diff --git a/joss/figs/software.eps b/joss/figs/software.eps
new file mode 100644
index 0000000..4ed9d30
Binary files /dev/null and b/joss/figs/software.eps differ
diff --git a/joss/paper.bib b/joss/paper.bib
index f80fb26..71946e4 100644
--- a/joss/paper.bib
+++ b/joss/paper.bib
@@ -13,7 +13,8 @@ @book{Parikh:2013
   	Author = {N. Parikh},
   	Booktitle = {Proximal Algorithms},
   	Publisher = {Foundations and Trends in Optimization},
-  	Year = 2013
+  	Year = 2013,
+  	doi = {doi.org/10.1561/2400000003}
 }
 
 @book{Combettes:2011,
@@ -22,7 +23,8 @@ @book{Combettes:2011
   	Booktitle = {Fixed-Point Algorithms for Inverse Problems in Science and Engineering.},
   	Publisher = {Springer Optimization and Its Applications},
   	Title = {Proximal splitting methods in signal processing},
-  	Year = 2011
+  	Year = 2011,
+    doi = {10.1007/978-1-4419-9569-8_10},
 }
 
 @article{Boyd:2011,
@@ -111,11 +113,14 @@ @online{Maheswaranathan
   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/},
+@article{Melchior,
+    author = {F. Moolekamp and P. Melchior},
+    title = "{Block-simultaneous direction method of multipliers: a proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints}",
+    journal = {Optimization and Engineering},
+    year = 2018,
+    volume = 19,
+    doi = {10.1007/s11081-018-9380-y},
+    url = {https://link.springer.com/article/10.1007/s11081-018-9380-y}
 }
 
 @online{Chierchia,
@@ -125,18 +130,26 @@ @online{Chierchia
   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/},
+@software{pyxu-framework,
+  author       = {Matthieu Simeoni and
+                  Sepand Kashani and
+                  Joan Rué-Queralt and
+                  Pyxu Developers},
+  title        = {pyxu-org/pyxu: pyxu},
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.4486431},
+  url          = {https://doi.org/10.5281/zenodo.4486431}
+}
+
+@article{Heide:2016,
+author = {Heide, Felix and Diamond, Steven and Nie\ss{}ner, Matthias and Ragan-Kelley, Jonathan and Heidrich, Wolfgang and Wetzstein, Gordon},
+title = {ProxImaL: efficient image optimization using proximal algorithms},
+year = {2016},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+volume = {35},
+number = {4},
+doi = {10.1145/2897824.2925875},
 }
 
 
diff --git a/joss/paper.md b/joss/paper.md
index 878e357..cf83627 100644
--- a/joss/paper.md
+++ b/joss/paper.md
@@ -62,8 +62,8 @@ 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
-*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
+*proxalgs* [@Maheswaranathan], *proxmin* [@Melchior], *The Proximity Operator Repository* [@Chierchia], *ProxImaL* [@Heide:2016], 
+and *pyxu* [@pyxu-framework]. 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. Moreover, to the best of our knowledge, all of these packages
 provide purely CPU-based implementations (apart from *pyxu*). 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 easily handle problems with millions of unknowns and inherits