add citation to JOSS paper

docs/source/index.rst

@@ -21,6 +21,31 @@ The library is developed by Wes Holliday (http://wesholliday.net) and Eric Pacui
 Related resources
 ------------------------
 
+If you would like to acknowledge our work in a scientific paper,
+please use the following citation:
+
+Wesley H. Holliday and Eric Pacuit (2025). pref_voting: The Preferential Voting Tools package for Python. Journal of Open Source Software, 10(105), 7020. https://doi.org/10.21105/joss.07020
+
+**Bibtex***:
+
+```bibtex
+@article{HollidayPacuit2025, 
+  author = {Wesley H. Holliday and Eric Pacuit}, 
+  title = {pref_voting: The Preferential Voting Tools package for Python}, 
+  journal = {Journal of Open Source Software},
+  year = {2025}, 
+  publisher = {The Open Journal}, 
+  volume = {10}, 
+  number = {105}, 
+  pages = {7020}, 
+  doi = {10.21105/joss.07020}
+}
+
+```
+
+Related resources
+------------------------
+
 - VoteKit (https://votekit.readthedocs.io/) - A Python package developed by the MGGG Redistricting Lab (https://mggg.org/) designed to facilitate the study of different election methods.
 
 - prefsampling (https://comsoc-community.github.io/prefsampling/) - A Python library for sampling from preference profiles with respect to different probability models.

pref_voting/utility_functions.py

@@ -71,18 +71,19 @@ def city_block_utility(v_pos: float32[:], c_pos: float32[:]):
     return -distance.cityblock(v_pos, c_pos)
 
 @jit(nopython=True, fastmath=True)
-def shepsle_utility(v_pos: float32[:], c_pos: float32[:]):
+def shepsle_utility(v_pos: float32[:], c_pos: float32[:], kappa: float32 = 1):
     """
     The Shepsle utility function from "The Strategy of Ambiguity: Uncertainty and Electoral Competition" by Kenneth A. Shepsle, American Political Science Review, 1972, vol. 66, issue 2, pp. 555-568.   For a justification of this utility function, see Appendix B from *Making Multicandidate Elections More Democratic* (https://doi.org/10.1515/9781400859504.114) by S. Merrill III. 
 
     Args:
         v_pos (numpy array): The position(s) of the voter.
-        c_pos (numpy array): The position(s) of the candidate.  
+        c_pos (numpy array): The position(s) of the candidate. 
+        kappa (float): A parameter that determines the steepness of the utility function. 
     Returns:    
         float: The utility of the candidate to the voter.      
     """
     d = np.linalg.norm(v_pos - c_pos)
-    return np.exp(-d**2 / 2)
+    return np.exp((kappa**2 * -d**2) / 2)
 
 
 @jit(nopython=True, fastmath=True)