Added years to references of codes

PyLops · Feb 29, 2024 · 1754c3d · 1754c3d
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diff --git a/joss/paper.bib b/joss/paper.bib
@@ -104,31 +104,36 @@ @article{Ravasi:2021
     url = {https://www.earthdoc.org/content/papers/10.3997/2214-4609.2021612003}
 }
 
-@online{Chierchia,
-  author = {G. Chierchia and E. Chouzenoux and P. L. Combettes and J.-C. Pesquet},
-  title = {The Proximity Operator Repository},
-  url = {https://proximity-operator.net/},
-}
-
 @online{Maheswaranathan,
+  year = 2019,
   author = {N. Maheswaranathan and S. Zapp and B. Poole},
   title = {proxalgs},
   url = {https://github.com/ganguli-lab/proxalgs/},
 }
 
 @online{Melchior,
+  year = 2022,
   author = {P. Melchior and F. Moolekamp},
   title = {proxmin},
   url = {https://github.com/pmelchior/proxmin/},
 }
 
+@online{Chierchia,
+  year = 2024,
+  author = {G. Chierchia and E. Chouzenoux and P. L. Combettes and J.-C. Pesquet},
+  title = {The Proximity Operator Repository},
+  url = {https://proximity-operator.net/},
+}
+
 @online{Kashani,
+  year = 2024,
   author = {S. Kashani and M. Simeoni et al.},
   title = {pyxu},
   url = {https://github.com/pyxu-org/pyxu/},
 }
 
 @online{Chan,
+  year = 2024,
   author = {A. Chan and S. Diamond et al.},
   title = {ProxImaL},
   url = {https://github.com/comp-imaging/ProxImaL/},

diff --git a/joss/paper.md b/joss/paper.md
@@ -62,7 +62,7 @@ with state-of-the-art algorithms already provided in the library.
 
 Several projects in the Python ecosystem provide implementations of proximal operators and/or algorithms, 
 which present some overlap with those available in `PyProximal`. A (possibly not exhaustive) list of other projects is composed of
-*The Proximity Operator Repository* [@Chierchia], *proxalgs* [@Maheswaranathan], *proxmin* [@Melchior], *ProxImaL* [@Chan], 
+*proxalgs* [@Maheswaranathan], *proxmin* [@Melchior], *The Proximity Operator Repository* [@Chierchia], *ProxImaL* [@Chan], 
 and *pyxu* [@Kashani]. A key common feature of all of the above mentioned packages is to be self-contained; as such, not only proximal operators and solvers
 are provided, but also linear operators that are useful for the applications that the package targets. On the other hand, `PyProximal` heavily relies on and seamlessly integrates with `PyLops` [@Ravasi:2020], a Python library for matrix-free linear algebra 
 and optimization. As such, it can seamlessy handle problems with millions of unknowns and inherits