Paramus Math
Computing Applications (HPC) MIT (Free)
About
Sensitivity analysis and data exploration via SALib: Sobol indices, Morris screening, FAST, and Delta-MIM. Includes t-SNE projection, Gower distance, and SHAP value attribution.
Citation
Herman, J.; Usher, W. SALib: An open-source Python library for sensitivity analysis. J. Open Source Software 2(9), 97 (2017). DOI:10.21105/joss.00097
Frequently Asked Questions
What is Paramus Math?
Paramus Math is a computing applications (hpc) application available in the Paramus App Store. Sensitivity analysis and data exploration via SALib: Sobol indices, Morris screening, FAST, and Delta-MIM. Includes t-SNE projection, Gower distance, and SHAP value attribution.
Is Paramus Math free to use?
Yes. Paramus Math is distributed under the MIT (Free) license and is available at no cost through the Paramus App Store.
How do I install Paramus Math?
Paramus Math is installed through Paramus Chemistry OS, an on-premise Windows platform for computational chemistry. Open the Paramus App Store in your local installation and select Paramus Math for one-click deployment.
What type of application is Paramus Math?
Paramus Math belongs to the “Computing Applications (HPC)” category in the Paramus App Store. It runs on Paramus Chemistry OS and can also be accessed through Paramus Cloud for supported workflows.
What platform does Paramus Math run on?
Paramus Math runs on Paramus Chemistry OS, a Windows-based on-premise platform that provides local compute power for demanding simulations. It requires a Paramus OS installation with appropriate hardware resources.
Can Paramus Math be automated or integrated with AI workflows?
Yes. Paramus Math is available as part of the Paramus ecosystem which supports MCP (Model Context Protocol) tools for AI-driven automation. This enables integration with large language models and automated research pipelines.
How should I cite Paramus Math in publications?
The recommended citation for Paramus Math is: Herman, J.; Usher, W. SALib: An open-source Python library for sensitivity analysis. J. Open Source Software 2(9), 97 (2017). DOI:10.21105/joss.00097