diff --git a/rstan/rstan/R/loo.R b/rstan/rstan/R/loo.R
index 2be5668e2..5e4df9c36 100644
--- a/rstan/rstan/R/loo.R
+++ b/rstan/rstan/R/loo.R
@@ -1,10 +1,10 @@
 # Leave-one-out cross-validation
-# 
+#
 # The \code{loo} method for stanfit objects ---a wrapper around the
 # \code{loo.array} method from the \pkg{loo} package--- computes approximate
 # leave-one-out cross-validation using Pareto smoothed importance sampling
 # (PSIS-LOO CV).
-# 
+#
 # @param x stanfit object
 # @param pars Name of parameter, transformed parameter, or generated quantity in
 #   the Stan program corresponding to the pointwise log-likelihood. If not
@@ -20,8 +20,9 @@
 # @param k_threshold Threshold value for Pareto k values above which
 #   the moment matching algorithm is used. If \code{moment_match} is \code{FALSE},
 #   this is ignored.
+# @param r_eff Whether to compute r_eff to pass to loo package.
 # @param ... Ignored.
-# 
+#
 # @details Stan does not automatically compute and store the log-likelihood. It
 #   is up to the user to incorporate it into the Stan program if it is to be
 #   extracted after fitting the model. In a Stan model, the pointwise log
@@ -47,31 +48,37 @@ loo.stanfit <-
            cores = getOption("mc.cores", 1),
            moment_match = FALSE,
            k_threshold = 0.7,
+           r_eff = FALSE,
            ...) {
     stopifnot(length(pars) == 1L)
     stopifnot(is.logical(save_psis))
     stopifnot(is.logical(moment_match))
     stopifnot(is.numeric(k_threshold))
-    
+    stopifnot(is.logical(r_eff))
+
     LLarray <- loo::extract_log_lik(stanfit = x,
                                     parameter_name = pars,
                                     merge_chains = FALSE)
-    r_eff <- loo::relative_eff(x = exp(LLarray), cores = cores)
-    
+
+    if (!r_eff) {
+      r_eff <- NULL
+    } else {
+      r_eff <- loo::relative_eff(x = exp(LLarray), cores = cores)
+    }
+
     if (moment_match) {
       loo <- suppressWarnings(loo::loo.array(LLarray,
                                              r_eff = r_eff,
                                              cores = cores,
                                              save_psis = save_psis))
-      
+
       x_array <- as.array(x)
       chain_id <- rep(seq(dim(x_array)[2]),each = dim(x_array)[1])
       loo <- loo_moment_match.stanfit(
         x, loo = loo, chain_id = chain_id, k_threshold = k_threshold,
         cores = cores, parameter_name = pars, ...
       )
-    }
-    else {
+    } else {
       loo <- loo::loo.array(LLarray,
                             r_eff = r_eff,
                             cores = cores,
diff --git a/rstan/rstan/man/stanfit-method-loo.Rd b/rstan/rstan/man/stanfit-method-loo.Rd
index e11837cad..973baae8e 100644
--- a/rstan/rstan/man/stanfit-method-loo.Rd
+++ b/rstan/rstan/man/stanfit-method-loo.Rd
@@ -20,6 +20,7 @@ sampling (Vehtari, Gelman, and Gabry, 2017a,2017b).
     cores = getOption("mc.cores", 1),
     moment_match = FALSE,
     k_threshold = 0.7,
+    r_eff = FALSE,
     \dots)
 }
 
@@ -43,6 +44,14 @@ sampling (Vehtari, Gelman, and Gabry, 2017a,2017b).
   \item{k_threshold}{Threshold value for Pareto k values above which
   the moment matching algorithm is used. If \code{moment_match} is \code{FALSE},
   this is ignored.}
+  \item{r_eff}{\code{TRUE} or \code{FALSE} indicating whether to compute the
+  \code{r_eff} argument to pass to the \pkg{loo} package. If \code{TRUE},
+  will call \code{loo::relative_eff()}. If \code{FALSE}
+  (the default), we avoid computing \code{r_eff}, which can be very slow.
+  \code{r_eff} measures the amount of autocorrelation in MCMC draws, and is
+  used to compute more accurate ESS and MCSE estimates for pointwise and
+  total ELPDs. When \code{r_eff=FALSE}, the reported ESS and MCSE estimates
+  may be over-optimistic if the posterior draws are far from independent.}
   \item{\dots}{Ignored.}
 }