There are no observations.

There are no observations.

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

Analyzes a one-way classification model with covariates.

Analysis of Variance table and related statistics.

Analyzes a balanced factorial design with fixed effects.

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

Computes least-square estimates of parameters for an ARMA model.

Estimates missing values in a time series collected with equal spacing. Missing values can be replaced by these estimates prior to fitting a time series using the ARMA class.

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

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. This class also allows computation of forecasts.

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

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.

Computes the sample autocorrelation function of a stationary time series.

The input variance is not in the allowed range.

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

Cumulative probability distribution functions.

Chi-squared goodness-of-fit test.

The ClassificationVariable vector has not been initialized.

The Classification Variable limit set by the user through setUpperBound has been exceeded.

The number of distinct values for each Classification Variable must be greater than 1.

Performs a hierarchical cluster analysis from a distance matrix.

Perform a K-means (centroid) cluster analysis.

There is a cluster with no points.

The equality constraints are inconsistent.

Performs a chi-squared analysis of a two-way contingency table.

Computes the sample variance-covariance or correlation matrix.

The variance-Covariance matrix is singular.

The variance-Covariance matrix is singular.

The variance-Covariance matrix is singular.

Computes the sample cross-correlation function of two stationary time series.

Cycling is occurring.

The number of observations to be deleted (set by setObservationMax) has grown too large.

The iteration did not converge.

Differences a seasonal or nonseasonal time series.

Different observations are being deleted from return matrix than were originally entered.

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

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

An error occured in calculating the eigenvalues of the adjusted (inverse) covariance matrix. Check the input covariance matrix.

Computes empirical quantiles.

There are no observations in a group. Cannot compute statistics.

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

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

Generates the low-discrepancy Faure sequence.

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

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

The problem is ill-conditioned.

The bound for the relative error is too small.

The initial values for the moving average parameters are not invertable. Execution is halted.

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

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

Inverse cumulative probability distribution functions.

Inverse of user-supplied cumulative distribution function.

Performs Kalman filtering and evaluates the likelihood function for the state-space model.

Computes the Kaplan-Meier reliability function estimates or the CDF based on failure data that may be multi-censored.

Computes Kaplan-Meier (or product-limit) estimates of survival probabilities for a sample of failure times that possibly contain right consoring.

The class KolmogorovOneSample performs a Kolmogorov-Smirnov goodness-of-fit test in one sample.

Performs a Kolmogorov-Smirnov two-sample test.

Performs lack-of-fit test for a univariate time series or transfer function given the appropriate correlation function.

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.

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

Inner Class CaseStatistics allows for the computation of predicted values, confidence intervals, and diagnostics for detecting outliers and cases that greatly influence the fitted regression.

CoefficientTTestsValue contains statistics related to the regression coefficients.

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

The input matrix is singular.

A 32-bit Mersenne Twister generator.

A 64-bit Mersenne Twister generator.

More observations are being deleted from the output covariance matrix than were originally entered (the corresponding row, column of the incidence matrix is less than zero).

Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.

Performs Student-Newman-Keuls multiple comparisons test.

A negative frequency was encountered.

A negative weight was encountered.

The iteration has not made good progress.

No appropriate ARIMA model could be found.

Convergence did not occur within the maximum number of iterations.

No degrees of freedom error.

The solution is noninvertible.

Fits a multivariate nonlinear regression model using least squares.

Maximum number of iterations exceeded.

The problem is ill-conditioned.

The problem is ill-conditioned.

The solution is nonstationary.

No variable has positive variance. The Mahalanobis distances cannot be computed.

The algorithm is not making any progress. Try a new initial guess.

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

Performs a test for normality.

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

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

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

Covariance matrix is not positive definite.

Covariance matrix is not positive semi-definite.

Hessian matrix is not semi-definite.

No Variables can enter the model.

No variables can enter the model.

There is no variation in the input data.

No vector X satisfies all of the constraints.

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

Probability density functions.

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

The pooled variance-Covariance matrix is singular.

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

Generate uniform and non-uniform random number distributions.

The model has been determined to be rank deficient.

Rank of covariance matrix error.

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

Generates regressors for a general linear model.

The computations cannot continue because a scale factor is zero.

Selects the best multiple linear regression models.

SummaryStatistics contains statistics related to the regression coefficients.

Performs a sign test.

Covariance matrix is singular.

Triangular matrix is singular.

A collection of sorting functions.

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

CoefficientTTestsValue contains statistics related to the student-t test, for each regression coefficient.

Computes basic univariate statistics.

The sum of the weights have become negative.

Tallies observations into a multi-way frequency table.

Tallies the number of unique values of each variable.

Tallies the frequency of each cell in x.

Tallies observations into a one-way frequency table.

Tallies observations into a two-way frequency table.

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

Maximum number of function evaluations exceeded.

Maximum number of iterations exceeded.

The maximum number of iterations was exceeded, increase maximum iterations or try a different parameter estimation method.

Maximum number of Jacobian evaluations exceeded.

More observations have been deleted than were originally entered (the sum of frequencies has become negative).

Generates summary statistics using user-supplied functions in a nonlinear regression model.

The variables are determined by the equality constraints.

Performs a Wilcoxon rank sum test.

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

Interface for the user-supplied cumulative distribution function to be used by InverseCdf and ChiSquaredTest.

Public interface for the user-supplied distribution function.

Public interface for a user-supplied probability distribution.

Interface implemented by generators of random or quasi-random multidimension sequences.

Interface for user supplied function to UserBasisRegression object.

Public interface for the user supplied function to compute the derivative for NonlinearRegression.

Public interface for the user supplied function for NonlinearRegression.

Base pseudorandom number.

Compute option.

ErrorCalculation members indicate whether interaction effects are pooled into the error or not.

Parameter Estimation procedures.

Parameter Estimation procedures.

Missing value estimation methods.

Methods for centering the input time series.

Indicates which information criterion is used in the optimum model search.

Standard Error computation method.

Indicates the function used to model the distribution parameter.

Specifies the type of linkage.

Specifies the type of transformation.

Specifies the type of matrix to be computed.

Standard Error computation method.

Classification method.

Covariance matrix type.

Discrimination methods.

Prior probabilities type.

Specifies the type of distance method.

Specifies the type of scaling.

Matrix type.

Model type.

Tie handling options.

Determines how to break a tie.

Dummy variable types.

Criterion Methods.

Direction indicator.