Updated README with citations; added citation.bib

Author: PedroSeber

README.md

@@ -1,6 +1,6 @@
 # Smart Process Analytics
 
-Smart Process Analytics (SPA) is a Python software for predictive modeling. The original version is associated with the paper ["Smart process analytics for predictive modeling" by Weike Sun and Richard D. Braatz](https://doi.org/10.1016/j.compchemeng.2020.107134). Since 2022, it has been updated by Pedro Seber. This fork is different enough from the original version that it should be considered its own thing.
+Smart Process Analytics (SPA) is a Python software for predictive modeling. Since 2022, this version has been updated by Pedro Seber after being forked from [the original version](https://github.com/vickysun5/SmartProcessAnalytics). This fork is different enough from the original version that it should be considered its own thing.
 
 To run SPA on your computer, simply download the source code of the [most recent release](https://github.com/PedroSeber/SmartProcessAnalytics/releases). Unzip that folder somewhere convenient, open a terminal in that folder, (optionally) create a new conda environment or activate your conda environment of choice, and run `pip install -e .` (note the dot after -e). SPA should then be usable after `import SPA` and calling the `SPA.main_SPA()` function. If you are having issues installing ace-cream, comment its line out in the [setup.py](setup.py) file and try again. Most of SPA will work without ace-cream.
 
@@ -16,4 +16,12 @@ A typical run of [SPA.py](Code-SPA/SPA.py) automatically calls [cv\_final.py](Co
 
 The final result is stored in the `selected_model` and `fitting_result` variables returned by [SPA.py](Code-SPA/SPA.py). It is also saved as .json and .p files.
 
-Please contact Pedro Seber (pseber[at]mit{dot}edu) or Richard Braatz (braatz[at]mit{dot}edu) for any inquiries.
+## Citing
+This version of SPA has been the subject of multiple publications. If you have used SPA, please cite the following works (depending on what was used). Bibtex-formatted citations are available in [citation.bib](citation.bib).
+
+| Publication name | Please cite if... |
+| :----------: | :----: |
+| [LCEN: A Novel Feature Selection Algorithm for Nonlinear, Interpretable Machine Learning Models](https://arxiv.org/abs/2402.17120) | You used LCEN for any task.
+| [Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models](https://doi.org/10.1101/2024.08.27.609988) | You used MLPs or RNNs generated by SPA, even if not for a residual hybrid model.
+
+Please contact Pedro Seber (pseber[at]mit{dot}edu) and/or Richard Braatz (braatz[at]mit{dot}edu) for any inquiries.

citation.bib

@@ -0,0 +1,21 @@
+@misc{Seber-and-Braatz-2024-LCEN,
+      title={{LCEN}: A Novel Feature Selection Algorithm for Nonlinear, Interpretable Machine Learning Models},
+      author={Pedro Seber and Richard D. Braatz},
+      year={2024},
+      eprint={2402.17120},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2402.17120},
+}
+
+@article {Seber-and-Braatz-2024-Residual-Hybrid,
+	author = {Seber, Pedro and Braatz, Richard D.},
+	title = {Improving {N}-Glycosylation and Biopharmaceutical Production Predictions Using Auto{ML}-Built Residual Hybrid Models},
+	elocation-id = {2024.08.27.609988},
+	year = {2024},
+	doi = {10.1101/2024.08.27.609988},
+	publisher = {Cold Spring Harbor Laboratory},
+	URL = {https://www.biorxiv.org/content/early/2024/08/28/2024.08.27.609988},
+	eprint = {https://www.biorxiv.org/content/early/2024/08/28/2024.08.27.609988.full.pdf},
+	journal = {bioRxiv}
+}