diff --git a/.github/.gitignore b/.github/.gitignore
new file mode 100644
index 0000000..2d19fc7
--- /dev/null
+++ b/.github/.gitignore
@@ -0,0 +1 @@
+*.html
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
new file mode 100644
index 0000000..ed7650c
--- /dev/null
+++ b/.github/workflows/pkgdown.yaml
@@ -0,0 +1,48 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/github-pages-deploy-action@v4.4.1
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index b0594be..852c439 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   bartools_quickstart: bartools_quickstart.html
-last_built: 2023-11-02T03:56Z
+last_built: 2023-11-05T11:37Z
 urls:
   reference: https://danevass.github.io/bartools/reference
   article: https://danevass.github.io/bartools/articles
diff --git a/vignettes/bartools_quickstart.Rmd b/vignettes/bartools_quickstart.Rmd
index 8e6cc94..4a51507 100644
--- a/vignettes/bartools_quickstart.Rmd
+++ b/vignettes/bartools_quickstart.Rmd
@@ -255,20 +255,19 @@ Here, barcodes/tags are represented by bubbles aligned on a single plane.
 The size of the bubbles reflects the percentage abundance of each barcode within a sample.
 
 ```{r}
-plotBarcodeBubble(dge.filtered.collapsed$counts, 
-                  proportion.cutoff = 10, 
-                  labels = T)
+plotBarcodeBubble(counts = dge.filtered.collapsed$counts, 
+                  proportionCutoff = 10, 
+                  labelBarcodes = T)
 ```
 
 Using the `orderSample` parameter, bubbleplots can also be arranged according to frequency in a particular sample which can help with visual comparison of large vs small clones across samples and conditions. 
 
 ```{r}
-plotOrderedBubble(counts.obj = dge.filtered.collapsed$counts, 
-                  proportion.cutoff = 10, 
-                  labels = T, 
+plotOrderedBubble(counts = dge.filtered.collapsed$counts, 
+                  proportionCutoff = 10, 
+                  labelBarcodes = T, 
                   orderSample = "T0", 
                   colorDominant = F, 
-                  filterLow = T, 
                   samples = dge.filtered.collapsed$samples, 
                   group = "Treatment")
 ```
@@ -276,26 +275,24 @@ plotOrderedBubble(counts.obj = dge.filtered.collapsed$counts,
 Barcodes that fail to meet a defined abundance threshold in any sample can be greyed out.
 
 ```{r}
-plotOrderedBubble(counts.obj = dge.filtered.collapsed$counts, 
-                  proportion.cutoff = 10, 
-                  labels = T, 
+plotOrderedBubble(counts = dge.filtered.collapsed$counts, 
+                  proportionCutoff = 10, 
+                  labelBarcodes = T, 
                   orderSample = "T0", 
                   colorDominant = T, 
-                  filterLow = T, 
                   samples = dge.filtered.collapsed$samples, 
                   group = "Treatment")
 ```
 
-Or filtered from the plot entirely using `filterLow` and `filter.cutoff` parameters
+Or filtered from the plot entirely using the `filterCutoff` parameter
 
 ```{r}
-plotOrderedBubble(counts.obj = dge.filtered.collapsed$counts, 
-                  proportion.cutoff = 10, 
-                  labels = T, 
+plotOrderedBubble(counts = dge.filtered.collapsed$counts, 
+                  proportionCutoff = 10, 
+                  labelBarcodes = T, 
                   orderSample = "T0", 
                   colorDominant = T, 
-                  filterLow = T, 
-                  filter.cutoff = 0.01,
+                  filterCutoff = 0.01,
                   samples = dge.filtered.collapsed$samples, 
                   group = "Treatment")
 ```
@@ -305,7 +302,7 @@ plotOrderedBubble(counts.obj = dge.filtered.collapsed$counts,
 Alternatively, we can focus in on the most abundant barcodes within a set of samples to more easily observe how these change in frequency over the course of an experiment.
 
 ```{r}
-plotBarcodeHistogram(dge.filtered.collapsed$counts,
+plotBarcodeHistogram(counts.obj = dge.filtered.collapsed$counts,
                      sample = dge.filtered.collapsed$samples$group[[10]],
                      top = 50)
 ```
@@ -316,7 +313,7 @@ For timecourse experiments it is useful to visualise how the kinetics of barcode
 In this instance we can use `plotBarcodeTimeseries` to get an idea of the relative abundance of the top *n* barcodes in a sample relative to others.
 
 ```{r}
-plotBarcodeTimeseries(dge.filtered.collapsed, top = 5)
+plotBarcodeTimeseries(counts.obj = dge.filtered.collapsed, top = 5)
 ```
 
 ### Principal Components Analysis
@@ -324,10 +321,8 @@ plotBarcodeTimeseries(dge.filtered.collapsed, top = 5)
 A global level PCA analysis is a good way to get a high level understanding of the similarities and differences between samples.
 
 ```{r}
-plotBarcodePCA(
-  dge.filtered.collapsed[, dge.filtered.collapsed$samples$Treatment %in% 
-                           c("T0", "Vehicle", "High_dose")], 
-  intgroup = "Treatment")
+subset <- dge.filtered.collapsed[, dge.filtered.collapsed$samples$Treatment %in% c("T0", "Vehicle", "High_dose")]
+plotBarcodePCA(object = subset, intgroup = "Treatment")
 ```
 
 ### Heatmaps