Given an input array of deviate values, generates a canonical correlation array.

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

Evaluates the chi-squared distribution function.

Evaluates the inverse of the chi-squared distribution function.

Performs a chi-squared test for normality.

Performs a chi-squared goodness-of-fit test.

Performs a hierarchical cluster analysis given a distance matrix.

Performs a K-means (centroid) cluster analysis.

Computes cluster membership for a hierarchical cluster tree.

Performs a Cochran Q test for related observations.

Calculates the complement of the chi-squared distribution.

Calculates the complement of the F distribution function.

Evaluates the complementary noncentral F cumulative distribution function (CDF).

Calculates the complement of the Student's t distribution function.

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

Sets up a table to generate pseudorandom numbers from a general continuous distribution.

Computes the sample variance-covariance or correlation matrix.

Performs the Cox and Stuart’ sign test for trends in location and dispersion.

Analyzes data from balanced and unbalanced completely randomized experiments.

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

Releases NVIDIA memory allocated by the IMSL C Numerical Library.

Gets parameters used by the specified function to determine if the NVIDIA CUDA Toolkit algorithm will be used.

Sets parameters used by the specified function to determine if the NVIDIA CUDA Toolkit algorithm will be used.

Performs a Cramer-von-Mises test for normality.