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. Open the folder with File|Open. View|Terminal to bring up a command-line. In the terminal window, type dotnet new console to create a […]
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 […]
NMath is moving from Microsoft Solver Foundation to Google OR Tools. This change improves our LP and MIP Solver performance.
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 […]
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 […]
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 […]
CenterSpace will be adding support for both .NET Core and .NET Standard to NMath by the end of 2018. NMath has also dropped support of both the OSX and Linux86 operating systems in NMath release 6.2.0.41.
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.