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Development / Components & Libraries / Meta.Numerics 2.0
Meta.Numerics 2.0
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Meta.Numerics 2.0 Description
Meta.Numerics is a library for advanced scientific computation in the .NET Framework. It can be used from C#, Visual Basic, F#, or any other .NET programming language. The Meta.
Meta.Numerics is a library for advanced scientific computation in the .NET Framework. It can be used from C#, Visual Basic, F#, or any other .NET programming language. The Meta.Numerics library is fully object-oriented and optimized for speed of implementation and execution. Meta.Numerics library features include complex numbers and other mathematical objects, special functions, numerical calculus, statistics and data analysis, linear algebra, and Fourier transforms. Meta.Numerics defines several specialized mathematical objects: - Complex numbers - Matrices - Spinors - Uncertain values For each type, appropriate arithmetic operations are defined and associated classes implement appropriate operations. Advanced Functions: - Gamma - Psi (Digamma) - Beta - Error Function - Fresnel Integrals - Exponential and Trigonometric Integrals - Bessel J and Y - Modified Bessel I and K - Coulomb Wave Functions F and G - Reimann Zeta - Dilogarithm Li2 (Spence's Function) - Orthogonal polynomials - Elliptic Integrals Numerical Analysis: - maxima and minima - roots - integration - differentiation Statistics and Data Analysis: Data Collections The library provides specialized classes for working with various types of data, including: - Univariate Samples - Bivariate Samples - Multivariate Samples - Experimental Data with Error Bars - Contingency Tables For each kind of data, methods allow you to evaluate descriptive statistics, fit models, and perform appropriate statisical tests. All fits produce not just the best-fit parameter set, but also error bars, a covariance matrix, and a goodness-of-fit test. Specialized methods make it easy to load data for analysis from a database or spreadsheet. Matrix Algebra: The library defines a number of matrix classes: rectangular, square, symmetric, and tridiagonal. Each class defines operations appropriate to that matrix type, implemented to exploit the matrix structure for optimum performance. - Arithmetic - Decomposition - Determinant - Inverse - Eigenvalues and Eigenvectors
OS: Windows
Tags: Scientific - Technical - Engineering - Statistics - Numeric - .Net - C# - Csharp - Vb - Visual Basic - F# - Fsharp