About NMath Stats
Probability distributions, combinatorial functions, descriptive statistics, clustering, regression, hypothesis tests, anova and non-negative matrix factorizaion (NMF).
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 probability distributions, including normal (Gaussian), Poisson, chi-square, gamma, beta, Student's t, F, binomial, Johnson and negative binomial; Combinatorial functions, such as factorial, log factorial, binomial coefficient, and log binomial; Multiple linear regression; Logistic 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; Kruskal-Wallis ranks sum test; Non-negative matrix factorization (NMF); smoothing filters; principal component analysis and clustering. If you are interested in evaluating NMath Stats, we offer a free fully-functional trial version, distributed in binary form only for a 30-day evaluation period. Purchased product includes source code, user documentation, help files and examples.
Previous Versions
Here you can find the changelog of NMath Stats since it was posted on our website on 2011-02-02 18:41:59.
The latest version is 4.2 and it was updated on 2024-03-05 05:24:56. See below the changes in each version.
NMath Stats version 4.2
Updated At: 2016-03-01
Changes: Added two way anova with unbalanced designs, partial least squares discriminant analysis, auxiliary stats for logistic regressions.
NMath Stats version 2.4
Updated At: 2008-02-18
Changes: Now ships with 64-bit prebuilt assemblies. Miscellaneous bug fixes especially with respect to threading. Faster computation.
Disclaimer
External Download
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