IMSL C# Numerical Library

Imsl.Stat Namespace

Statistical classes

Namespace hierarchy

Classes

Class Description
AllDeletedException There are no observations.
AllMissingException There are no observations.
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. Missing values can be replaced by these estimates prior to fitting a time series using the ARMA class.
ARMAMaxLikelihood Computes maximum likelihood estimates of parameters for an ARMA model with p and q autoregressive and moving average terms respectively.
ARSeasonalFit Estimates the optimum seasonality parameters for a time series using an autoregressive model, AR(p), to represent the time series.
AutoCorrelation Computes the sample autocorrelation function of a stationary time series.
BadVarianceException The input variance is not in the allowed range.
CategoricalGenLinModel Analyzes categorical data using logistic, probit, Poisson, and other linear models.
Cdf Cumulative probability distribution functions, probability density functions, and their inverses.
ChiSquaredTest Chi-squared goodness-of-fit test.
ClassificationVariableException The ClassificationVariable vector has not been initialized.
ClassificationVariableLimitException The Classification Variable limit set by the user through setUpperBound has been exceeded.
ClassificationVariableValueException The number of distinct values for each Classification Variable must be greater than 1.
ClusterHierarchical Performs a hierarchical cluster analysis from a distance matrix.
ClusterKMeans Perform a K-means (centroid) cluster analysis.
ClusterNoPointsException There is a cluster with no points.
ConstrInconsistentException The equality constraints are inconsistent.
ContingencyTable Performs a chi-squared analysis of a two-way contingency table.
Covariances Computes the sample variance-covariance or correlation matrix.
CovarianceSingularException The variance-Covariance matrix is singular.
CrossCorrelation Computes the sample cross-correlation function of two stationary time series.
CyclingIsOccurringException Cycling is occurring.
DeleteObservationsException The number of observations to be deleted (set by setObservationMax) has grown too large.
DidNotConvergeException The iteration did not converge.
Difference Differences a seasonal or nonseasonal time series.
DiffObsDeletedException Different observations are being deleted from return matrix than were originally entered.
DiscriminantAnalysis Performs a linear or a quadratic discriminant function analysis among several known groups and the use of either reclassification, split sample, or the leaving-out-one methods in order to evaluate the rule.
Dissimilarities Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.
EigenvalueException An error occured in calculating the eigenvalues of the adjusted (inverse) covariance matrix. Check the input covariance matrix.
EmpiricalQuantiles Computes empirical quantiles.
EmptyGroupException There are no observations in a group. Cannot compute statistics.
EqConstrInconsistentException The equality constraints and the bounds on the variables are found to be inconsistent.
FactorAnalysis Performs Principal Component Analysis or Factor Analysis on a covariance or correlation matrix.
FaureSequence Generates the low-discrepancy Faure sequence.
GARCH Computes estimates of the parameters of a GARCH(p,q) model.
IllConditionedException The problem is ill-conditioned.
IncreaseErrRelException The bound for the relative error is too small.
InitialMAException The initial values for the moving average parameters are not invertable. Execution is halted.
InverseCdf Inverse of user-supplied cumulative distribution function.
KalmanFilter Performs Kalman filtering and evaluates the likelihood function for the state-space model.
KolmogorovOneSample The class KolmogorovOneSample performs a Kolmogorov-Smirnov goodness-of-fit test in one sample.
KolmogorovTwoSample Performs a Kolmogorov-Smirnov two-sample test.
LinearRegression Fits a multiple linear regression model with or without an intercept.
LinearRegression.CaseStatistics 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.
LinearRegression.CoefficientTTestsValue CoefficientTTestsValue contains statistics related to the regression coefficients.
MatrixSingularException The input matrix is singular.
MersenneTwister A 32-bit Mersenne Twister generator.
MersenneTwister64 A 64-bit Mersenne Twister generator.
MoreObsDelThanEnteredException 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).
MultiCrossCorrelation Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.
MultipleComparisons Performs Student-Newman-Keuls multiple comparisons test.
NegativeFreqException A negative frequency was encountered.
NegativeWeightException A negative weight was encountered.
NewInitialGuessException The iteration has not made good progress.
NoConvergenceException Convergence did not occur within the maximum number of iterations.
NoDegreesOfFreedomException No degrees of freedom error.
NonInvertibleException The solution is noninvertible.
NonlinearRegression Fits a multivariate nonlinear regression model using least squares.
NonPositiveEigenvalueException Maximum number of iterations exceeded.
NonPosVarianceException The problem is ill-conditioned.
NonPosVarianceXYException The problem is ill-conditioned.
NonStationaryException The solution is nonstationary.
NoPositiveVarianceException No variable has positive variance. The Mahalanobis distances cannot be computed.
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.
NotCDFException The function is not a Cumulative Distribution Function (CDF).
NotPositiveDefiniteException Covariance matrix is not positive definite.
NotPositiveSemiDefiniteException Covariance matrix is not positive semi-definite.
NotSemiDefiniteException Hessian matrix is not semi-definite.
NoVariablesEnteredException No Variables can enter the model.
NoVariablesException No variables can enter the model.
NoVariationInputException There is no variation in the input data.
NoVectorXException No vector X satisfies all of the constraints.
PooledCovarianceSingularException The pooled variance-Covariance matrix is singular.
Random Generate uniform and non-uniform random number distributions.
RankException Rank of covariance matrix error.
Ranks Compute the ranks, normal scores, or exponential scores for a vector of observations.
RegressorsForGLM Generates regressors for a general linear model.
ScaleFactorZeroException The computations cannot continue because a scale factor is zero.
SelectionRegression Selects the best multiple linear regression models.
SelectionRegression.SummaryStatistics SummaryStatistics contains statistics related to the regression coefficients.
SignTest Performs a sign test.
SingularException Covariance matrix is singular.
SingularTriangularMatrixException Triangular matrix is singular.
Sort A collection of sorting functions.
StepwiseRegression Builds multiple linear regression models using forward selection, backward selection, or stepwise selection.
StepwiseRegression.CoefficientTTestsValue CoefficientTTestsValue contains statistics related to the student-t test, for each regression coefficient.
Summary Computes basic univariate statistics.
SumOfWeightsNegException The sum of the weights have become negative.
TableMultiWay Tallies observations into a multi-way frequency table.
TableMultiWay.TableBalanced Tallies the number of unique values of each variable.
TableMultiWay.TableUnbalanced Tallies the frequency of each cell in x.
TableOneWay Tallies observations into a one-way frequency table.
TableTwoWay Tallies observations into a two-way frequency table.
TooManyCallsException The number of calls to the function has exceeded the maximum number of iterations times the number of moving average (MA) parameters+1.
TooManyFunctionEvaluationsException Maximum number of function evaluations exceeded.
TooManyIterationsException Maximum number of iterations exceeded.
TooManyJacobianEvalException Maximum number of Jacobian evaluations exceeded.
TooManyObsDeletedException More observations have been deleted than were originally entered (the sum of frequencies has become negative).
UserBasisRegression Generates summary statistics using user-supplied functions in a nonlinear regression model.
VarsDeterminedException The variables are determined by the equality constraints.
WilcoxonRankSum Performs a Wilcoxon rank sum test.
ZeroNormException The computations cannot continue because the Euclidean norm of the column is equal to zero.

Interfaces

Interface Description
ICdfFunction Interface for the user-supplied cumulative distribution function to be used by InverseCdf and ChiSquaredTest.
IRandomSequence Interface implemented by generators of random or quasi-random multidimension sequences.
IRegressionBasis Interface for user supplied function to UserBasisRegression object.
NonlinearRegression.IDerivative Public interface for the user supplied function to compute the derivative for NonlinearRegression.
NonlinearRegression.IFunction Public interface for the user supplied function for NonlinearRegression.
Random.BaseGenerator Base pseudorandom number.

Enumerations

Enumeration Description
ANOVAFactorial.ErrorCalculation ErrorCalculation members indicate whether interaction effects are pooled into the error or not.
ARAutoUnivariate.ParamEstimation Parameter Estimation procedures.
ARMA.ParamEstimation Parameter Estimation procedures.
ARMAEstimateMissing.MissingValueEstimation Missing value estimation methods.
ARSeasonalFit.CenterMethod Methods for centering the input time series.
AutoCorrelation.StdErr Standard Error computation method.
CategoricalGenLinModel.DistributionParameterModel Indicates the function used to model the distribution parameter.
Covariances.MatrixType Specifies the type of matrix to be computed.
CrossCorrelation.StdErr Standard Error computation method.
DiscriminantAnalysis.Classification Classification Method.
DiscriminantAnalysis.CovarianceMatrix Covariance Matrix type.
DiscriminantAnalysis.Discrimination Discrimination Methods.
DiscriminantAnalysis.PriorProbabilities Prior probabilities type.
FactorAnalysis.MatrixType Matrix type.
FactorAnalysis.Model Model type.
Ranks.Tie Determines how to break a tie.
RegressorsForGLM.DummyType Dummy variable types.
SelectionRegression.Criterion Criterion Methods.
StepwiseRegression.Direction Direction indicator.