Interface  Description 

CdfFunction 
Public interface for the usersupplied cumulative distribution function
to be used by InverseCdf and ChiSquaredTest.

Distribution 
Public interface for the usersupplied distribution function.

NonlinearRegression.Derivative 
Public interface for the user supplied function to compute the
derivative for
NonlinearRegression . 
NonlinearRegression.Function 
Public interface for the user supplied function for
NonlinearRegression . 
ProbabilityDistribution 
Public interface for a usersupplied probability distribution.

Random.BaseGenerator 
Base pseudorandom number.

RandomSequence 
Interface implemented by generators of random or quasirandom
multidimensional sequences.

RegressionBasis 
Public interface for user supplied function to
UserBasisRegression object. 
TimeSeriesOperations.Function 
Public interface for the usersupplied function that defines how to
combine two synchronous time series values.

Class  Description 

ANCOVA 
Analyzes a oneway classification model with covariates.

ANOVA 
Analysis of Variance table and related statistics.

ANOVAFactorial 
Analyzes a balanced factorial design with fixed effects.

ARAutoUnivariate 
Automatically determines the best autoregressive time series model using
Akaike's Information Criterion.

ARMA 
Computes leastsquare estimates of parameters for an ARMA model.

ARMAEstimateMissing 
Estimates missing values in a time series collected with equal spacing.

ARMAMaxLikelihood 
Computes maximum likelihood estimates of
parameters for an ARMA model with p and q autoregressive and
moving average terms respectively.

ARMAOutlierIdentification 
Detects and determines outliers and simultaneously estimates the model parameters in a time
series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA
model.

ARSeasonalFit 
Estimates the optimum seasonality parameters for a time series using an
autoregressive model, AR(p), to represent the time series.

AutoARIMA 
Automatically identifies time series outliers, determines parameters of a
multiplicative seasonal model and produces forecasts that incorporate the effects of
outliers whose effects persist beyond the end of the series.

AutoCorrelation 
Computes the sample autocorrelation function of a stationary time
series.

CategoricalGenLinModel 
Analyzes categorical data using logistic, probit, Poisson, and other linear
models.

Cdf 
Cumulative probability distribution functions.

ChiSquaredTest 
Chisquared goodnessoffit test.

ClusterHierarchical 
Performs a hierarchical cluster analysis from a distance matrix.

ClusterKMeans 
Perform a Kmeans (centroid) cluster analysis.

ClusterKNN 
Perform a kNearest Neighbor classification.

ContingencyTable 
Performs a chisquared analysis of a twoway contingency table.

Covariances 
Computes the sample variancecovariance or correlation matrix.

CrossCorrelation 
Computes the sample crosscorrelation function of two stationary time
series.

Difference 
Differences a seasonal or nonseasonal time series.

DiscriminantAnalysis 
Performs a linear or a quadratic discriminant function analysis among
several known groups.

Dissimilarities 
Computes a matrix of dissimilarities (or similarities) between the columns
(or rows) of a matrix.

EmpiricalQuantiles 
Computes empirical quantiles.

FactorAnalysis 
Performs Principal Component Analysis or Factor Analysis on a covariance or correlation matrix.

FaureSequence 
Generates the lowdiscrepancy Faure sequence.

GammaDistribution 
Evaluates a gamma probability density for a given set of data.

GARCH 
Computes estimates of the parameters of a GARCH(p,q) model.

HoltWintersExponentialSmoothing 
Calculates parameters and forecasts using the HoltWinters
Multiplicative or Additive forecasting method for seasonal data.

InvCdf 
Inverse cumulative probability distribution functions.

InverseCdf 
Inverse of usersupplied cumulative distribution function.

KalmanFilter 
Performs Kalman filtering and evaluates the likelihood function for the
statespace model.

KaplanMeierECDF 
Computes the KaplanMeier reliability function estimates or the CDF based on
failure data that may be multicensored.

KaplanMeierEstimates 
Computes KaplanMeier (or productlimit) estimates of survival probabilities
for a sample of failure times that possibly contain right consoring.

KolmogorovOneSample 
The class
KolmogorovOneSample performs a KolmogorovSmirnov
goodnessoffit test in one sample. 
KolmogorovTwoSample 
Performs a KolmogorovSmirnov twosample test.

LackOfFit 
Performs lackoffit test for a univariate time series or transfer function
given the appropriate correlation function.

LifeTables 
Computes population (current) or cohort life tables based upon the observed
population sizes at the middle (for population table) or the beginning (for
cohort table) of some user specified age intervals.

LinearRegression 
Fits a multiple linear regression model with or without an intercept.

LogNormalDistribution 
Evaluates a lognormal probability density for a given set of data.

MersenneTwister 
A 32bit Mersenne Twister generator.

MersenneTwister64 
A 64bit Mersenne Twister generator.

MultiCrossCorrelation 
Computes the multichannel crosscorrelation function of two mutually
stationary multichannel time series.

MultipleComparisons 
Performs StudentNewmanKeuls multiple comparisons test.

NonlinearRegression 
Fits a multivariate nonlinear regression model using least squares.

NormalDistribution 
Evaluates the normal (Gaussian) probability density for a given set of data.

NormalityTest 
Performs a test for normality.

NormOneSample 
Computes statistics for mean and variance inferences using a sample
from a normal population.

NormTwoSample 
Computes statistics for mean and variance inferences using samples from
two normal populations.

PartialCovariances 
Class
PartialCovariances computes the partial covariances or partial
correlations from an input covariance or correlation matrix. 
Probability density functions.


PoissonDistribution 
Evaluates a Poisson probability density of a given set of data.

ProportionalHazards 
Analyzes survival and reliability data using Cox's proportional hazards model.

Random 
Generate uniform and nonuniform random number distributions.

Ranks 
Compute the ranks, normal scores, or exponential scores
for a vector of observations.

RegressorsForGLM 
Generates regressors for a general linear model.

SelectionRegression 
Selects the best multiple linear regression models.

SignTest 
Performs a sign test.

Sort 
A collection of sorting functions.

StepwiseRegression 
Builds multiple linear regression models using forward selection, backward
selection, or stepwise selection.

Summary 
Computes basic univariate statistics.

TableMultiWay 
Tallies observations into a multiway frequency table.

TableOneWay 
Class
TableOneWay calculates a frequency table for a data array. 
TableTwoWay 
Class
TableTwoWay calculates a twodimensional frequency table for
a data array based upon two variables. 
TimeSeries 
A specialized class for time series data and analysis.

TimeSeriesOperations 
A class of operations and methods for objects of class TimeSeries.

TimeSeriesOperations.CombineMethod 
Public enum of methods for combining synchronous time series values.

TimeSeriesOperations.MergeRule 
Public enum of merge rules that defines how two time series should be
merged.

UserBasisRegression 
Fits a linear function of the form ,
where are the user basis functions
evaluated at index values
is the intercept,
are the coefficients associated with the basis functions, and is the random
error associated with y.

VectorAutoregression 
Performs vector autoregression for a multivariate time series.

WilcoxonRankSum 
Performs a Wilcoxon rank sum test.

Exception  Description 

ARAutoUnivariate.TriangularMatrixSingularException 
The input triangular matrix is singular.

ARMA.IllConditionedException 
The problem is illconditioned.

ARMA.IncreaseErrRelException 
The bound for the relative error is too small.

ARMA.MatrixSingularException 
The input matrix is singular.

ARMA.NewInitialGuessException 
The iteration has not made good progress.

ARMA.TooManyCallsException 
The number of calls to the function has exceeded the maximum
number of iterations times the number of moving average (MA)
parameters + 1.

ARMA.TooManyFcnEvalException 
Maximum number of function evaluations exceeded.

ARMA.TooManyITNException 
Maximum number of iterations exceeded.

ARMA.TooManyJacobianEvalException 
Maximum number of Jacobian evaluations exceeded.

ARMAMaxLikelihood.InitialMAException 
The initial values for the moving average parameters are
not invertible.

ARMAMaxLikelihood.NonInvertibleException 
The solution is noninvertible.

ARMAMaxLikelihood.NonStationaryException 
The solution is nonstationary.

AutoARIMA.NoAcceptableModelFoundException 
No appropriate ARIMA model could be found.

AutoCorrelation.NonPosVariancesException 
The problem is illconditioned.

CategoricalGenLinModel.ClassificationVariableException 
The ClassificationVariable vector has not been initialized.

CategoricalGenLinModel.ClassificationVariableLimitException 
The Classification Variable limit set by the user through
setUpperBound has been exceeded. 
CategoricalGenLinModel.ClassificationVariableValueException 
The number of distinct values for each Classification Variable must be
greater than 1.

CategoricalGenLinModel.DeleteObservationsException 
The number of observations to be deleted (set by
setObservationMax )
has grown too large. 
CategoricalGenLinModel.RankDeficientException 
The model has been determined to be rank deficient.

ChiSquaredTest.DidNotConvergeException 
The iteration did not converge

ChiSquaredTest.NoObservationsException 
There are no observations.

ChiSquaredTest.NotCDFException 
The function is not a Cumulative Distribution Function (CDF).

ClusterKMeans.ClusterNoPointsException 
There is a cluster with no points

ClusterKMeans.NoConvergenceException 
Convergence did not occur within the maximum number of iterations.

Covariances.NonnegativeFreqException 
Frequencies must be nonnegative.

Covariances.NonnegativeWeightException 
Weights must be nonnegative.

CrossCorrelation.NonPosVariancesException 
The problem is illconditioned.

DiscriminantAnalysis.CovarianceSingularException 
The variancecovariance matrix is singular.

DiscriminantAnalysis.EmptyGroupException 
There are no observations in a group.

DiscriminantAnalysis.SumOfWeightsNegException 
The sum of the weights have become negative.

Dissimilarities.NoPositiveVarianceException 
No variable has positive variance.

Dissimilarities.ScaleFactorZeroException 
The computations cannot continue because a scale factor is zero.

Dissimilarities.ZeroNormException 
The computations cannot continue because the Euclidean norm of the
column is equal to zero.

EmpiricalQuantiles.ScaleFactorZeroException 
The computations cannot continue because a scale factor is zero.

FactorAnalysis.BadVarianceException 
Bad variance error.

FactorAnalysis.EigenvalueException 
Eigenvalue error.

FactorAnalysis.NonPositiveEigenvalueException 
Non positive eigenvalue error.

FactorAnalysis.NotPositiveSemiDefiniteException 
Covariance matrix not positive semidefinite.

FactorAnalysis.NotSemiDefiniteException 
Hessian matrix not semidefinite.

FactorAnalysis.RankException 
Rank of covariance matrix error.

FactorAnalysis.SingularException 
Covariance matrix singular error.

GARCH.ConstrInconsistentException 
The equality constraints are inconsistent.

GARCH.EqConstrInconsistentException 
The equality constraints and the bounds on the variables are
found to be inconsistent.

GARCH.NoVectorXException 
No vector X satisfies all of the constraints.

GARCH.TooManyIterationsException 
Number of function evaluations exceeded 1000.

GARCH.VarsDeterminedException 
The variables are determined by the equality constraints.

InverseCdf.DidNotConvergeException 
The iteration did not converge

MultiCrossCorrelation.NonPosVariancesException 
The problem is illconditioned.

NonlinearRegression.NegativeFreqException 
A negative frequency was encountered.

NonlinearRegression.NegativeWeightException 
A negative weight was encountered.

NonlinearRegression.TooManyIterationsException 
The number of iterations has exceeded the maximum allowed.

NormalityTest.NoVariationInputException 
There is no variation in the input data.

PartialCovariances.InvalidMatrixException 
Exception thrown if a computed correlation is greater than one for some pair of variables.

PartialCovariances.InvalidPartialCorrelationException 
Exception thrown if a computed partial correlation is greater than one for some pair of variables.

Pdf.AltSeriesAccuracyLossException 
The magnitude of alternating series sum is too small relative to the sum
of positive terms to permit a reliable accuracy.

ProportionalHazards.ClassificationVariableLimitException 
The Classification Variable limit set by the user through
setUpperBound has been exceeded. 
SelectionRegression.NoVariablesException 
No Variables can enter the model.

StepwiseRegression.CyclingIsOccurringException 
Cycling is occurring.

StepwiseRegression.NoVariablesEnteredException 
No Variables can enter the model.

Copyright © 19702015 Rogue Wave Software
Built October 13 2015.