regenerate doc and reindent lines

R/E_loo.R

@@ -47,12 +47,12 @@
 #'   calling `E_loo()`, the smoothed log-weights are used to estimate
 #'   Pareto-k's, which may produce optimistic estimates.
 #'
-#'   For mean, var, and sd the returned Pareto-k is maximum of
-#'   Pareto-k's for left and right tail of \eqn{hr} and right tail of
-#'   \eqn{r}, where \eqn{h=x} in case of mean and \eqn{h=x^2} in case
-#'   of var and sd, and \eqn{r} is the importance ratio. For quantile
-#'   the returned Pareto-k is the Pareto-k for the right tail of
-#'   \eqn{r}.
+#'   For mean, var, and sd the returned Pareto-k is the maximum of the
+#'   Pareto-k's for the left and right tail of \eqn{hr} and the right tail of
+#'   \eqn{r}, where \eqn{r} is the importance ratio and \eqn{h=x} for
+#'   `type="mean"` and \eqn{h=x^2} for `type="var"` and `type="sd"`. For
+#'   `type="quantile"`, the returned Pareto-k is the Pareto-k for the right tail
+#'   of \eqn{r}.
 #'  }
 #' }
 #'
@@ -288,13 +288,13 @@ E_loo_khat.matrix <- function(x, psis_object, log_ratios, ...) {
 #' @return Scalar h-specific k-hat estimate.
 #'
 .E_loo_khat_i <- function(x_i, log_ratios_i, tail_len_i) {
-    h_theta <- x_i
-    r_theta <- exp(log_ratios_i - max(log_ratios_i))
-    khat_r <- posterior::pareto_khat(r_theta, tail="right", ndraws_tail = tail_len_i)$khat
-    if (is.null(x_i)) {
-      khat_r
-    } else {
-      khat_hr <- posterior::pareto_khat(h_theta*r_theta, tail="both", ndraws_tail = tail_len_i)$khat
+  h_theta <- x_i
+  r_theta <- exp(log_ratios_i - max(log_ratios_i))
+  khat_r <- posterior::pareto_khat(r_theta, tail="right", ndraws_tail = tail_len_i)$khat
+  if (is.null(x_i)) {
+    khat_r
+  } else {
+    khat_hr <- posterior::pareto_khat(h_theta*r_theta, tail="both", ndraws_tail = tail_len_i)$khat
     max(khat_hr, khat_r)
-    }
+  }
 }