Minor edit

Author: HadenSmith

paper/paper.md

@@ -57,7 +57,7 @@ Numerics fills this gap by providing domain-specific capabilities within .NET:
 
 # State of the Field
 
-General-purpose .NET numerical libraries exist, most notably Math.NET Numerics [@mathnetnumerics], which provides linear algebra, probability distributions, and basic statistics. However, Math.NET Numerics does not include L-moment estimation, hydrology-specific distributions (Log-Pearson Type III, Kappa-Four), mixture and competing-risk models, adaptive integration, bootstrap resampling, copulas, or global optimization algorithms. Math.NET Numerics includes basic MCMC samplers (Metropolis-Hastings, Hybrid Monte Carlo, Slice), but lacks the adaptive and ensemble methods (Adaptive RWMH, DE-MCz, NUTS) needed for reliable calibration of hydrologic models. Contributing these features to Math.NET Numerics was not pursued because the scope of domain-specific functionality, the distinct API design requirements for risk assessment workflows, and the need for long-term maintenance by a domain-expert organization warranted an independent library.
+General-purpose .NET numerical libraries exist, most notably Math.NET Numerics [@mathnetnumerics], which provides linear algebra, probability distributions, and basic statistics. However, Math.NET Numerics does not include L-moment estimation, hydrology-specific distributions (Log-Pearson Type III, Kappa-Four), mixture and competing-risk models, adaptive integration, bootstrap resampling, copulas, or global optimization algorithms. Math.NET Numerics includes basic MCMC samplers (Metropolis-Hastings, Hybrid Monte Carlo, Slice), but lacks the adaptive and ensemble methods (Adaptive RWMH, DE-MCzs, NUTS) needed for reliable calibration of hydrologic models. Contributing these features to Math.NET Numerics was not pursued because the scope of domain-specific functionality, the distinct API design requirements for risk assessment workflows, and the need for long-term maintenance by a domain-expert organization warranted an independent library.
 
 In other language ecosystems, Python's SciPy [@virtanen2020] provides broad numerical capabilities but lacks specialized hydrological distributions and L-moment estimation. R packages such as `lmom` [@hosking2019lmom] and `evd` [@stephenson2002evd] offer these features individually, but integrating R into .NET production applications introduces runtime dependencies and complicates deployment in regulated government environments. No single package in any ecosystem consolidates mixture distributions, competing-risk models, adaptive integration, extreme-value statistics, and MCMC into a unified library for engineering risk analysis.