Class AnovaRegressionFactorParam provides information about a regression parameter associated with a specific level of an ANOVA factor.

Class AnovaRegressionInteractionParam provides information about a regression parameter associated with the interaction between the level of one ANOVA factor and the level of another ANOVA factor.

Class AnovaRegressionParameter provides information about a regression parameter used to perform an analysis of variance by class TwoWayAnova.

Class AnovaRegressionSubjectParam provides information about a regression parameter associated with a subject dummy regression variable.

Class BetaDistribution represents the beta probability distribution.

Class BinomialDistribution represents the discrete probability distribution of obtaining exactly n successes in N trials where the probability of success on each trial is P.

Class for computing the Box-Cox power tranformations defined for a set of data points, {yi}, and parameter value lambda by yi(lambda) = (yi^lambda - 1)/lambda. In addition methods for computing the corresponding log-likelihood function and the value of lambda which maximizes it are provided.

Class ChiSquareDistribution represents the chi-square probability distribution.

Class ClusterAnalysis perform hierarchical cluster analysis.

Class ClusterSet represents a collection of objects assigned to a finite number of clusters.

Class ConnectivityMatrix represents a symmetric matrix of double-precision floating point values.

Class CORegressionCalculation computes linear regression parameters by the method of least squares using a complete orthogonal decomposition.

Class DataFrame represents a two-dimensional data object consisting of a list of columns of the same length.

Class DFBoolColumn represents a column of logical data in a data frame.

Abstract base class for data frame column types.

Class DFDataTimeColumn represents a column of DataTime data in a data frame.

Class DFGenericColumn represents a column of generic data in a data frame.

Class DFIntColumn represents a column of integer data in a data frame.

Class DFNumericColumn represents a column of numeric data in a data frame.

Class DFStringColumn represents a column of string data in a data frame.

Class Distance provides functions for computing the distance between objects.

Class PowerDistance compute the power distance between two vectors.

Class performs a factor analysis on a symmetric matrix of data, assumed to be either a correlation or covariance matrix, using specified factor extraction and rotation algorithms. The analysis consists of 2 steps: First, factors are extracted from the symmetric matrix data, second, the factors are rotated in order to maximize the relationship between the variables and some of the factors.

Class DoublePCA performs a principal component analysis on a given double-precision data matrix, or data frame.

Class ExponentialDistribution represents the Exponential probability distribution.

Class Factor represents a categorical vector in which all elements are drawn from a finite number of factor levels.

Class FactorAnalysisCorrelation performs a factor analysis on a set of case data using the correlation matrix and specified factor extraction and rotation algorithms.

Class FactorAnalysisCovariance performs a factor analysis on a set of case data using the covariance matrix and specified factor extraction and rotation algorithms.

Class FDistribution represents the F probability distribution.

Class FloatPCA performs a principal component analysis on a given single-precision data matrix.

Class GammaDistribution represents the gamma probability distribution.

Class GeometricDistribution represents the goemetric probability distribution.

Class GoodnessOfFit tests goodness of fit for least squares model-fitting classes, such as LinearRegression, PolynomialLeastSquares, and OneVariableFunctionFitter.

Class GoodnessOfFitParameter tests statistical hypotheses about estimated parameters in regression models.

Instances of the InputVariableCorrelator class are used to induce a desired rank correlation among input variables.

Interface for performing a Partial Least Squares (PLS) calculation.

Interface for performing a Partial Least Squares (PLS) calculation.

Class JohnsonDistribution represents the Johnson system of distributions.

Class KFoldsSubsets generates k-fold subsets for cross validation.

Class KFoldSubsets generates k-fold subsets for cross validation.

Class KMeansClustering performs k-means clustering on a set of data points.

Class KruskalWallisTable summarizes the information of Kruskal-Wallis rank sum test.

Class KruskalWallisTest performs a Kruskal-Wallis rank sum test.

Class LeaveOneOutSubsets generates the index subsets for a leave-one-out cross validations calculation.

Class LinearRegression computes a multiple linear regression from an input matrix of independent variable values and vector of dependent variable values.

Class LinearRegressionAnova tests overall model significance for linear regressions computed by class LinearRegression.

Class LinearRegressionParameter tests statistical hypotheses about estimated parameters in linear regressions computed by class LinearRegression.

Class Linkage provides functions for computing the distance between clusters of objects.

Class LogisticDistribution represents the logistic probability distribution with a specifed location (mean) and scale.

Class for performing a binomial logistic regression.

Class for for calculating "goodness of fit" statistics for a logistic regression model.

Class representing a group used in computing the Hosmer Lemeshow statistic for a logistic regression model.

Class containing the attributes of the Hosmer Lemeshow statistic for a logistic regression model.

Class containing the attributes of the Pearson chi-square statistic associated with a logistic regression model.

Class containing Pearson Residual attributes. The Pearson Residual is calculated for each covariate pattern.

Class LogisticRegressionParameter tests statistical hypotheses about estimated parameters in linear regressions computed by class LogisticRegression.

Class LognormalDistribution represents the lognormal probability distribution.

Class NegativeBinomialDistribution represents the discrete probability distribution of obtaining N successes in a series of x trials, where the probability of success on each trial is P.

Parameter calculation for a logistic regression model. The parameters are computed to maximize the log likelihood function for the model, using the Newton Raphson algorithm to compute the zeros of the first order partial derivaties of the log likelihood function.

Class NMFact performs non-negative matrix factorization.

Class NMFAlsUpdate encapsulates the Alternating Least Squares (ALS) update algorithm.

Class NMFClustering performs a Non-negative Matrix Factorization (NMF) of a given matrix.

Class NMFConsensusMatrix uses a non-negative matrix factorization to cluster samples.

Class NMFDivergenceUpdate encapulates an NMF update algorithm which minimizes a divergence functional.

Class NMFGdClsUpdate encapsulates the Gradient Descent - Constrained Least Squares (GDCLS) algorithm for Nonnegative Matrix Facotorization (NMF).

Class NMFMultiplicativeUpdate encapsulates a multiplicative update algorithm for Nonnegative Matrix Factorization (NMF).

Class NMFNonsmoothUpdate encapulates an NMF update algorithm which minimizes a cost functional designed to explicitly represent sparseness, in the form on nonsmoothness, which is controlled by a single parameter.

Class NormalDistribution represents the normal (Gaussian) probability distribution with a specifed mean and variance.

Used as a class type parameter value to factor analysis classes when no factor rotation is desired.

The class contains static methods for creating function objects suitable for use as "number of factors" functors used in the factor extraction step of a factor analysis. These functions take as paramters the eigenvalues and eigenvectors values computed during factor extraction and return the number of factors to "keep".

Class OneSampleAndersonDarlingTest performs a Anderson-Darling test of the distribution of one sample.

Class OneSampleKSTest performs a Kolmogorov-Smirnov test of the distribution of one sample.

Class OneSampleTTest compares a single sample mean to an expected mean from a normal distribution with an unknown standard deviation.

Class OneSampleZTest compares a single sample mean to an expected mean from a normal distribution with known standard deviation.

Class OneWayAnova computes and summarizes a traditional one-way (single factor) Analysis of Variance (ANOVA).

Class OneWayAnovaTable summarizes the information of a traditional one-way Analysis of Variance (ANOVA) table.

Class OneWayRanova summarizes the information of a one-way repeated measures Analysis of Variance (RANOVA).

Class OneWayRanovaTable summarizes the information of a traditional one-way repeated measures Analysis of Variance (RANOVA) table.

Class OrderedConnectivityMatrix reorders the rows and columns of an connectivity matrix so that the most affiliated elements appear as clusters of higher values along the diagonal.

Class implementing the principle components (PC) algorithm for factor extraction when performing factor analysis. Used as a class type parameter for the factor analysis classes.

Class PearsonsChiSquareTest tests whether the frequency distribution of experimental outcomes are consistant with a particular theoretical distribution.

Class PLS1 performs a Partial Least Squares (PLS) regression calculation on a set of predictive and one-dimensional response values. The result is used to predict response variable values.

Class PLS1Anova performs a standard ANalysis Of VAriance (ANOVA) for a Partial Least Squares 1 (PLS1) regression model.

Class PLS1CrossValidation performs an evaluation of a PLS (Partial Least Squares) model.

Class PLS1CrossValidationData divides Partial Least Squares - one dimensional response variable,(PLS1), data into training and testing subsets.

Class PLS2CrossValidationResult performs a Partial Least Squares - one dimensional response variable, (PLS1), cross validation calculation.

Class PLS1NipalsAlgorithm encapsulates the Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm for computing partial least squares regression components.

Class PLS2 performs a Partial Least Squares (PLS) regression calculation on a set of predictive and response values. The result is used to predict response variable values.

Class PLS2Anova performs a standard ANalysis Of VAriance (ANOVA) for a Partial Least Squares (PLS) regression model.

Class PLS2CrossValidation performs an evaluation of a PLS (Partial Least Squares) model.

Class PLS2CrossValidationData divides Partial Least Squares (PLS) data into training and testing subsets.

Class PLS2CrossValidationResult performs a Partial Least Squares (PLS) cross validation calculation.

Class PLS2NipalsAlgorithm encapsulates the Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm for computing partial least squares regression components.

Class PLS2SimplsAlgorithm encapsulates the Straightforward IMplementation of Partial Least Squares, or SIMPLS, algorithm (de Jong, 1993) for computing partial least squares regression components.

Class PoissonDistribution represents a poisson distribution with a specified lambda, which is both the mean and the variance of the distribution. The poisson distribution a discrete distribution representing the probability of obtaining exactly n successes in N trials.

Class for computing the dominant eigenvalue and eigenvector of a square matrix using the iterative power method.

Class ProbabilityDistribution is the abstract base class for classes that represent distributions of random variables.

Computes the process capability parameters Cp, Cpm, Cp for normally distributed data. If the data is not normal the Box-Cox transform can be used.

Computes process performance parameters Pp and Ppk for normally distributed data. If the data is not normal the Box-Cox transform can be used.

Class QRRegressionCalculation computes linear regression parameters by the method of least squares using a QR decomposition.

Instances of the ReducedVarianceInputCorrelator class are used to induce a desired rank correlation among input variables.

Base class for linear and logistic regression.

Class implementing the interface for computing factor scores using the regression algorithm. The regression algorithm uses a least squares regression approach to predict factor scores. Specifically this method computes the solution X to the matrix equation RX = B, where R = covariance matrix, B = factor matrix, X = factor scores.

Class ShapiroWilkTest tests the null hypothesis that the sample comes from a normally distributed population.

Class StatsFunctions provides statistical functions for NMath types, including descriptive statistics and special functions.

Class StatsSettings contains global settings for NMath Stats classes.

Class Subset represents a collection of indices that can be used to view a subset of data from another data structure.

Class SVDRegressionCalculation computes linear regression parameters by the method of least squares using a singular value decomposition.

Class TDistribution represents Student's t-distribution with the specified degrees of freedom.

Class TriangularDistribution represents the triangular probability distribution.

Parameter calculation for a logistic regression model. The parameters are computed to maximize the log likelihood function for the model, using a trust region optimization algorithm to compute the zeros of the first order partial derivaties of the log likelihood function. The minimization is performed by an instance of the class CenterSpace.NMath.Analysis.TrustRegionMinimizer and algorithms parameters may be controlled through this object. It is accessible through the Minimizer class property, and a TrustRegionParameterCalc instace may be constructed with a give TrustRegionMinimizer object which has the desired properties. TrustRegionMinimizer

Class TwoSampleFTest tests if the variances of two populations are equal.

Class TwoSampleKSTest performs a two-sample Kolmogorov-Smirnov test to compare the distributions of values in two data sets.

Class TwoSamplePairedTTest tests if two paired sets of observed values differ from each other in a significant way.

Class TwoSampleUnpairedTTest tests the null hypothesis that the two population means corresponding to two random samples are equal.

Class TwoSampleUnpairedUnequalTTest tests the null hypothesis that the two population means corresponding to two random samples are equal.

Class TwoWayAnova performs a balanced two-way analysis of variance.

Class TwoWayAnovaTable summarizes the information of a traditional two-way Analysis of Variance (ANOVA) table.

Class TwoWayRanova performs a balanced two-way analysis of variance with repeated measures on one factor.

Class TwoWayRanovaTable summarizes the information of a traditional two-way Analysis of Variance (RANOVA) table.

Class TwoWayRanovaTwo performs a balanced two-way analysis of variance with repeated measures on both factors.

Class TwoWayRanovaTwoTable summarizes the information of a traditional two-way Analysis of Variance, with repeated measures on both factors, table,

Class UniformDistribution represents the Uniform probability distribution.

Class for computing the varimax rotation of the factor from a factor analysis. Rotates the coordinates to maximize the sum of the variances of the squared loadings. Kaiser normalization is optionally performed, and the default stopping tolerance (1e-12) is used.

Class WeibullDistribution represents the Weibull probability distribution.

Interface for generating subsets of data to be used in a cross validation process.

Interface for data frame column types.

Interface for factor extration algorithms used in factor analysis.

Interface for factor analysis factor rotation algorithms. Factors are rotated in order to maximize the relationship between the variables and some of the factors.

Interface for factor score computation in a factor analysis.

Interface class for calculating the parameters of a logistic regression model.

Interface to be implemented by all Non-negative Matrix Factorization (NMF) update algorithms used by the NMFact class.

Interface implemented by probablility distributions.

Interface for classes used by class LinearRegression to calculate regression parameters.

Functor that takes two vectors and returns a measure of the distance (similarity) between them.

Functor that computes the linkage (similarity) between two groups.

Given an entry aij in the connectivity matrix A, this delegate must return the distance between the elements i and j to be used for performing the hierarchical cluster analysis.

Functor that takes a data frame column and returns a datetime value.

Functor that takes a data frame column and returns a double-precision floating point number.

Functor that takes a data frame column and returns a generic object.

Functor that takes a data frame column and returns an integer.

Functor that takes a double-precision floating point number and returns a boolean value.

Functor that takes a data frame column and returns a boolean value.

Functor that takes an integer and returns a boolean value.

Functor that takes a string and returns a boolean value.

Functor that takes a data frame column and returns a string.

Enumeration for specifying a biased or unbiased estimator.

Enumeration for specifying the form of an alternative hypothesis in a hypothesis test.

An enumeration representing methods used to choose the initial cluster centers.

Enumeration for specifying different sorting types, such as ascending or descending order.