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

Performs canonical correlation analysis from a data matrix.

Performs canonical correlation analysis from a variance‑covariance matrix or a correlation matrix.

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

Prints a plot of two sample cumulative distribution functions.

Prints a sample cumulative distribution function (CDF), a theoretical CDF, and confidence band information.

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

Constructs an equivalent completely testable multivariate general linear hypothesis HBU = G from a partially testable hypothesis HpBU = Gp.

Computes an upper triangular factorization of a real symmetric nonnegative definite matrix.

Evaluates the chi‑squared cumulative distribution function.

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

Evaluates the inverse of the chi‑squared cumulative distribution function.

Evaluates the chi‑squared probability density function.

Computes a confidence interval on a variance component estimated as proportional to the difference in two mean squares in a balanced complete experimental design.

Performs a hierarchical cluster analysis given a distance matrix.

Calculates and tests the significance of the Kendall coefficient of concordance.

Computes cluster membership for a hierarchical cluster tree.

Computes the variance‑covariance or correlation matrix.

Computes a pooled variance‑covariance matrix from the observations.

Computes the cross periodogram of two stationary time series using a fast Fourier transform.

Returns CPU time used in seconds.

Evaluates the noncentral chi‑squared cumulative distribution function.

Evaluates the inverse of the noncentral chi‑squared cumulative function.

This function evaluates the noncentral chi‑squared probability density function.

Estimates the nonnormalized cross‑spectral density of two stationary time series using a spectral window given the time series data.

Estimates the nonnormalized cross‑spectral density of two stationary time series using a spectral window given the spectral densities and cross periodogram.

Computes cell frequencies, cell means, and cell sums of squares for multivariate data.

Estimates the nonnormalized cross‑spectral density of two stationary time series using a weighted cross periodogram given the time series data.

Estimates the nonnormalized cross‑spectral density of two stationary time series using a weighted cross periodogram given the spectral densities and cross periodogram.

Computes partial association statistics for log‑linear models in a multidimensional contingency table.

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

Computes Fisher’s exact test probability and a hybrid approximation to the Fisher exact test probability for a contingency table using the network algorithm.

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

Computes model estimates and associated statistics for a hierarchical log‑linear model.

Computes model estimates and covariances in a fitted log‑linear model.

Computes exact probabilities in a two‑way contingency table.

Performs generalized Mantel‑Haenszel tests in a stratified contingency table.

Estimates the bivariate normal correlation coefficient using a contingency table.

Computes contrast estimates and sums of squares.

Builds hierarchical log‑linear models using forward selection, backward selection, or stepwise selection.

Performs a chi‑squared analysis of a 2 by 2 contingency table.

Performs a generalized linear least squares analysis of transformed probabilities in a two‑dimensional contingency table.

Performs a Cramer‑von Mises test for normality.

Converts a character string containing an integer number into the corresponding integer form.