diff --git a/inst/examples/evaluateRM_help.R b/inst/examples/evaluateRM_help.R
new file mode 100644
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+++ b/inst/examples/evaluateRM_help.R
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+# EcoRM evaluation at different resolutions (>4min runtime)
+root.dir  <- list.files(system.file(package = "gbif.range"), pattern = "extdata", full.names = TRUE)
+
+res1km <- evaluateRM(root.dir = root.dir, 
+           valData.dir = "SDM", 
+           ecoRM.dir = "EcoRM",
+           verbose = TRUE, 
+           print.map = TRUE,
+           valData.type = "TIFF", 
+           mask = NULL, 
+           res.fact = NULL)
+
+res10km <- evaluateRM(root.dir = root.dir, 
+                   valData.dir = "SDM", 
+                   ecoRM.dir = "EcoRM",
+                   verbose = TRUE, 
+                   print.map = TRUE,
+                   valData.type = "TIFF", 
+                   mask = NULL, 
+                   res.fact = 10)
+
+
+# Extract and plot a specific overlay map
+terra::plot(
+  res10km$overlay_list[[1]],
+  col = c("gray", "red", "blue", "purple"),
+  breaks = c(-0.5, 0.5, 1.5, 2.5, 3.5),
+  legend = FALSE,
+  main = paste0("Species: ", res10km$df_eval[1,1],"\n", 
+                "Sensitivity = ", round(res10km$df_eval[1,6], digits = 2)," & ", 
+                "Precision = ", round(res10km$df_eval[1,7], digits = 2)),
+  las = 1
+)
+
+legend("bottomright",
+       legend = c(
+         "Abs in both",
+         "Pres in ecoRM only (FP)",
+         "Pres in valRM only (FA)",
+         "Pres in both (TP)"
+       ),
+       fill = c("gray", "red", "blue", "purple"),
+       bg = NA,
+       box.col = NA,
+       inset = c(0,0.2)
+)
+
+## Compare sensitivity and precision outputs ##
+# Calculate overall performance (e.g. mean sensitivity & precision)
+res1km$df_eval$Mean_SenPrec <- (res1km$df_eval$Sen_ecoRM+res1km$df_eval$Prec_ecoRM)/2 
+res10km$df_eval$Mean_SenPrec <- (res10km$df_eval$Sen_ecoRM+res10km$df_eval$Prec_ecoRM)/2 
+
+# Combine the data frames and add a resolution column
+combined_df <- bind_rows(
+  res1km$df_eval %>% mutate(Resolution = "1km"),
+  res10km$df_eval %>% mutate(Resolution = "10km")
+
+)
+
+# Gather data into long format
+long_df <- combined_df %>%
+  select(Sen_ecoRM, Prec_ecoRM, Mean_SenPrec, Resolution) %>%
+  pivot_longer(cols = c(Sen_ecoRM, Prec_ecoRM, Mean_SenPrec), 
+               names_to = "Variable", values_to = "Value")
+
+## Plot results for different range data resolutions ##
+# Sensitivity increases and precision decreases with increasing resolution
+# Overall performance is slightly higher at more coarse grain
+ggplot(long_df, aes(x = Variable, y = Value, fill = Resolution)) +
+  geom_boxplot() +
+  labs(x = "Variable", y = "Value", title = "Boxplot of Sen_ecoRM and Prec_ecoRM") +
+  scale_fill_manual(values = c("10km" = "#619CFF", "1km" = "#FFC300")) +
+  theme_minimal()