Add JOSS badge/citation

README.md

@@ -5,7 +5,7 @@
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+[![DOI](https://joss.theoj.org/papers/10.21105/joss.05976/status.svg)](https://doi.org/10.21105/joss.05976)
 
 **quantile-forest** offers a Python implementation of quantile regression forests compatible with scikit-learn.
 
@@ -27,12 +27,12 @@ Usage
 -----
 
 ```python
-  from quantile_forest import RandomForestQuantileRegressor
-  from sklearn import datasets
-  X, y = datasets.fetch_california_housing(return_X_y=True)
-  qrf = RandomForestQuantileRegressor()
-  qrf.fit(X, y)
-  y_pred = qrf.predict(X, quantiles=[0.025, 0.5, 0.975])
+from quantile_forest import RandomForestQuantileRegressor
+from sklearn import datasets
+X, y = datasets.fetch_california_housing(return_X_y=True)
+qrf = RandomForestQuantileRegressor()
+qrf.fit(X, y)
+y_pred = qrf.predict(X, quantiles=[0.025, 0.5, 0.975])
 ```
 
 Documentation
@@ -45,3 +45,23 @@ References
 ----------
 
 <a id="1">[1]</a> N. Meinshausen, "Quantile Regression Forests", Journal of Machine Learning Research, 7(Jun), 983-999, 2006. http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf
+
+Citation
+--------
+
+If you use this package in academic work, please consider citing https://joss.theoj.org/papers/10.21105/joss.05976 as
+
+```bib
+@article{Johnson2024,
+    doi = {10.21105/joss.05976},
+    url = {https://doi.org/10.21105/joss.05976},
+    year = {2024},
+    publisher = {The Open Journal},
+    volume = {9},
+    number = {93},
+    pages = {5976},
+    author = {Reid A. Johnson},
+    title = {quantile-forest: A Python Package for Quantile Regression Forests},
+    journal = {Journal of Open Source Software}
+}
+```