diff --git a/.Rbuildignore b/.Rbuildignore
index b7d3e72..c57d8d6 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -5,3 +5,6 @@
 ^\.github$
 ^data-raw$
 ^vignettes/prerender_figures\.R$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
diff --git a/.gitignore b/.gitignore
index 0126ae9..90cd220 100644
--- a/.gitignore
+++ b/.gitignore
@@ -52,3 +52,4 @@ rsconnect/
 .DS_Store
 .quarto
 inst/doc
+docs
diff --git a/DESCRIPTION b/DESCRIPTION
index ce290a4..177895e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -62,4 +62,5 @@ Depends:
 LazyData: true
 LazyDataCompression: xz
 VignetteBuilder: knitr
+URL: https://bigmindlab.github.io/OmicsKit
 
diff --git a/NAMESPACE b/NAMESPACE
index d7bc4fc..44d21fc 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,9 +2,9 @@
 
 export(add_annotations)
 export(addgenesPA)
-export(calc_jaccard)
 export(detect_filter)
 export(do_clust)
+export(geneset_similarity)
 export(get_annotations)
 export(get_network_communities)
 export(get_stars)
diff --git a/R/dataclust_PA.R b/R/dataclust_PA.R
index f3fb3bb..ff04feb 100644
--- a/R/dataclust_PA.R
+++ b/R/dataclust_PA.R
@@ -8,7 +8,7 @@
 #' apoptosis & cell death, cell cycle & DNA damage, immune response &
 #' inflammation, and metabolism. Gene set names follow standard database
 #' conventions (`KEGG_`, `HALLMARK_`, `GO_`) and gene symbols are real human
-#' genes. Designed to be used as input to [calc_jaccard()].
+#' genes. Designed to be used as input to [geneset_similarity()].
 #'
 #' @format A named list of 40 elements. Each element is a character vector of
 #'   human gene symbols (HGNC) belonging to that gene set. Gene set sizes range
@@ -26,11 +26,11 @@
 #' # Inspect one gene set
 #' geneset_list[["KEGG_APOPTOSIS"]]
 #'
-#' # Use with calc_jaccard()
+#' # Use with geneset_similarity()
 #' data(camera_results)
-#' jac <- calc_jaccard(geneset_list, camera_results, fdr_th = 0.05)
+#' jac <- geneset_similarity(geneset_list, camera_results, fdr_th = 0.05)
 #'
-#' @seealso [calc_jaccard()], [camera_results]
+#' @seealso [geneset_similarity()], [camera_results]
 "geneset_list"
 
 
@@ -40,7 +40,7 @@
 #' analysis, containing significance values for the 40 gene sets in
 #' [geneset_list]. Approximately 60% of gene sets have FDR < 0.05, providing
 #' enough significant sets for meaningful clustering. Designed to be used
-#' alongside [geneset_list] as input to [calc_jaccard()].
+#' alongside [geneset_list] as input to [geneset_similarity()].
 #'
 #' @format A data frame with 40 rows and 4 columns:
 #'   \describe{
@@ -63,9 +63,9 @@
 #' # How many gene sets are significant?
 #' sum(camera_results$FDR < 0.05)
 #'
-#' # Use with calc_jaccard()
+#' # Use with geneset_similarity()
 #' data(geneset_list)
-#' jac <- calc_jaccard(geneset_list, camera_results, fdr_th = 0.05)
+#' jac <- geneset_similarity(geneset_list, camera_results, fdr_th = 0.05)
 #'
-#' @seealso [calc_jaccard()], [geneset_list]
+#' @seealso [geneset_similarity()], [geneset_list]
 "camera_results"
diff --git a/R/doclust_PA.R b/R/doclust_PA.R
index ba3e7db..485e190 100644
--- a/R/doclust_PA.R
+++ b/R/doclust_PA.R
@@ -4,14 +4,14 @@
 # community detection, and super-term generation.
 #
 # Functions:
-#   calc_jaccard            — Compute Jaccard similarity & distance matrices
+#   geneset_similarity            — Compute Jaccard similarity & distance matrices
 #   do_clust                — Hierarchical clustering with silhouette selection
 #   get_superterm           — TF-IDF super-term labels for gene set communities
 #   get_network_communities — Community detection + super-terms in one call
 # =============================================================================
 
 ########################
-# Function calc_jaccard #
+# Function geneset_similarity #
 ########################
 
 #' Compute Jaccard similarity and distance matrices for gene sets
@@ -51,7 +51,7 @@
 #' )
 #'
 #' # Only the first three gene sets pass the FDR threshold
-#' jac <- calc_jaccard(geneset_list, results, fdr_th = 0.05)
+#' jac <- geneset_similarity(geneset_list, results, fdr_th = 0.05)
 #'
 #' jac$jaccard_sim   # similarity matrix
 #' jac$dist_mat      # distance object (usable in UMAP, clustering, etc.)
@@ -63,7 +63,7 @@
 #' @importFrom rlang .data
 #' @export
 
-calc_jaccard <- function(geneset_list, results, fdr_th = 0.05) {
+geneset_similarity <- function(geneset_list, results, fdr_th = 0.05) {
 
   if (!is.list(geneset_list) || is.null(names(geneset_list))) {
     stop("`geneset_list` must be a named list of character vectors.", call. = FALSE)
@@ -136,7 +136,7 @@ calc_jaccard <- function(geneset_list, results, fdr_th = 0.05) {
 #' returns cluster assignments, a silhouette ggplot2 object, and a
 #' ComplexHeatmap with dendrogram.
 #'
-#' @param x A `JaccardResult` object (output of [calc_jaccard()]) or an
+#' @param x A `JaccardResult` object (output of [geneset_similarity()]) or an
 #'   object of class `dist`.
 #' @param method Agglomeration method passed to [stats::hclust()].
 #'   Default: `"ward.D2"`.
@@ -168,7 +168,7 @@ calc_jaccard <- function(geneset_list, results, fdr_th = 0.05) {
 #'   FDR     = c(0.01, 0.02, 0.03, 0.04, 0.01)
 #' )
 #'
-#' jac   <- calc_jaccard(geneset_list, results)
+#' jac   <- geneset_similarity(geneset_list, results)
 #' clust <- do_clust(jac)
 #'
 #' clust$silhouette_plot               # ggplot2 silhouette curve
@@ -177,7 +177,7 @@ calc_jaccard <- function(geneset_list, results, fdr_th = 0.05) {
 #' clust$cluster_assignments           # tibble: NAME | cluster
 #' }
 #'
-#' @seealso [calc_jaccard()], [get_network_communities()],
+#' @seealso [geneset_similarity()], [get_network_communities()],
 #'   [network_clust()], [network_clust_gg()]
 #' @import ggplot2
 #' @importFrom rlang .data
@@ -204,7 +204,7 @@ do_clust <- function(x, method = "ward.D2", max_k = NULL) {
     jaccard_sim <- 1 - as.matrix(x)
   } else {
     stop(
-      "`x` must be a `JaccardResult` object (output of `calc_jaccard()`) ",
+      "`x` must be a `JaccardResult` object (output of `geneset_similarity()`) ",
       "or an object of class `dist`.",
       call. = FALSE
     )
@@ -471,10 +471,10 @@ get_superterm <- function(geneset_names, community_membership,
 #' Convenience wrapper that builds a binary adjacency network from a Jaccard
 #' similarity matrix, runs a community-detection algorithm, and optionally
 #' generates super-term labels for each community via [get_superterm()].
-#' Designed to be the single step between [calc_jaccard()] and the network
+#' Designed to be the single step between [geneset_similarity()] and the network
 #' plotting functions [network_clust()] / [network_clust_gg()].
 #'
-#' @param x A `JaccardResult` object (output of [calc_jaccard()]).
+#' @param x A `JaccardResult` object (output of [geneset_similarity()]).
 #' @param threshold Numeric between 0 and 1. Gene set pairs with a Jaccard
 #'   similarity above this value are connected in the network. Default: `0.3`.
 #' @param method Character. Community detection algorithm to use. One of:
@@ -506,7 +506,7 @@ get_superterm <- function(geneset_names, community_membership,
 #' res <- read.csv("path/to/results.csv")
 #'
 #' # Full workflow
-#' jac   <- calc_jaccard(gsl, res, fdr_th = 0.05)
+#' jac   <- geneset_similarity(gsl, res, fdr_th = 0.05)
 #' clust <- do_clust(jac)
 #' net   <- get_network_communities(jac, threshold = 0.3, method = "louvain")
 #'
@@ -524,7 +524,7 @@ get_superterm <- function(geneset_names, community_membership,
 #' plots$combined
 #' }
 #'
-#' @seealso [calc_jaccard()], [do_clust()], [get_superterm()],
+#' @seealso [geneset_similarity()], [do_clust()], [get_superterm()],
 #'   [network_clust()], [network_clust_gg()]
 #' @importFrom magrittr %>%
 #' @export
@@ -542,7 +542,7 @@ get_network_communities <- function(x,
   }
   if (!inherits(x, "JaccardResult")) {
     stop(
-      "`x` must be a `JaccardResult` object (output of `calc_jaccard()`).",
+      "`x` must be a `JaccardResult` object (output of `geneset_similarity()`).",
       call. = FALSE
     )
   }
diff --git a/R/list_gmts.R b/R/list_gmts.R
index c5f5844..1368364 100644
--- a/R/list_gmts.R
+++ b/R/list_gmts.R
@@ -6,7 +6,7 @@
 #'
 #' Scans a directory for `.gmt` files, parses them, and returns a single named
 #' list where each element is a character vector of gene symbols for one gene
-#' set. The output is ready to be passed directly to [calc_jaccard()].
+#' set. The output is ready to be passed directly to [geneset_similarity()].
 #'
 #' **GMT format:** each row contains the gene set name in column 1, an optional
 #' description in column 2, and gene symbols from column 3 onward. Empty fields
@@ -31,11 +31,11 @@
 #' names(geneset_list)[1:5]          # first five gene set names
 #' geneset_list[["KEGG_APOPTOSIS"]]  # genes in a specific set
 #'
-#' # Pass directly to calc_jaccard
-#' jac <- calc_jaccard(geneset_list, results_df, fdr_th = 0.05)
+#' # Pass directly to geneset_similarity
+#' jac <- geneset_similarity(geneset_list, results_df, fdr_th = 0.05)
 #' }
 #'
-#' @seealso [calc_jaccard()]
+#' @seealso [geneset_similarity()]
 #' @export
 
 list_gmts <- function(dir) {
diff --git a/R/plotclust_PA.R b/R/plotclust_PA.R
index d66a5f5..ebd333c 100644
--- a/R/plotclust_PA.R
+++ b/R/plotclust_PA.R
@@ -22,7 +22,7 @@
 #' For a ggplot2-based version that returns plot objects instead of drawing
 #' them, see [network_clust_gg()].
 #'
-#' @param x A `JaccardResult` object (output of [calc_jaccard()]).
+#' @param x A `JaccardResult` object (output of [geneset_similarity()]).
 #' @param clust_result A list returned by [do_clust()], used to color nodes by
 #'   hierarchical cluster assignment.
 #' @param jaccard_threshold Numeric. Minimum Jaccard similarity required for an
@@ -61,7 +61,7 @@
 #' gsl <- list_gmts("path/to/gmt_folder/")
 #' res <- read.csv("path/to/results.csv")
 #'
-#' jac   <- calc_jaccard(gsl, res, fdr_th = 0.05)
+#' jac   <- geneset_similarity(gsl, res, fdr_th = 0.05)
 #' clust <- do_clust(jac)
 #' net   <- get_network_communities(jac, threshold = 0.3)
 #'
@@ -89,7 +89,7 @@
 #' dev.off()
 #' }
 #'
-#' @seealso [calc_jaccard()], [do_clust()], [get_network_communities()],
+#' @seealso [geneset_similarity()], [do_clust()], [get_network_communities()],
 #'   [get_superterm()], [network_clust_gg()]
 #' @importFrom magrittr %>%
 #' @importFrom rlang .data
@@ -110,7 +110,7 @@ network_clust <- function(x, clust_result,
   # --- Input validation ---------------------------------------------------
   if (!inherits(x, "JaccardResult")) {
     stop(
-      "`x` must be a `JaccardResult` object (output of `calc_jaccard()`).",
+      "`x` must be a `JaccardResult` object (output of `geneset_similarity()`).",
       call. = FALSE
     )
   }
@@ -317,7 +317,7 @@ network_clust <- function(x, clust_result,
 #' For a base R igraph version that draws directly to the active graphics
 #' device, see [network_clust()].
 #'
-#' @param x A `JaccardResult` object (output of [calc_jaccard()]).
+#' @param x A `JaccardResult` object (output of [geneset_similarity()]).
 #' @param clust_result A list returned by [do_clust()], used to color nodes by
 #'   hierarchical cluster assignment.
 #' @param jaccard_threshold Numeric. Minimum Jaccard similarity required for an
@@ -356,7 +356,7 @@ network_clust <- function(x, clust_result,
 #' gsl <- list_gmts("path/to/gmt_folder/")
 #' res <- read.csv("path/to/results.csv")
 #'
-#' jac   <- calc_jaccard(gsl, res, fdr_th = 0.05)
+#' jac   <- geneset_similarity(gsl, res, fdr_th = 0.05)
 #' clust <- do_clust(jac)
 #' net   <- get_network_communities(jac, threshold = 0.3)
 #'
@@ -389,7 +389,7 @@ network_clust <- function(x, clust_result,
 #' plots$clean + plots$superterms
 #' }
 #'
-#' @seealso [calc_jaccard()], [do_clust()], [get_network_communities()],
+#' @seealso [geneset_similarity()], [do_clust()], [get_network_communities()],
 #'   [get_superterm()], [network_clust()]
 #' @import ggplot2
 #' @importFrom magrittr %>%
@@ -417,7 +417,7 @@ network_clust_gg <- function(x, clust_result,
   # --- Input validation ---------------------------------------------------
   if (!inherits(x, "JaccardResult")) {
     stop(
-      "`x` must be a `JaccardResult` object (output of `calc_jaccard()`).",
+      "`x` must be a `JaccardResult` object (output of `geneset_similarity()`).",
       call. = FALSE
     )
   }
diff --git a/_pkgdown.yml b/_pkgdown.yml
new file mode 100644
index 0000000..33e8d42
--- /dev/null
+++ b/_pkgdown.yml
@@ -0,0 +1,144 @@
+package: OmicsKit
+title: "OmicsKit: A bioinformatics toolset for multiomics analysis"
+url: https://danielgarbozo.github.io/OmicsKit
+
+navbar:
+  structure:
+    left:  [reference, articles]
+    right: [github]
+  components:
+    github:
+      icon: fab fa-github
+      aria-label: GitHub
+      href: https://github.com/BigMindLab/OmicsKit
+
+home:
+  title: "OmicsKit"
+  description: >
+    Streamlined utilities for multi-omics analysis and publication-ready visuals.
+    Covers DEA, pathway analysis, dimensionality reduction, and more.
+  links:
+    - text: DEA Workflow
+      href: articles/DEA_workflow.html
+    - text: Pathway Analysis Workflow
+      href: articles/PA_workflow.html
+    - text: PA Clustering Workflow
+      href: articles/PA_clustering.html
+  sidebar:
+    structure: [links, license, authors, dev]
+
+authors:
+  David Requena:
+    href: https://orcid.org/0000-0002-5968-1133
+  Daniel Guevara:
+    href: https://orcid.org/0009-0001-2786-8729
+  Daniel Garbozo:
+    href: https://orcid.org/0009-0003-2495-6568
+  Angela Alarcon:
+    href: https://orcid.org/0000-0003-0293-5603
+
+
+footer:
+  structure:
+    left:  [developed_by]
+    right: [built_with]
+  components:
+    developed_by: |
+      Developed by the
+      <a href="https://github.com/BigMindLab">BigMind Lab</a>.
+      Contact: <a href="mailto:david.requena@nyulangone.org">david.requena@nyulangone.org</a>
+
+reference:
+  - title: "Differential Expression Analysis (DEA)"
+    desc: >
+      Functions for data quality control, normalization, dimensionality reduction,
+      annotation, and differential expression visualization.
+    contents:
+      - power_analysis
+      - tpm
+      - nice_PCA
+      - nice_UMAP
+      - nice_tSNE
+      - get_annotations
+      - add_annotations
+      - save_results
+      - split_cases
+      - nice_Volcano
+      - detect_filter
+      - get_stars
+      - nice_VSB
+      - nice_VSB_DEseq2
+
+  - title: "Genomics"
+    desc: "Survival analysis and genomics visualization utilities."
+    contents:
+      - nice_KM
+
+  - title: "Pathway Analysis (PA)"
+    desc: >
+      Tools for loading, merging, and visualizing GSEA / pathway analysis results,
+      including single- and multi-comparison plots and heatmaps.
+    contents:
+      - list_gmts
+      - merge_PA
+      - getgenesPA
+      - addgenesPA
+      - multiplot_PA
+      - splot_PA
+      - heatmap_PA
+      - heatmap_path_PA
+
+  - title: "PA Clustering"
+    desc: >
+      Pathway clustering via Jaccard similarity, hierarchical clustering,
+      and network-based community detection.
+    contents:
+      - geneset_similarity
+      - do_clust
+      - get_superterm
+      - get_network_communities
+      - network_clust
+      - network_clust_gg
+  - title: "Example datasets"
+    desc: "Built-in datasets for reproducible examples and vignettes."
+    contents:
+      - camera_results
+      - deseq2_results
+      - geneset_list
+      - gsea_results
+      - norm_counts
+      - raw_counts
+      - sampledata
+      - vst_counts
+
+articles:
+  - title: "Workflows"
+    desc: "End-to-end analysis examples with OmicsKit."
+    contents:
+      - DEA_workflow
+      - PA_workflow
+      - PA_clustering
+
+template:
+  bootstrap: 5
+  bootswatch: cosmo
+  bslib:
+    primary:    "#6000C6"
+    secondary:  "#B3A3FF"
+    body-bg:    "#FFFFFF"
+    navbar-bg:  "#6000C6"
+    navbar-fg:  "#FFFFFF"
+    link-color: "#6000C6"
+    pre-bg:     "#F7F7FB"
+  includes:
+    in_header: |
+      <style>
+        a.navbar-brand { font-weight: 700; }
+        .pkgdown-footer { font-size: 0.85rem; }
+      </style>
+
+development:
+  mode: release
+
+toc:
+  depth: 2
diff --git a/data-raw/example_PA.R b/data-raw/example_PA.R
index 2108929..e8c0ace 100644
--- a/data-raw/example_PA.R
+++ b/data-raw/example_PA.R
@@ -6,7 +6,7 @@
 # geneset_list
 # A named list of 40 curated gene sets with realistic KEGG / HALLMARK / GO
 # naming conventions and real human gene symbols, grouped into four biological
-# themes so that calc_jaccard() + do_clust() + get_network_communities()
+# themes so that geneset_similarity() + do_clust() + get_network_communities()
 # produce meaningful clustering results.
 #
 # Themes:
diff --git a/man/camera_results.Rd b/man/camera_results.Rd
index b374a08..c1635c2 100644
--- a/man/camera_results.Rd
+++ b/man/camera_results.Rd
@@ -26,7 +26,7 @@ A data frame simulating the output of a CAMERA differential expression
 analysis, containing significance values for the 40 gene sets in
 \link{geneset_list}. Approximately 60\% of gene sets have FDR < 0.05, providing
 enough significant sets for meaningful clustering. Designed to be used
-alongside \link{geneset_list} as input to \code{\link[=calc_jaccard]{calc_jaccard()}}.
+alongside \link{geneset_list} as input to \code{\link[=geneset_similarity]{geneset_similarity()}}.
 }
 \examples{
 data(camera_results)
@@ -37,12 +37,12 @@ head(camera_results)
 # How many gene sets are significant?
 sum(camera_results$FDR < 0.05)
 
-# Use with calc_jaccard()
+# Use with geneset_similarity()
 data(geneset_list)
-jac <- calc_jaccard(geneset_list, camera_results, fdr_th = 0.05)
+jac <- geneset_similarity(geneset_list, camera_results, fdr_th = 0.05)
 
 }
 \seealso{
-\code{\link[=calc_jaccard]{calc_jaccard()}}, \link{geneset_list}
+\code{\link[=geneset_similarity]{geneset_similarity()}}, \link{geneset_list}
 }
 \keyword{datasets}
diff --git a/man/do_clust.Rd b/man/do_clust.Rd
index 2be3419..fb97bb8 100644
--- a/man/do_clust.Rd
+++ b/man/do_clust.Rd
@@ -7,7 +7,7 @@
 do_clust(x, method = "ward.D2", max_k = NULL)
 }
 \arguments{
-\item{x}{A \code{JaccardResult} object (output of \code{\link[=calc_jaccard]{calc_jaccard()}}) or an
+\item{x}{A \code{JaccardResult} object (output of \code{\link[=geneset_similarity]{geneset_similarity()}}) or an
 object of class \code{dist}.}
 
 \item{method}{Agglomeration method passed to \code{\link[stats:hclust]{stats::hclust()}}.
@@ -50,7 +50,7 @@ results <- data.frame(
   FDR     = c(0.01, 0.02, 0.03, 0.04, 0.01)
 )
 
-jac   <- calc_jaccard(geneset_list, results)
+jac   <- geneset_similarity(geneset_list, results)
 clust <- do_clust(jac)
 
 clust$silhouette_plot               # ggplot2 silhouette curve
@@ -61,6 +61,6 @@ clust$cluster_assignments           # tibble: NAME | cluster
 
 }
 \seealso{
-\code{\link[=calc_jaccard]{calc_jaccard()}}, \code{\link[=get_network_communities]{get_network_communities()}},
+\code{\link[=geneset_similarity]{geneset_similarity()}}, \code{\link[=get_network_communities]{get_network_communities()}},
 \code{\link[=network_clust]{network_clust()}}, \code{\link[=network_clust_gg]{network_clust_gg()}}
 }
diff --git a/man/geneset_list.Rd b/man/geneset_list.Rd
index 01f45a1..00ae84d 100644
--- a/man/geneset_list.Rd
+++ b/man/geneset_list.Rd
@@ -21,7 +21,7 @@ A named list of 40 curated gene sets spanning four biological themes:
 apoptosis & cell death, cell cycle & DNA damage, immune response &
 inflammation, and metabolism. Gene set names follow standard database
 conventions (\code{KEGG_}, \code{HALLMARK_}, \code{GO_}) and gene symbols are real human
-genes. Designed to be used as input to \code{\link[=calc_jaccard]{calc_jaccard()}}.
+genes. Designed to be used as input to \code{\link[=geneset_similarity]{geneset_similarity()}}.
 }
 \examples{
 data(geneset_list)
@@ -32,12 +32,12 @@ length(geneset_list)
 # Inspect one gene set
 geneset_list[["KEGG_APOPTOSIS"]]
 
-# Use with calc_jaccard()
+# Use with geneset_similarity()
 data(camera_results)
-jac <- calc_jaccard(geneset_list, camera_results, fdr_th = 0.05)
+jac <- geneset_similarity(geneset_list, camera_results, fdr_th = 0.05)
 
 }
 \seealso{
-\code{\link[=calc_jaccard]{calc_jaccard()}}, \link{camera_results}
+\code{\link[=geneset_similarity]{geneset_similarity()}}, \link{camera_results}
 }
 \keyword{datasets}
diff --git a/man/calc_jaccard.Rd b/man/geneset_similarity.Rd
similarity index 93%
rename from man/calc_jaccard.Rd
rename to man/geneset_similarity.Rd
index d6c54e3..573aabd 100644
--- a/man/calc_jaccard.Rd
+++ b/man/geneset_similarity.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/doclust_PA.R
-\name{calc_jaccard}
-\alias{calc_jaccard}
+\name{geneset_similarity}
+\alias{geneset_similarity}
 \title{Compute Jaccard similarity and distance matrices for gene sets}
 \usage{
-calc_jaccard(geneset_list, results, fdr_th = 0.05)
+geneset_similarity(geneset_list, results, fdr_th = 0.05)
 }
 \arguments{
 \item{geneset_list}{A named list where each element is a character vector of
@@ -48,7 +48,7 @@ results <- data.frame(
 )
 
 # Only the first three gene sets pass the FDR threshold
-jac <- calc_jaccard(geneset_list, results, fdr_th = 0.05)
+jac <- geneset_similarity(geneset_list, results, fdr_th = 0.05)
 
 jac$jaccard_sim   # similarity matrix
 jac$dist_mat      # distance object (usable in UMAP, clustering, etc.)
diff --git a/man/get_network_communities.Rd b/man/get_network_communities.Rd
index 9e3813d..effc3eb 100644
--- a/man/get_network_communities.Rd
+++ b/man/get_network_communities.Rd
@@ -15,7 +15,7 @@ get_network_communities(
 )
 }
 \arguments{
-\item{x}{A \code{JaccardResult} object (output of \code{\link[=calc_jaccard]{calc_jaccard()}}).}
+\item{x}{A \code{JaccardResult} object (output of \code{\link[=geneset_similarity]{geneset_similarity()}}).}
 
 \item{threshold}{Numeric between 0 and 1. Gene set pairs with a Jaccard
 similarity above this value are connected in the network. Default: \code{0.3}.}
@@ -56,7 +56,7 @@ A named list with four elements:
 Convenience wrapper that builds a binary adjacency network from a Jaccard
 similarity matrix, runs a community-detection algorithm, and optionally
 generates super-term labels for each community via \code{\link[=get_superterm]{get_superterm()}}.
-Designed to be the single step between \code{\link[=calc_jaccard]{calc_jaccard()}} and the network
+Designed to be the single step between \code{\link[=geneset_similarity]{geneset_similarity()}} and the network
 plotting functions \code{\link[=network_clust]{network_clust()}} / \code{\link[=network_clust_gg]{network_clust_gg()}}.
 }
 \examples{
@@ -65,7 +65,7 @@ gsl <- list_gmts("path/to/gmt_folder/")
 res <- read.csv("path/to/results.csv")
 
 # Full workflow
-jac   <- calc_jaccard(gsl, res, fdr_th = 0.05)
+jac   <- geneset_similarity(gsl, res, fdr_th = 0.05)
 clust <- do_clust(jac)
 net   <- get_network_communities(jac, threshold = 0.3, method = "louvain")
 
@@ -85,6 +85,6 @@ plots$combined
 
 }
 \seealso{
-\code{\link[=calc_jaccard]{calc_jaccard()}}, \code{\link[=do_clust]{do_clust()}}, \code{\link[=get_superterm]{get_superterm()}},
+\code{\link[=geneset_similarity]{geneset_similarity()}}, \code{\link[=do_clust]{do_clust()}}, \code{\link[=get_superterm]{get_superterm()}},
 \code{\link[=network_clust]{network_clust()}}, \code{\link[=network_clust_gg]{network_clust_gg()}}
 }
diff --git a/man/list_gmts.Rd b/man/list_gmts.Rd
index de832ee..77ddc64 100644
--- a/man/list_gmts.Rd
+++ b/man/list_gmts.Rd
@@ -19,7 +19,7 @@ last occurrence overwrites the earlier one.
 \description{
 Scans a directory for \code{.gmt} files, parses them, and returns a single named
 list where each element is a character vector of gene symbols for one gene
-set. The output is ready to be passed directly to \code{\link[=calc_jaccard]{calc_jaccard()}}.
+set. The output is ready to be passed directly to \code{\link[=geneset_similarity]{geneset_similarity()}}.
 }
 \details{
 \strong{GMT format:} each row contains the gene set name in column 1, an optional
@@ -37,11 +37,11 @@ length(geneset_list)              # number of gene sets
 names(geneset_list)[1:5]          # first five gene set names
 geneset_list[["KEGG_APOPTOSIS"]]  # genes in a specific set
 
-# Pass directly to calc_jaccard
-jac <- calc_jaccard(geneset_list, results_df, fdr_th = 0.05)
+# Pass directly to geneset_similarity
+jac <- geneset_similarity(geneset_list, results_df, fdr_th = 0.05)
 }
 
 }
 \seealso{
-\code{\link[=calc_jaccard]{calc_jaccard()}}
+\code{\link[=geneset_similarity]{geneset_similarity()}}
 }
diff --git a/man/network_clust.Rd b/man/network_clust.Rd
index cb85245..8931d94 100644
--- a/man/network_clust.Rd
+++ b/man/network_clust.Rd
@@ -16,7 +16,7 @@ network_clust(
 )
 }
 \arguments{
-\item{x}{A \code{JaccardResult} object (output of \code{\link[=calc_jaccard]{calc_jaccard()}}).}
+\item{x}{A \code{JaccardResult} object (output of \code{\link[=geneset_similarity]{geneset_similarity()}}).}
 
 \item{clust_result}{A list returned by \code{\link[=do_clust]{do_clust()}}, used to color nodes by
 hierarchical cluster assignment.}
@@ -78,7 +78,7 @@ them, see \code{\link[=network_clust_gg]{network_clust_gg()}}.
 gsl <- list_gmts("path/to/gmt_folder/")
 res <- read.csv("path/to/results.csv")
 
-jac   <- calc_jaccard(gsl, res, fdr_th = 0.05)
+jac   <- geneset_similarity(gsl, res, fdr_th = 0.05)
 clust <- do_clust(jac)
 net   <- get_network_communities(jac, threshold = 0.3)
 
@@ -108,6 +108,6 @@ dev.off()
 
 }
 \seealso{
-\code{\link[=calc_jaccard]{calc_jaccard()}}, \code{\link[=do_clust]{do_clust()}}, \code{\link[=get_network_communities]{get_network_communities()}},
+\code{\link[=geneset_similarity]{geneset_similarity()}}, \code{\link[=do_clust]{do_clust()}}, \code{\link[=get_network_communities]{get_network_communities()}},
 \code{\link[=get_superterm]{get_superterm()}}, \code{\link[=network_clust_gg]{network_clust_gg()}}
 }
diff --git a/man/network_clust_gg.Rd b/man/network_clust_gg.Rd
index 3f6bf93..165e8de 100644
--- a/man/network_clust_gg.Rd
+++ b/man/network_clust_gg.Rd
@@ -16,7 +16,7 @@ network_clust_gg(
 )
 }
 \arguments{
-\item{x}{A \code{JaccardResult} object (output of \code{\link[=calc_jaccard]{calc_jaccard()}}).}
+\item{x}{A \code{JaccardResult} object (output of \code{\link[=geneset_similarity]{geneset_similarity()}}).}
 
 \item{clust_result}{A list returned by \code{\link[=do_clust]{do_clust()}}, used to color nodes by
 hierarchical cluster assignment.}
@@ -81,7 +81,7 @@ device, see \code{\link[=network_clust]{network_clust()}}.
 gsl <- list_gmts("path/to/gmt_folder/")
 res <- read.csv("path/to/results.csv")
 
-jac   <- calc_jaccard(gsl, res, fdr_th = 0.05)
+jac   <- geneset_similarity(gsl, res, fdr_th = 0.05)
 clust <- do_clust(jac)
 net   <- get_network_communities(jac, threshold = 0.3)
 
@@ -116,6 +116,6 @@ plots$clean + plots$superterms
 
 }
 \seealso{
-\code{\link[=calc_jaccard]{calc_jaccard()}}, \code{\link[=do_clust]{do_clust()}}, \code{\link[=get_network_communities]{get_network_communities()}},
+\code{\link[=geneset_similarity]{geneset_similarity()}}, \code{\link[=do_clust]{do_clust()}}, \code{\link[=get_network_communities]{get_network_communities()}},
 \code{\link[=get_superterm]{get_superterm()}}, \code{\link[=network_clust]{network_clust()}}
 }
diff --git a/vignettes/pathway_analysis_clustering.Rmd b/vignettes/PA_clustering.Rmd
similarity index 97%
rename from vignettes/pathway_analysis_clustering.Rmd
rename to vignettes/PA_clustering.Rmd
index a79dee0..cb48f83 100644
--- a/vignettes/pathway_analysis_clustering.Rmd
+++ b/vignettes/PA_clustering.Rmd
@@ -27,7 +27,7 @@ vignette demonstrates how to use OmicsKit's pathway clustering functions to:
 
 1. **Load gene sets** from GMT files with `list_gmts()`
 2. **Quantify redundancy** between gene sets using Jaccard similarity with
-   `calc_jaccard()`
+   `geneset_similarity()`
 3. **Cluster** redundant gene sets hierarchically with `do_clust()`
 4. **Detect communities** in the gene set network and generate interpretable
    labels with `get_network_communities()`
@@ -77,7 +77,7 @@ sum(camera_results$FDR < 0.05)
 
 ## Step 2 — Jaccard similarity matrix
 
-`calc_jaccard()` filters the gene sets by FDR threshold and computes all
+`geneset_similarity()` filters the gene sets by FDR threshold and computes all
 pairwise Jaccard similarity coefficients:
 
 $$J(A, B) = \frac{|A \cap B|}{|A \cup B|}$$
@@ -85,8 +85,8 @@ $$J(A, B) = \frac{|A \cap B|}{|A \cup B|}$$
 A value of 1 means the two gene sets share identical gene membership; 0 means
 no overlap at all.
 
-```{r calc_jaccard}
-jac <- calc_jaccard(
+```{r geneset_similarity}
+jac <- geneset_similarity(
   geneset_list  = geneset_list,
   results       = camera_results,
   fdr_th        = 0.05
@@ -291,7 +291,7 @@ library(OmicsKit)
 gsl <- list_gmts("path/to/gmt_folder/")
 
 # 2. Jaccard similarity (filter by FDR < 0.05)
-jac <- calc_jaccard(gsl, camera_results, fdr_th = 0.05)
+jac <- geneset_similarity(gsl, camera_results, fdr_th = 0.05)
 
 # 3. Hierarchical clustering
 clust <- do_clust(jac)