(#1) Add initial State of the field subsection and add citations for some related packages

Author: jbytecode

paper/paper.bib

@@ -30,4 +30,20 @@ @article{HiGHS
   year={2018},
   doi={10.1007/s12532-017-0130-5},
   publisher={Springer}
+}
+
+@misc{gurobi-optimods,
+  author = {{Gurobi Optimization, LLC}},
+  title = {{Gurobi OptiMods: Data-driven APIs for common optimization tasks}},
+  howpublished = {\url{https://github.com/Gurobi/gurobi-optimods}},
+  year = {2023},
+  note = {Accessed: 2025-08-12}
+}
+
+
+@misc{Graphs2021,
+  author       = {Fairbanks, James and Besan{\c{c}}on, Mathieu and Simon, Sch{\"o}lly and Hoffiman, J{\'u}lio and Eubank, Nick and Karpinski, Stefan},
+  title        = {JuliaGraphs/Graphs.jl: an optimized graphs package for the Julia programming language},
+  year         = 2021,
+  url = {https://github.com/JuliaGraphs/Graphs.jl/}
 }

paper/paper.md

@@ -22,6 +22,10 @@ bibliography: paper.bib
 
 `OperationsResearchModels.jl` is a Julia package [@julia] that offers comprehensive implementations for numerous topics typically covered in an Operations Research (OR) curriculum. Its primary objective during development was to serve academic and pedagogical purposes, providing a clear and accessible platform for learning and applying OR concepts. While not optimized for high-performance computing, the package leverages JuMP for its underlying mathematical modeling, which inherently provides a reasonable level of computational efficiency. This design allows the package to deliver a suite of functions that solve classical operations research problems with remarkable ease and consistency, simplifying the process for students and researchers alike.
 
+# State of the field
+
+Gurobi OptiMods is an open-source Python package that provides pre-implemented optimization use cases built on the Gurobi solver. Each module includes comprehensive documentation detailing its application and the underlying mathematical model [@gurobi-optimods]. Julia's `Graphs.jl` [Graphs2021] package provides efficient methods for important network analysis topics such as minimal spanning tree and the shortest path. 
+
 # Statement of Need
 
 JuMP [@JuMP] provides an excellent interface and macros for uniformly accessing optimizer functionality. Any mathematical optimization problem can be assembled with three core components: the objective function (`@objective`), variable definitions (`@variable`), and constraints (`@constraints`). The researcher's role is to formulate the original problem as a mathematical optimization problem and then translate it using JuMP's macros.