Our customers

This is a partial list of companies who are using our libraries:

ABB Robotics
Allstate
Arcam
Astra Schedule
Babson College
Canyon Associates
Caxton Associates
CECity
Constellation Energy
CreditSights
DeepOcean
Duke University
Dynamotive
Elecsoft
Engelhard Corporation
Epcor
Equipoise Software
Galileo International
GAM UK
Gammex
GlaxoSmithKline
Global Matrix
The Hartford
Infinera Corporation
Intel
JDS Uniphase
LaBranche & Co.
Learning & Skills Council
Jacobs Consultancy
Litman Gregory
Lucas Systems
Malvern Instruments
Medrio
Merck & Co.
Mintera.
Monitor Software
MorningStar
NanoString Technologies
Paletta Invent
Parametric Portfolio Associates
Prosanos
RATA Associates
RiskShield
Ramboll
Standard & Poor's
Strategic Analysis Corporation
Univ. of Alicante
Univ. of South Carolina
vielife
Xerox
US Army

Home»Features»Statistics Library Features

Try it now

Extreme Optimization Numerical Libraries for .NET

Statistics Library Features

Below is a list of features for the statistics library portion of the Extreme Optimization Numerical Libraries for .NET. Also see the detailed data analysis mathematics, and vector and matrix library feature lists.

Descriptive Statistics

• Measures of central tendency: mean, median, trimmed mean, harmonic mean, geometric mean.
• Measures of scale: variance, standard deviation, range, interquartile range, absolute deviation from mean and median.
• Higher moments: skewness, kurtosis.

Probability Distributions

• Probability density function (PDF).
• Cumulative distribution function (CDF).
• Percentile or inverse cumulative distribution function.
• Moments: mean, variance, skewness and kurtosis.
• Generate random samples from any distribution.
• Parameter estimation for selected distributions Updated!

Continuous Probability Distributions

• Beta distribution.
• Cauchy distribution.
• Chi-squared distribution.
• Erlang distribution.
• Exponential distribution.
• F distribution.
• Gamma distribution.
• Generalized Pareto distribution.
• Gumbel distribution.
• Inverse chi-square distribution.
• Inverse gamma distribution.
• Inverse Gaussian distribution.
• Inverse Weibull distribution.
• Laplace distribution.
• Logistic distribution.
• Log-logistic distribution.
• Lognormal distribution.
• Maxwell distribution.
• Normal distribution.
• Normal inverse Gaussian distribution.
• Pareto distribution.
• Piecewise distribution.
• Rayleigh distribution.
• Student t distribution.
• Transformed beta distribution.
• Transformed gamma distribution.
• Triangular distribution.
• General truncated distributions.
• Uniform distribution.
• Weibull distribution.

Discrete Probability Distributions

• Bernoulli distribution.
• Binomial distribution.
• Geometric distribution.
• Hypergeometric distribution.
• Log-series distribution.
• Negative binomial distribution.
• Poisson distribution.
• Uniform distribution.

Multivariate Probability Distributions

• Multivariate normal distribution.
• Dirichlet distribution.
• Wischart distribution.

Histograms

• One-dimensional histograms.
• Probability distribution associated with a histogram.

General Linear Models

• Infrastructure for General Linear Model and Generalized Linear Model calculations.
• Analysis of variance.
• Regression analysis.
• Model-specific hypothesis tests.

Analysis of variance (ANOVA)

• One and two-way ANOVA.
• Post-hoc tests for one-way ANOVA: Tukey, Tukey-Kramer, Fisher-Heyter, Scheffé
• One-way ANOVA with repeated measures.

Regression analysis

• Simple, multiple, and polynomial regression
• Nonlinear regression
• Logistic regression
• Generalized linear models
• Flexible regression models.
• Variance-covariance matrix, regression matrix.
• Confidence intervals and significance tests for regression parameters.

Time series analysis

• Treat several observation variables as a unit.
• Change frequency of time series.
• Automatically apply predefined aggregators.
• Advanced aggregators: volume weighted average.

Transformations of Time Series Data

• Lagged time series, sums, products.
• Change, percent change, growth rate.
• Extrapolated change, percent change, growth rate.
• Period to date sums and differences.
• Simple, exponential, weighted moving average.
• Savitsky-Golay smoothing.

Multivariate Models

• Hierarchical clustering.
• Linkage: single, complete, average, centroid, Ward, median, McQuitty
• Continuous distance measures: Euclidean, squared Euclidean, maximum, Manhattan, Canberra, cosine, correlation, Minkowski
• Binary distance measures: binary matching, Jaccard, Russell, Hamann, dice, anti-dice, Sneath, Rogers, Ochiai, Yule, Anderberg, Kulczynski, Pearson
• K-means clustering.
• Initialize using: random centers, random assignments, K-means++
• Factor analysis.
• Factor methods: principal components, iterative principal axis, unweighted least squares, generalized least squares, maximum likelihood, alpha factoring, image factoring.
• Rotation methods: Varimax, Equamax, Quartimax, Parsimax, Promax.
• Scoring method: regression, Bartlett, Anderson-Rubin.
• Principal Component Analysis (PCA).

Statistical tests

• Tests for the mean: one sample z-test, one sample t-test.
• Paired and unpaired two-sample t test for the difference between two sample means.
• Two Sample z-test for ratios.
• One sample chi-squared test for variance.
• F-test for the ratio of two variances.
• One and two sample Kolmogorov-Smirnov test.
• Tests for normality: Anderson-Darling, Shapiro-Wilk
• Chi-squared goodness-of-fit test.
• Test for outliers: Grubbs' test, Generalized ESD test.
• Bartlett and Levene tests for homogeneity of variances.
• McNemar and Stuart-Maxwell test.

Random number generation

• Compatible with the .NET Framework's System.Random.
• Four generators, with varying quality, period and speed to suit your application.
• Generate random samples from any distribution.
• Quasi-random sequences: Fauré, Halton, Sobol sequences
• Shufflers and randomized enumerators