update paper reference

README.md

@@ -3,8 +3,8 @@
 Benchmarking framework for generating high-dimensional synthetic drifted data and evaluating
 models.
 
-The corresponding open-access paper, [Edgar Wolf and Tobias Windisch (2024), A method to benchmark high-dimensional 
-process drift detection](https://arxiv.org/abs/2409.03669), describes the
+The corresponding open-access paper, [Edgar Wolf and Tobias Windisch (2025), A method to benchmark high-dimensional 
+process drift detection](https://link.springer.com/article/10.1007/s10845-025-02590-9), describes the
 method in detail.
 
 To run the benchmarks, execute:
@@ -46,3 +46,18 @@ def plot_benchmark(df):
 
 df = pd.read_json('benchmarks.json') 
 fig = plot_benchmark(df)
+```
+
+## Citation
+Please cite [**driftbench**](https://github.com/edgarWolf/driftbench) if you use this framework in your publications:
+```bibtex
+@article{wolf_method_2025,
+	title = {A method to benchmark high-dimensional process drift detection},
+	issn = {1572-8145},
+	url = {https://doi.org/10.1007/s10845-025-02590-9},
+	doi = {10.1007/s10845-025-02590-9},
+	journal = {Journal of Intelligent Manufacturing},
+	author = {Wolf, Edgar and Windisch, Tobias},
+	year = {2025},
+}
+```

VERSION

@@ -1 +1 @@
-0.0.8
+0.0.9