Technical report highlights a set of libraries for numerical processing, aiming to enhance scientific data handling.
This solution is a set of reusable .NET class libraries centered on numerical processing, scientific data handling, and parallel execution support. The main project, `INAF.Libraries.Net.Math`, provides algorithms and models for fitting, smoothing, clustering, contour extraction, and statistical analysis. It is supported by two utility libraries: `INAF.Libraries.Net.Extensions` for general-purpose extension methods and `INAF.Libraries.Net.Parallelization` for controlled parallel execution.The codebase targets `net10.0`, uses nullable reference types, and follows a modular structure organized by domain areas such as `Fit`, `Stats`, `Smoothing`, `Models`, and `Clustering`.
No takes yet. Share an insight, caveat, or question.
Francesco Carraro (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: