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What’s new in NMath Stats V3.6?
- New LogisticRegression and related classes for performing binomial logistic regression.
- New classes for process control statistics, such as Cp, Cpm, Cp, Pp, and Ppk.
What's new in NMath Stats V3.5?
- Classes DoubleFactorAnalysis, FactorAnalysisCorrelation, FactorAnalysisCovariance, and supporting types for performing factor analysis
- Class OneSampleAndersonDarlingTest for performing an Anderson-Darling test of the distribution of one sample
- Class ShapiroWilkTest for testing the null hypothesis that a sample comes from a normally distributed population
- Method StatsFunctions.FishersExactTest() for computing the Fisher's Exact Test p-value for a specified 2 x 2 contingency table
NMath and NMath Stats libraries are now available for use in developing applications on Mono. The Mono versions include all of the same features as the .NET/Windows libraries, with native code compiled for Linux and Mac OS X.
What's new in NMath Stats V3.4?
- Added assembly NMathStatsChartMicrosoft.dll containing class NMathStatsChart, which provides static methods for plotting NMath Stats types using the Microsoft Chart Controls for .NET.
- Upgraded to Intel MKL 10.3 Update 6 with resulting performance increases.
- Modified class TDistribution to accept fractional degrees of freedom.
- Added class TwoSampleUnpairedUnequalTTest. Unlike TwoSampleUnpairedTTest, a pooled estimate of the variance is not used.
What's new in NMath Stats V3.3?
- Added a class for performing a Pearson's chi-square hypothesis test.
What's new in NMath Stats V3.2?
- Upgraded to Intel MKL 10.2 Update 5 with resulting performance increases.
- Added classes InputVariableCorrelator and ReducedVarianceInputCorrelator to induce a desired rank correlation among a set of random input variables.
- Added class KruskalWallisTest for performing a Kruskal-Wallis rank sum test.
- Added class JohnsonDistribution to encapsulate the Johnson system of distributions.
- Added classes GoodnessOfFit and GoodnessOfFitParameter for testing the goodness of fit of least squares model-fitting classes, such as LinearRegression, PolynomialLeastSquares, and OneVariableFunctionFitter.
- Added class BoxCox for computing the Box-Cox power tranformations.
- Added StatsFunction.Median() for float types.
- Fixed issue where StatsFunctions.SumOfSquares() could occasionally return a negative value.
- Improved support for missing values in DataFrame.Load().
What's new in NMath Stats V3.1?
- K-Means Clustering - NMath Stats 3.1 includes a class for k-means clustering, to complement the hierarchical cluster analysis already in the library. The k-means clustering method assigns data points into k groups such that the sum of squares from points to the computed cluster centers is minimized. In general, k-means clustering uses far less memory that constructing a full hierarchical cluster tree.
- Cronbach’s Alpha - NMath Stats 3.1 includes a method for computing Cronbach’s alpha, a measure of reliability (or consistency) in the framework of the domain sampling model. Cronbach’s alpha measures how well a set of items (or variables) measures a single unidimensional latent construct.
- Smoothing Filters - NMath Stats 3.1 will include classes for smoothing data by replacing each data point with a linear combination of the data points immediately to the left and right of the data point. You can provide the coefficients to use in the linear combination, or use provided methods to generate coefficients that implement a moving average filter and a Savitzky-Golay filter. A Savitzky-Golay filter performs a local polynomial regression on the data to determine the smoothed value for each point. This tends to preserve features of the data which are flattened by moving averages, for example, peak finding.
What's new in NMath Stats V3.0?
- Data clustering via nonnegative matrix factorization (NMF)
- Custom debug visualizer for DataFrame
NMath Stats Features
- NMath Stats contains a data table class with functions for computing descriptive statistics, such as mean, variance, standard deviation, percentile, median, quartiles, geometric mean, harmonic mean, RMS, kurtosis, skewness, and many more
- PDF, CDF, inverse CDF, and random variable moments for a variety of probability distributions, including normal (Gaussian), Poisson, chi-square, gamma, beta, Student's t, F, binomial, and negative binomial
- Combinatorial functions, such as factorial, log factorial, binomial coefficient, and log 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); Partial least squares (PLS) with cross validation and the NIPALS and SIMPLS algorithms
- Non-negative matrix factorization (NNMF); Multivariate statistical analyses, including principal component analysis and hierarchical cluster analysis.
NMath Stats is part of CenterSpace Software’s NMath product suite, which provides object-oriented components for mathematical, engineering, scientific, and financial applications on the .NET platform. NMath Stats provides functions for statistical computation, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, and analysis of variance.
Fully compliant with the Microsoft Common Language Specification, all NMath Stats routines are callable from any .NET language, including C#, Visual Basic.NET, and Managed C++.
NMath Stats requires NMath.