Add JOSS paper to README and CITATION.cff

CITATION.cff

@@ -0,0 +1,11 @@
+cff-version: 1.2.0
+message: "If you use this software, please it as below."
+authors:
+- family-names: "Bosch"
+  given-names: "Nathanael"
+  orcid: "https://orcid.org/0000-0003-0139-4622"
+title: 'ProbNumDiffEq.jl: Probabilistic Numerical Solvers for Ordinary Differential Equations in Julia'
+version: 0.16.2
+doi: 10.21105/joss.07048
+date-released: '2024-10-01'
+url: https://doi.org/10.21105/joss.07048

README.md

@@ -5,6 +5,7 @@
 [![Build Status](https://github.com/nathanaelbosch/ProbNumDiffEq.jl/workflows/CI/badge.svg)](https://github.com/nathanaelbosch/ProbNumDiffEq.jl/actions)
 [![Coverage](https://codecov.io/gh/nathanaelbosch/ProbNumDiffEq.jl/branch/main/graph/badge.svg?token=eufIemCGXn)](https://codecov.io/gh/nathanaelbosch/ProbNumDiffEq.jl)
 [![Benchmarks](http://img.shields.io/badge/benchmarks-docs-blueviolet.svg)](https://nathanaelbosch.github.io/ProbNumDiffEq.jl/dev/benchmarks/multi-language-wrappers/)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.07048/status.svg)](https://doi.org/10.21105/joss.07048)
 
 ![Banner](./examples/banner.svg?raw=true)
 
@@ -75,6 +76,26 @@ If there is anything that's unclear or doesn't work, we would very much like to
 This includes not just bugs and feature requests but also general questions about the software, feedback and suggestions.
 
 
+## Citing ProbNumDiffEq.jl
+
+If you use ProbNumDiffEq.jl helpful for your research project, please cite our JOSS paper ([link](https://doi.org/10.21105/joss.07048)):
+```
+@article{Bosch2024,
+  doi =          {10.21105/joss.07048},
+  url =          {https://doi.org/10.21105/joss.07048},
+  year =         2024,
+  publisher =    {The Open Journal},
+  volume =       9,
+  number =       101,
+  pages =        7048,
+  author =       {Nathanael Bosch},
+  title =        {ProbNumDiffEq.jl: Probabilistic Numerical Solvers for Ordinary
+                  Differential Equations in Julia},
+  journal =      {Journal of Open Source Software}
+}
+```
+
+
 ## Related packages
 
 - [ProbDiffEq](https://pnkraemer.github.io/probdiffeq/) is similar in scope to ProbNumDiffEq.jl and it provides fast and feature-rich probabilistic ODE solvers but is implemented in Python and built on JAX.