-

Author: dirmeier

.github/workflows/draft.yaml

@@ -0,0 +1,20 @@
+ame: Draft PDF
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          path: paper/paper.pdf

paper/paper.md

@@ -49,7 +49,7 @@ While the main focus of the package is the implementation of recent algorithms t
 In addition, `sbijax` provides functionality for model diagnostics, posterior visualization and Markov Chain Monte Carlo (MCMC) sampling.
 The package uses the high-performance computing framework `JAX` as a backend [@jax2018github].
 Using `JAX` has several advantages, including a) that it uses the same syntax as `numpy` [@harris2020array] which enables a seamless transition for applied scientists who already are familiar with it,
-and b) that empirical evaluations have shown that `JAX` can be significantly faster than `PyTorch1 (see, e.g., @phan2019composable).
+and b) that empirical evaluations have shown that `JAX` can be significantly faster than `PyTorch` (see, e.g., @phan2019composable).
 
 | Model                                          | Class name | Reference                   |
 |------------------------------------------------|------------|-----------------------------|
@@ -88,4 +88,8 @@ are using `sbijax` for their experiments. @dirmeier2025causal proposed a novel f
 
 No GenAI or other AI tools have been used in writing the software or this manuscript.
 
+# Acknowledgements
+
+This research was supported by the Swiss National Science Foundation (Grant No. $200021_208249$).
+
 # References