Documentation/187 minor comments on joss paper text

paper.bib

@@ -4,7 +4,7 @@ @article{scikit-learn
               and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
               and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
               Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
-    journal = {J. Mach. Learn. Res.},
+    journal = {Journal of Machine Learning Research},
     volume = {12},
     pages = {2825--2830},
     year = {2011},
@@ -29,7 +29,7 @@ @article{pykoopman
 @inproceedings{dlkoopman,
     author = {Sourya Dey and Eric William Davis},
     title = {{DLKoopman: A deep learning software package for Koopman theory}},
-    booktitle = {Proc. Mach. Learn. Res.},
+    booktitle = {Proceedings of Machine Learning Research},
     pages = {1467--1479},
     volume = {211},
     publisher = {{PMLR}},
@@ -53,13 +53,13 @@ @article{dahdah_system_2022
     author = {Steven Dahdah and James R. Forbes},
     title = {System norm regularization methods for {Koopman} operator
              approximation},
-    journal = {Proc. R. Soc. A},
+    journal = {Proceedings of the Royal Society A},
 }
 
 @article{dahdah_2024_closed-loop,
     title = {Closed-loop {Koopman} operator approximation},
     author = {Dahdah, Steven and Forbes, James Richard},
-    journal = {Mach. Learn.: Sci. Technol.},
+    journal = {Machine Learning: Science and Technology},
     volume = {5},
     number = {2},
     pages = {025038},
@@ -89,9 +89,13 @@ @article{lortie_2024_asymptotically
     title = {Asymptotically Stable Data-Driven {Koopman} Operator Approximation
         with Inputs using Total Extended {DMD}},
     author = {Lortie, Louis and Forbes, James Richard},
-    year = {2024},
-    journaltitle = {{\tt arXiv:2408.16846v1[eess.SY]}},
-    doi = {10.48550/arXiv.2408.16846},
+    journal = {Machine Learning: Science and Technology},
+    volume = {6},
+    number = {1},
+    pages = {015003},
+    year = {2025},
+    publisher = {{IOP} Publishing},
+    doi={10.1088/2632-2153/ada33b},
 }
 
 @misc{pykoop,

paper.md

@@ -40,8 +40,6 @@ controller or observer synthesis for a wide range of systems using
 well-established linear tools. `pykoop`'s documentation, along with examples in
 script and notebook form, can be found at at
 [pykoop.readthedocs.io/en/stable](https://pykoop.readthedocs.io/en/stable/).
-Its source code and issue tracker are available at
-[github.com/decargroup/pykoop](https://github.com/decargroup/pykoop).
 Its releases are also archived on Zenodo [@pykoop].
 
 # Statement of need
@@ -82,10 +80,10 @@ exogenous inputs are not.
 
 # Scholarly publications using `pykoop`
 
-The Koopman operator regression methods proposed in [@dahdah_system_2022] have
+The Koopman operator regression methods proposed in @dahdah_system_2022 have
 been implemented within `pykoop`, while the methods proposed in
-[@dahdah_2024_closed-loop], [@lortie_2024_forward-backward],
-[@lortie_2024_asymptotically], and [@dahdah_2024_uncertainty] are all based on
+@dahdah_2024_closed-loop, @lortie_2024_forward-backward,
+@lortie_2024_asymptotically, and @dahdah_2024_uncertainty are all based on
 `pykoop`, but are implemented in their own repositories.
 
 # Acknowledgements