diff --git a/R/log_transform.R b/R/log_transform.R
index eb83319..d44464b 100644
--- a/R/log_transform.R
+++ b/R/log_transform.R
@@ -3,24 +3,38 @@
 #' @param estimate Point estimates on the original response scale
 #' @param std.error Standard error of the point estimates on the original response scale
 #' @param sim Number of simulations to run when generating distribution to sample from
-#'
 #' @return A dataframe containing the `mean_log`, `std.error_log`, `lower` and `upper` 95% CI on the log scale
 #' @export
-#'
 #' @examples log_transform(1.77, 1.01, 1000)
-log_transform <- function(estimate, std.error, sim) {
+#' @import dplyr
+#' @importFrom cli cli_alert_danger cli_alert_success
+#' @import purrr
+#' @seealso Equivalent in workflow data hierarchy to [Z_VZ_preds()].
+log_transform <- function(estimate = numeric(1L), 
+                          std.error = numeric(1L), 
+                          sim = 10000L) {
+  
   simulated <- rnorm(sim, estimate, std.error)
   log_simulated <- suppressWarnings(log(simulated)) %>%
     na.omit()
+  
   m_est <- mean(log_simulated)
   std.error_est <- sd(log_simulated)
   quantiles <- quantile(log_simulated, c(0.025, 0.975), na.rm = TRUE)
-  std.error <- data.frame(mean_log = m_est, std.error_log = std.error_est, lower = quantiles[[1]], upper = quantiles[[2]])
-  if (flatten_dbl(std.error) %>%
-    map_lgl(.f = ~ is.na(.x) | is.nan(.x) | is.infinite(.x)) %>%
-    any()) {
+  
+  out <- data.frame(mean_log = m_est, 
+                    std.error_log = std.error_est, 
+                    lower = quantiles[[1]], 
+                    upper = quantiles[[2]])
+  
+  if (flatten_dbl(out) %>%
+      map_lgl(.f = ~ is.na(.x) | is.nan(.x) | is.infinite(.x)) %>%
+      any()) {
+    
     cli::cli_alert_danger("{.val NA}, {.val Inf} or {.val NaN} returned during back-transformation of effect sizes and standard errors.")
   }
+  
   cli::cli_alert_success("Log-transformed out-of-sample predictions, using {.val {sim}} simulations.")
-  return(std.error)
+  
+  return(out)
 }