Interface | Description |
---|---|
CdfFunction |
Public interface for the user-supplied cumulative distribution function
to be used by InverseCdf and ChiSquaredTest.
|
Distribution |
Public interface for the user-supplied 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 user-supplied probability distribution.
|
Random.BaseGenerator |
Base pseudorandom number.
|
RandomSequence |
Interface implemented by generators of random or quasi-random
multidimensional sequences.
|
RegressionBasis |
Public interface for user supplied function to
UserBasisRegression object. |
TimeSeriesOperations.Function |
Public interface for the user-supplied function that defines how to
combine two synchronous time series values.
|
Class | Description |
---|---|
ANCOVA |
Analyzes a one-way 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 least-square 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 |
Chi-squared goodness-of-fit test.
|
ClusterHierarchical |
Performs a hierarchical cluster analysis from a distance matrix.
|
ClusterKMeans |
Perform a K-means (centroid) cluster analysis.
|
ClusterKNN |
Perform a k-Nearest Neighbor classification.
|
ContingencyTable |
Performs a chi-squared analysis of a two-way contingency table.
|
Covariances |
Computes the sample variance-covariance or correlation matrix.
|
CrossCorrelation |
Computes the sample cross-correlation 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 low-discrepancy 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 Holt-Winters
Multiplicative or Additive forecasting method for seasonal data.
|
InvCdf |
Inverse cumulative probability distribution functions.
|
InverseCdf |
Inverse of user-supplied cumulative distribution function.
|
KalmanFilter |
Performs Kalman filtering and evaluates the likelihood function for the
state-space model.
|
KaplanMeierECDF |
Computes the Kaplan-Meier reliability function estimates or the CDF based on
failure data that may be multi-censored.
|
KaplanMeierEstimates |
Computes Kaplan-Meier (or product-limit) estimates of survival probabilities
for a sample of failure times that possibly contain right consoring.
|
KolmogorovOneSample |
The class
KolmogorovOneSample performs a Kolmogorov-Smirnov
goodness-of-fit test in one sample. |
KolmogorovTwoSample |
Performs a Kolmogorov-Smirnov two-sample test.
|
LackOfFit |
Performs lack-of-fit 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 32-bit Mersenne Twister generator.
|
MersenneTwister64 |
A 64-bit Mersenne Twister generator.
|
MultiCrossCorrelation |
Computes the multichannel cross-correlation function of two mutually
stationary multichannel time series.
|
MultipleComparisons |
Performs Student-Newman-Keuls 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 non-uniform 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 multi-way frequency table.
|
TableOneWay |
Class
TableOneWay calculates a frequency table for a data array. |
TableTwoWay |
Class
TableTwoWay calculates a two-dimensional 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 ill-conditioned.
|
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 ill-conditioned.
|
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 ill-conditioned.
|
DiscriminantAnalysis.CovarianceSingularException |
The variance-covariance 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 semi-definite.
|
FactorAnalysis.NotSemiDefiniteException |
Hessian matrix not semi-definite.
|
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 ill-conditioned.
|
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 © 1970-2015 Rogue Wave Software
Built October 13 2015.