nmath

NMath Analysis description
NMath Analysis is a software that contains classes for function minimization, root-finding, and linear programming NMath Analysis is a software that contains classes for function minimization, root-finding, and linear programming.

Here are some key features of "NMath Analysis":

· Classes for minimizing univariate functions using golden section search and Brent s method.
· Classes for minimizing multivariate functions using the downhill simplex method, Powell s direction set method , the conjugate gradient method, and the variable metric (or quasi-Newton) method.
· Simulated annealing.
· Linear programming using the simplex method.
· Least squares polynomial fitting.
· Classes for finding roots of univariate functions using the secant method, Ridders method, and the Newton-Raphson method.
· Numerical methods for double integration of functions of two variables.


Requirements:

· Microsoft .NET Framework
· NMath Core 2.2


Limitations:

· 30 days trial

NMath Matrix is a useful and advanced matrix manipulation library which extends NMath Core.

Here are some key features of "NMath Matrix":

· Full-featured structured sparse matrix classes, including triangular, symmetric, Hermitian, banded, tridiagonal, symmetric banded, and Hermitian banded.
· Functions for converting between general matrices and structured sparse matrix types.
· Functions for transposing structured sparse matrices, computing inner products, and calculating matrix norms.
· Classes for factoring structured sparse matrices, including LU, Bunch-Kaufman, and Cholesky factorizations, that you can be used to solve linear systems and compute determinants, inverses, and condition numbers.
· Orthogonal decomposition classes for general matrices, including QR decomposition and singular value decomposition (SVD).
· Advanced least squares factorization classes for general matrices, including Cholesky, QR, and SVD.
· Classes for solving symmetric, Hermitian, and nonsymmetric eigenvalue problems.

NMath Analysis is a software that contains classes for function minimization, root-finding, and linear programming.

Here are some key features of "NMath Analysis":

· Classes for minimizing univariate functions using golden section search and Brent s method.
· Classes for minimizing multivariate functions using the downhill simplex method, Powell s direction set method, the conjugate gradient method, and the variable metric (or quasi-Newton) method.
· Simulated annealing.
· Linear programming using the simplex method.
· Least squares polynomial fitting.
· Classes for finding roots of univariate functions using the secant method, Ridders method, and the Newton-Raphson method.
· Numerical methods for double integration of functions of two variables.

NMath Core description
Aprogram which contains foundational classes for object-oriented numerics on the .NET platform NMath Core is a program which contains foundational classes for object-oriented numerics on the .NET platform.

Here are some key features of "NMath Core":

· Single- and double-precision complex number classes.
· Full-featured vector and matrix classes for four datatypes: single- and double-precision floating point numbers, and single- and double-precision complex numbers.
· Flexible indexing using slices and ranges.
· LU factorization for a matrix, as well as functions for solving linear systems, computing determinants, inverses, and condition numbers.
· Least Squares solutions.
· Random number generation from various probability distributions, including the uniform, triangular, normal, Poisson, gamma, binomial, exponential, Pareto, and log normal distributions.
· Classes for encapsulating functions of one variable, with support for numerical integration (Romberg and Gauss-Kronrod methods), differentiation (Ridders method), and algebraic manipulation of functions.
· Polynomial encapsulation, interpolation, and exact differentiation and integration.
· Cubic spline interpolation.
· Extension of standard mathematical functions, such as Cos(), Sqrt(), and Exp(), to work with vectors, matrices, and complex number classes.
· Basic statistical functions, such as Mean(),
· Median(), and Variance(), with support for NaN (Not-A-Number) values.


Requirements:

· Microsoft .NET Framework


Limitations:

· 30 days trial

NMath Stats is a useful application that contains classes for data manipulation, statistical computation, and biostatistics.

Here are some key features of "NMath Stats":

· A data frame class for holding data of various types (numeric, string, boolean, datetime, and generic), with methods for appending, inserting, removing, sorting, and permuting rows and columns.
· Functions for computing descriptive statistics, such as mean, variance, standard deviation, percentile, median, quartiles, geometric mean, harmonic mean, RMS, kurtosis, skewness, and many more.
· Special functions, such as factorial, log factorial, binomial coefficient, log binomial, log gamma, incomplete gamma, beta, and incomplete beta.
· Probability density function (PDF), cumulative distribution function (CDF), inverse CDF, and random variable moments for a variety of probability distributions, including normal (Gaussian), Poisson, chi-square, gamma, beta, Students t, F, binomial, and negative binomial.
· Multiple linear regression.
· Basic hypothesis tests, such as z test, t-test, and F-test, with calculation of p-values, critical values, and confidence intervals.
· One-way and two-way analysis of variance (ANOVA) and analysis of variance with repeated measures (RANOVA).
· Multivariate statistical analyses, including principal component analysis and hierarchical cluster analysis.

NMath Core is a program which contains foundational classes for object-oriented numerics on the .NET platform.

Here are some key features of "NMath Core":

· Single- and double-precision complex number classes.
· Full-featured vector and matrix classes for four datatypes: single- and double-precision floating point numbers, and single- and double-precision complex numbers.
· Flexible indexing using slices and ranges.
· LU factorization for a matrix, as well as functions for solving linear systems, computing determinants, inverses, and condition numbers.
· Least Squares solutions.
· Random number generation from various probability distributions, including the uniform, triangular, normal, Poisson, gamma, binomial, exponential, Pareto, and log normal distributions.
· Classes for encapsulating functions of one variable, with support for numerical integration (Romberg and Gauss-Kronrod methods), differentiation (Ridders method), and algebraic manipulation of functions.
· Polynomial encapsulation, interpolation, and exact differentiation and integration.
· Cubic spline interpolation.
· Extension of standard mathematical functions, such as Cos(), Sqrt(), and Exp(), to work with vectors, matrices, and complex number classes.
· Basic statistical functions, such as Mean(),
· Median(), and Variance(), with support for NaN (Not-A-Number) values.