Getting Started with NMath

We are often asked how to get started with using NMath. This has gotten much simpler over the years. Here’s the quickest way to get going… Install Visual Studio Code. Create a folder. Run VS Code then open the folder with File|Open Folder…. View|Terminal to bring up a command-line. In the terminal window, type dotnet […]


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NMath and X86

NMath customers are overwhelmingly developing with 64-bit packages, therefore CenterSpace has decided to drop support for 32-bit operating systems with the release of NMath 7.2. However, we will continue to support x86 versions of NMath for the foreseeable future and their packages will continue to be available from nuget. Release of NMath 7.2 NMath version […]


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Chromatographic and Spectographic Data Analysis

Chromatographic and spectographic data analysis is a common application of the NMath class library and usually involves some or all of the following computing activities: Noise removal Baseline adjustment Peak finding Peak modeling Peak statistical analysis In this blog article we will discuss each of these activities and provide some NMath C# code on how […]


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Fitting the Weibull Distribution

The Weibull distribution is widely used in reliability analysis, hazard analysis, for modeling part failure rates and in many other applications. The NMath library currently includes 19 probably distributions and has recently added a fitting function to the Weibull distribution class at the request of a customer. The Weibull probability distribution, over the random variable […]


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NMath 7.0 & the .NET Standard Library

In December, CenterSpace Software rolled out a major new release of NMath, version 7.0, built on the .NET Standard Library 2.0. The focus of this release has been to support the .NET Standard library, to further improve the ease of use of the NMath library, and to unify all CenterSpace libraries into one. CenterSpace now […]


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Principal Components Regression: Part 3 – The NIPALS Algorithm

In this final entry of our three part series on Principle Component Regression (PCR) we described the NIPALS algorithm used to compute the principle components. This is followed by a theoretical discussion of why the NIPALS algorithm works that is accessible to non-experts.


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