RMC.Numerics 2.2.0

.NET 8.0 This package targets .NET 8.0. The package is compatible with this framework or higher. .NET Framework 4.8.1 This package targets .NET Framework 4.8.1. The package is compatible with this framework or higher. 
dotnet add package RMC.Numerics --version 2.2.0
                    


                    

                
NuGet\Install-Package RMC.Numerics -Version 2.2.0
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package. 
<PackageReference Include="RMC.Numerics" Version="2.2.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
<PackageVersion Include="RMC.Numerics" Version="2.2.0" />
                    


                            Directory.Packages.props
                        

                    

                
<PackageReference Include="RMC.Numerics" />
                    


                            Project file
For projects that support Central Package Management (CPM), copy this XML node into the solution Directory.Packages.props file to version the package. 
paket add RMC.Numerics --version 2.2.0
                    


                    

                
#r "nuget: RMC.Numerics, 2.2.0"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package. 
#:package RMC.Numerics@2.2.0
#:package directive can be used in C# file-based apps starting in .NET 10 preview 4. Copy this into a .cs file before any lines of code to reference the package. 
#addin nuget:?package=RMC.Numerics&version=2.2.0
                    


                            Install as a Cake Addin
                        

                    

                
#tool nuget:?package=RMC.Numerics&version=2.2.0
                    


                            Install as a Cake Tool

Numerics

CI JOSS DOI NuGet License: 0BSD

Numerics is a free and open-source numerical computing library for .NET developed by the U.S. Army Corps of Engineers Risk Management Center (USACE-RMC). It provides methods and algorithms for probability distributions, statistical analysis, numerical methods, optimization, machine learning, and Bayesian MCMC sampling - with a focus on hydrological and risk assessment applications.

Supported Frameworks

Framework Version
.NET 10.0, 9.0, 8.0
.NET Framework 4.8.1

Install via NuGet:

dotnet add package RMC.Numerics

Or search for RMC.Numerics in the NuGet Package Manager.

Documentation

User Guide and API Documentation - Comprehensive documentation with code examples and mathematical explanations.

Section Topics
Mathematics Adaptive one- and two-dimensional integration, differentiation, optimization, root finding, linear algebra, ODE solvers, special functions
Data Interpolation, linear regression, time series analysis
Statistics Descriptive and weighted statistics, goodness-of-fit metrics, hypothesis tests, global sensitivity analysis
Distributions 43 univariate distributions, parameter estimation, uncertainty analysis, copulas, multivariate distributions
Functions Uncertain univariate function forms, composition, posterior ensembles, link functions
Machine Learning GLM, decision trees, random forests, KNN, naive Bayes, k-means, GMM
Sampling MCMC (RWMH, ARWMH, DE-MCz, DE-MCzs, HMC, NUTS, Gibbs), random generation, scrambled quasi-random sequences, convergence diagnostics
References Consolidated bibliography

Prerequisites

  • .NET 8+ runtime (or .NET Framework 4.8.1 on Windows). Install the .NET SDK if you don't already have it.
    • The Microsoft .NET SDK is available on the App Portal for Corps users.

Support

USACE-RMC is committed to maintaining and supporting the library with regular updates, bug fixes, and enhancements.

The repository includes a unit testing library with over 2,300 tests that also serve as usage examples for the classes and methods in the library.

Contributing

Contributions are welcome. Please see CONTRIBUTING.md for guidelines on bug reports, feature requests, and pull requests.

License

See LICENSE for details.

Product Compatible and additional computed target framework versions.
.NET net8.0 is compatible.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed.  net9.0 is compatible.  net9.0-android was computed.  net9.0-browser was computed.  net9.0-ios was computed.  net9.0-maccatalyst was computed.  net9.0-macos was computed.  net9.0-tvos was computed.  net9.0-windows was computed.  net10.0 is compatible.  net10.0-android was computed.  net10.0-browser was computed.  net10.0-ios was computed.  net10.0-maccatalyst was computed.  net10.0-macos was computed.  net10.0-tvos was computed.  net10.0-windows was computed. 
.NET Framework net481 is compatible. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (2)

Showing the top 2 NuGet packages that depend on RMC.Numerics:

Package Downloads
RMC.Wpf.Framework.Controls

WPF controls, framework UI, DAG controls, database controls, numeric controls, OxyPlot controls, and AvalonDock assemblies for the RMC WPF Framework.
RMC.BestFit

Portable model library for RMC-BestFit Bayesian estimation, distribution fitting, and frequency analysis in flood risk and water resources engineering.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last Updated
2.2.0 46 9/18/2026
2.1.4 214 7/17/2026
2.1.3 126 7/15/2026
2.1.2 269 7/9/2026
2.1.1 179 6/17/2026
2.1.0 141 6/17/2026
2.0.2 294 4/27/2026
2.0.1 165 4/6/2026
2.0.0 135 4/6/2026
1.0.0 335 9/12/2024

Version 2.2.0 adds weighted statistics and global sensitivity estimators; adaptive two-dimensional Gauss-Kronrod integration; scrambled Sobol/Vegas sampling; conditional copulas and an Independence copula; composable, serializable functions; empirical convolution; sided extrapolation; singular-covariance multivariate normal support; and expanded shortest-path routing. It improves distribution tails, censored and interval likelihoods, uncertainty calculations, mixture and competing-risk evaluation, adaptive NUTS and MCMC diagnostics, BFGS convergence, matrix regularization, and regression coverage. Upgrade considerations: R-hat/ESS definitions, default NUTS mass adaptation, corrected numerical methods and fitting constraints can change reported results. See the v2.2.0 GitHub release notes for details.