diff --git a/R/analyze.R b/R/analyze.R
index 64feb80..fd74edc 100644
--- a/R/analyze.R
+++ b/R/analyze.R
@@ -33,18 +33,15 @@ analyze <- function(preset, progress = NULL) {
     #  - `score` Score for the gene between 0.0 and 1.0.
     methods <- list(
         "clusteriness" = clusteriness,
-        "correlation" = correlation,
-        "proximity" = proximity,
-        "neural" = neural,
         "clusteriness_positions" = function(...) {
             clusteriness(..., use_positions = TRUE)
         },
+        "correlation" = correlation,
         "correlation_positions" = function(...) {
             correlation(..., use_positions = TRUE)
         },
-        "proximity_positions" = function(...) {
-            proximity(..., use_positions = TRUE)
-        },
+        "proximity" = proximity,
+        "neural" = neural,
         "neural_positions" = function(...) {
             neural(..., use_positions = TRUE)
         }
diff --git a/R/proximity.R b/R/proximity.R
index ed91660..ec9e325 100644
--- a/R/proximity.R
+++ b/R/proximity.R
@@ -2,24 +2,18 @@
 #
 # A score will be given to each gene such that 0.0 corresponds to the maximal
 # mean distance across all genes and 1.0 corresponds to a distance of 0.
-proximity <- function(preset, use_positions = FALSE, progress = NULL) {
+proximity <- function(preset, progress = NULL) {
     species_ids <- preset$species_ids
     gene_ids <- preset$gene_ids
 
-    cached("proximity", c(species_ids, gene_ids, use_positions), {
+    cached("proximity", c(species_ids, gene_ids), {
         # Prefilter distances by species and gene.
         data <- geposan::distances[
             species %chin% preset$species_ids & gene %chin% preset$gene_ids
         ]
 
         # Compute the score as described above.
-
-        data <- if (use_positions) {
-            data[, .(mean_distance = mean(position)), by = "gene"]
-        } else {
-            data[, .(mean_distance = mean(distance)), by = "gene"]
-        }
-
+        data <- data[, .(mean_distance = mean(distance)), by = "gene"]
         max_distance <- data[, max(mean_distance)]
         data[, score := 1 - mean_distance / max_distance]