diff --git a/src/stan/analyze/mcmc/mcse.hpp b/src/stan/analyze/mcmc/mcse.hpp
index 9bd9aa8341..5584e9a7d7 100644
--- a/src/stan/analyze/mcmc/mcse.hpp
+++ b/src/stan/analyze/mcmc/mcse.hpp
@@ -11,44 +11,43 @@
 namespace stan {
 namespace analyze {
 
-
-  /**
-   * Computes the mean Monte Carlo error estimate for the central 90% interval.
-   * See https://arxiv.org/abs/1903.08008, section 4.4.
-   * Follows implementation in the R posterior package.
-   *
-   * @param chains matrix of draws across all chains
-   * @return mcse
-   */  
-  inline double mcse_mean(const Eigen::MatrixXd& chains) {
-    const Eigen::Index num_draws = chains.rows();
-    if (chains.rows() < 4
-	|| !is_finite_and_varies(chains))
-      return std::numeric_limits<double>::quiet_NaN();
-
-    double sd = (chains.array() - chains.mean()).square().sum() / (chains.size() - 1);
-    return std::sqrt(sd / ess(chains));
-  }
-
- /**
-   * Computes the standard deviation of the Monte Carlo error estimate
-   * https://arxiv.org/abs/1903.08008, section 4.4.
-   * Follows implementation in the R posterior package:
-   * https://github.com/stan-dev/posterior/blob/98bf52329d68f3307ac4ecaaea659276ee1de8df/R/convergence.R#L478-L496
-   *
-   * @param chains matrix of draws across all chains
-   * @return mcse
-   */ 
-  inline double mcse_sd(const Eigen::MatrixXd& chains) {
-    if (chains.rows() < 4
-	|| !is_finite_and_varies(chains))
-      return std::numeric_limits<double>::quiet_NaN();
-
-    Eigen::MatrixXd diffs = (chains.array() - chains.mean()).matrix();
-    double Evar = diffs.array().square().mean();
-    double varvar = (math::mean(diffs.array().pow(4) - Evar * Evar)) / ess(diffs.array().abs().matrix());
-    return std::sqrt(varvar / Evar / 4);
-  }
+/**
+ * Computes the mean Monte Carlo error estimate for the central 90% interval.
+ * See https://arxiv.org/abs/1903.08008, section 4.4.
+ * Follows implementation in the R posterior package.
+ *
+ * @param chains matrix of draws across all chains
+ * @return mcse
+ */
+inline double mcse_mean(const Eigen::MatrixXd& chains) {
+  const Eigen::Index num_draws = chains.rows();
+  if (chains.rows() < 4 || !is_finite_and_varies(chains))
+    return std::numeric_limits<double>::quiet_NaN();
+
+  double sd
+      = (chains.array() - chains.mean()).square().sum() / (chains.size() - 1);
+  return std::sqrt(sd / ess(chains));
+}
+
+/**
+ * Computes the standard deviation of the Monte Carlo error estimate
+ * https://arxiv.org/abs/1903.08008, section 4.4.
+ * Follows implementation in the R posterior package:
+ * https://github.com/stan-dev/posterior/blob/98bf52329d68f3307ac4ecaaea659276ee1de8df/R/convergence.R#L478-L496
+ *
+ * @param chains matrix of draws across all chains
+ * @return mcse
+ */
+inline double mcse_sd(const Eigen::MatrixXd& chains) {
+  if (chains.rows() < 4 || !is_finite_and_varies(chains))
+    return std::numeric_limits<double>::quiet_NaN();
+
+  Eigen::MatrixXd diffs = (chains.array() - chains.mean()).matrix();
+  double Evar = diffs.array().square().mean();
+  double varvar = (math::mean(diffs.array().pow(4) - Evar * Evar))
+                  / ess(diffs.array().abs().matrix());
+  return std::sqrt(varvar / Evar / 4);
+}
 
 }  // namespace analyze
 }  // namespace stan