted to show the expected behavior
+#'   under uniformity. The default is `prob=0.99`.
+#' @param ... Currently ignored.
+#' @template args-facet_args
+#'
+#' @details
+#' Each element of the list `ranks` should be a matrix of rank comparison
+#' results (encoded as `0` or `1`) associated with a single draw from the prior
+#' distribution. (These are not actually "ranks" but can be used afterwards to
+#' reconstruct (thinned) ranks.) The columns of each matrix are model parameters
+#' and the rows are posterior draws from the model fit to the data generated
+#' from the corresponding prior draw. An element is `0` or `1` depending on
+#' whether the posterior draw is greater or less than the corresponding "true"
+#' realization (from the prior). The matrices should have column names to
+#' correctly label the parameters.
+#'
+#' @section Plot Descriptions:
+#' \describe{
+#'   \item{`sbc_hist()`}{
+#'   SBC histogram from Talts et al. (2018). A separate plot (facet) is created
+#'   for each parameter.
+#'   }
+#' }
+#'
+#' @template return-ggplot
+#'
+#' @references
+#' Talts, S., Betancourt, M., Simpson, D., Vehtari, A., and Gelman, A. (2018).
+#' Validating Bayesian Inference Algorithms with Simulation-Based Calibration.
+#' arXiv preprint arXiv:1804.06788. \url{https://arxiv.org/abs/1804.06788}
+#'
+#' @seealso `rstan::sbc()`
+#'
+#' @examples
+#' # create some fake inputs to use for sbc_hist()
+#' set.seed(19)
+#' pars <- paste0("beta[", 1:4, "]")
+#' samples_per_prior <- 511
+#' n_replications <- 500
+#' ranks <- list()
+#' for (n in 1:n_replications) {
+#'   r1 <- matrix(0, nrow=samples_per_prior, ncol=length(pars),
+#'                dimnames=list(NULL, pars))
+#'   for (p1 in 1:length(pars)) {
+#'     r1[sample.int(samples_per_prior, floor(runif(1, 0, samples_per_prior))), p1] <- 1
+#'   }
+#'   ranks[[n]] <- r1
+#' }
+#'
+#' color_scheme_set("purple")
+#' sbc_hist(ranks)
+#' sbc_hist(ranks, worst = NA) # uses original parameter ordering
+#' sbc_hist(ranks, worst = 3, facet_args = list(labeller = ggplot2::label_parsed))
+#'
+sbc_hist <- function(ranks,
+                     ...,
+                     thin = 4,
+                     per_bin = 4,
+                     worst = 16,
+                     prob = 0.99,
+                     facet_args = list()) {
+  check_ignored_arguments(...)
+  stopifnot(is.list(ranks), all(sapply(ranks, is.matrix)))
+  rows <- sapply(ranks, nrow)
+  cols <- sapply(ranks, ncol)
+  if (any(rows != rows[1]) || any(cols != cols[1])) {
+    stop("Not all matrices in 'ranks' have the same dimensions.")
+  }
+
+  num_prior_draws <- length(ranks)
+  thinner <- seq(from = 1, to = nrow(ranks[[1]]), by = thin)
+  thinned_sample_size <- length(thinner)
+  u <- t(sapply(ranks, FUN = function(r) 1 + colSums(r[thinner, , drop = FALSE])))
+  if (ncol(ranks[[1]]) == 1) {
+    u <- t(u)
+    dimnames(u) <- list(NULL, colnames(ranks[[1]]))
+  }
+
+  if (!is.na(worst)) {
+    # order starting with worst (least uniform)
+    kl <- apply(u, 2, function(v) kl_uniform(v, num_prior_draws, thinned_sample_size))
+    filter <- order(-kl)[1:min(worst, ncol(u))]
+    u <- u[, filter, drop = FALSE]
+  }
+
+  pars <- parameter_names(u)
+  data <- data.frame(
+    Parameter = ordered(rep(pars, each = nrow(u)), levels = pars),
+    u = c(u)
+  )
+
+  num_bins <- thinned_sample_size / per_bin
+  if (thinned_sample_size %% per_bin != 0) {
+    warning(paste0("per_bin (", per_bin, ") does not evenly divide the ",
+                   "number of samples per prior (", thinned_sample_size ,")."))
+  }
+
+  # data for polygon showing expected behavior under uniformity
+  alpha <- 1 - prob
+  CI <- qbinom(
+    p = c(alpha / 2, 0.5, 1 - alpha / 2),
+    size = num_prior_draws,
+    prob = 1 / num_bins
+  )
+  CI <- CI + c(-0.5, 0, 0.5)
+  offset <- 2 * per_bin
+  polygon_data <- data.frame(
+    x= c(-offset, 0, -offset, thinned_sample_size + offset,
+         thinned_sample_size, thinned_sample_size + offset, -offset),
+    y = c(CI[1], CI[2], CI[3], CI[3], CI[2], CI[1], CI[1])
+  )
+
+  graph <- ggplot(data, aes_(x = ~ u)) +
+    geom_polygon(
+      aes_(x = ~ x, y = ~ y),
+      data = polygon_data,
+      fill = "lightgray",
+      color = NA,
+      alpha = 1
+    ) +
+    geom_segment(
+      x = -offset,
+      xend = thinned_sample_size + offset,
+      y = CI[3],
+      yend = CI[3],
+      color = get_color("mid_highlight"),
+      alpha = 0.5,
+      size = 0.2
+    ) +
+    geom_histogram(
+      bins = num_bins,
+      fill = get_color("mid"),
+      color = get_color("mid_highlight"),
+      size = 0.25,
+      na.rm = TRUE
+    ) +
+    geom_segment(
+      x = -offset,
+      xend = thinned_sample_size + offset,
+      y = CI[1],
+      yend = CI[1],
+      color = get_color("mid_highlight"),
+      alpha = 0.1,
+      size = 0.2
+    ) +
+    scale_x_continuous(
+      name = "Rank statistic",
+      breaks = c(0, round(thinned_sample_size / 2), thinned_sample_size)
+    ) +
+    coord_cartesian(expand = FALSE)
+
+  facet_args[["facets"]] <- ~ Parameter
+  graph +
+    do.call("facet_wrap", facet_args) +
+    bayesplot_theme_get() +
+    yaxis_text(FALSE) +
+    yaxis_title(FALSE) +
+    yaxis_ticks(FALSE)
+}
+
+
+
+# internal ----------------------------------------------------------------
+kl_uniform <- function(v, num_prior_draws, samples_per_prior) {
+  # D_{KL}(v || uniform)
+  # https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
+  expected_pr <- 1.0 / samples_per_prior
+  observed_pr <- table(v) / num_prior_draws
+  sum(observed_pr * log(observed_pr / expected_pr))
+}
diff --git a/man/sbc_hist.Rd b/man/sbc_hist.Rd
new file mode 100644
index 00000000..424534e2
--- /dev/null
+++ b/man/sbc_hist.Rd
@@ -0,0 +1,95 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sbc.R
+\name{sbc_hist}
+\alias{sbc_hist}
+\title{Diagnostics for Simulation Based Calibration}
+\usage{
+sbc_hist(ranks, ..., thin = 4, per_bin = 4, worst = 16,
+  prob = 0.99, facet_args = list())
+}
+\arguments{
+\item{ranks}{A list of matrices. See \strong{Details}.}
+
+\item{...}{Currently ignored.}
+
+\item{thin}{An integer indicating the thinning interval to use when plotting
+so that the histograms consist of (close to) independent realizations. Set
+the \code{thin} argument such that the resulting number of draws approximately
+matches the effective sample size.}
+
+\item{per_bin}{An integer indicating the number of histogram entries to
+combine into a single bar. For best results, one plus the number of draws
+from the posterior should be evenly divisible by the number of histogram
+bins after thinning. For example, if there are \code{511} posterior draws (per
+matrix in \code{ranks}) and \code{thin=4}, then after thinning there will be \code{128}
+draws. If \code{per_bin=4}, then \code{128/4=32} histogram bars will be drawn.}
+
+\item{worst}{How many parameters to show, or \code{NA} to plot all parameters. If
+\code{worst} is not \code{NA} then parameters are ordered by the degree of
+non-uniformity so, for example, \code{worst = 10} means to plot only the \code{10}
+worst parameters.}
+
+\item{prob}{The size of the interval plotted to show the expected behavior
+under uniformity. The default is \code{prob=0.99}.}
+
+\item{facet_args}{A named list of arguments (other than \code{facets}) passed
+to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}}
+to control faceting.}
+}
+\value{
+A ggplot object that can be further customized using the \strong{ggplot2} package.
+}
+\description{
+Diagnostics for Simulation Based Calibration
+}
+\details{
+Each element of the list \code{ranks} should be a matrix of rank comparison
+results (encoded as \code{0} or \code{1}) associated with a single draw from the prior
+distribution. (These are not actually "ranks" but can be used afterwards to
+reconstruct (thinned) ranks.) The columns of each matrix are model parameters
+and the rows are posterior draws from the model fit to the data generated
+from the corresponding prior draw. An element is \code{0} or \code{1} depending on
+whether the posterior draw is greater or less than the corresponding "true"
+realization (from the prior). The matrices should have column names to
+correctly label the parameters.
+}
+\section{Plot Descriptions}{
+
+\describe{
+\item{\code{sbc_hist()}}{
+SBC histogram from Talts et al. (2018). A separate plot (facet) is created
+for each parameter.
+}
+}
+}
+
+\examples{
+# create some fake inputs to use for sbc_hist()
+set.seed(19)
+pars <- paste0("beta[", 1:4, "]")
+samples_per_prior <- 511
+n_replications <- 500
+ranks <- list()
+for (n in 1:n_replications) {
+  r1 <- matrix(0, nrow=samples_per_prior, ncol=length(pars),
+               dimnames=list(NULL, pars))
+  for (p1 in 1:length(pars)) {
+    r1[sample.int(samples_per_prior, floor(runif(1, 0, samples_per_prior))), p1] <- 1
+  }
+  ranks[[n]] <- r1
+}
+
+color_scheme_set("purple")
+sbc_hist(ranks)
+sbc_hist(ranks, worst = NA) # uses original parameter ordering
+sbc_hist(ranks, worst = 3, facet_args = list(labeller = ggplot2::label_parsed))
+
+}
+\references{
+Talts, S., Betancourt, M., Simpson, D., Vehtari, A., and Gelman, A. (2018).
+Validating Bayesian Inference Algorithms with Simulation-Based Calibration.
+arXiv preprint arXiv:1804.06788. \url{https://arxiv.org/abs/1804.06788}
+}
+\seealso{
+\code{rstan::sbc()}
+}
diff --git a/tests/testthat/test-sbc.R b/tests/testthat/test-sbc.R
new file mode 100644
index 00000000..ccd27965
--- /dev/null
+++ b/tests/testthat/test-sbc.R
@@ -0,0 +1,44 @@
+library(bayesplot)
+context("SBC")
+
+set.seed(19)
+pars <- paste0("beta[", 1:2, "]")
+samples_per_prior <- 511
+n_replications <- 50
+ranks <- list()
+for (n in 1:n_replications) {
+  r1 <- matrix(0, nrow=samples_per_prior, ncol=length(pars), dimnames=list(NULL, pars))
+  for (p1 in 1:length(pars)) {
+    r1[sample.int(samples_per_prior, floor(runif(1, 0, samples_per_prior))), p1] <- 1
+  }
+  ranks[[n]] <- r1
+}
+set.seed(NULL)
+
+test_that("sbc_hist returns a ggplot object", {
+  expect_gg(sbc_hist(ranks, worst = NA))
+
+  g <- sbc_hist(ranks)
+  expect_gg(g)
+  expect_equal(nlevels(g$data$Parameter), ncol(ranks[[1]]))
+
+  g2 <- sbc_hist(ranks, worst = 1)
+  expect_gg(g2)
+  expect_equal(nlevels(g2$data$Parameter), 1)
+})
+
+test_that("sbc_hist throws correct warnings and errors", {
+  expect_warning(sbc_hist(ranks, per_bin = 27),
+                 "does not evenly divide the number of samples per prior")
+  expect_warning(sbc_hist(ranks, thin = 3),
+                 "does not evenly divide the number of samples per prior")
+
+  ranks2 <- ranks
+  ranks2[[1]] <- ranks[[1]][, 1, drop=FALSE]
+  expect_error(sbc_hist(ranks2), "Not all matrices in 'ranks' have the same dimensions")
+
+  ranks2[[1]] <- ranks[[1]][, 1, drop=TRUE]
+  expect_error(sbc_hist(ranks2), "all(sapply(ranks, is.matrix)) is not TRUE", fixed = TRUE)
+
+  expect_error(sbc_hist(ranks[[1]]), "is.list(ranks) is not TRUE", fixed=TRUE)
+})