Computes method of moments estimates of the moving average parameters of an ARMA model.

Exacts maximum likelihood estimation of the parameters in a univariate ARMA (auto‑regressive, moving average) time series model.

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

Computes an upper triangular factorization of a real symmetric matrix A plus a diagonal matrix D, where D is determined sequentially during the Cholesky factorization in order to make A + D nonnegative definite.

Computes a median polish of a two‑way table.

Computes least squares estimates of a linear regression model for a multichannel time series with a specified base channel.

Calculates maximum likelihood estimates for the parameters of one of several univariate probability distributions.

Obtains normalized product‑moment (double centered) matrices from dissimilarity matrices.

Computes distances in a multidimensional scaling model.

Performs individual‑differences multidimensional scaling for metric data using alternating least squares.

Computes initial estimates in multidimensional scaling models.

Transforms dissimilarity/similarity matrices and replace missing values by estimates to obtain standardized dissimilarity matrices.

Computes various stress criteria in multidimensional scaling.

Computes a test for the independence of k sets of multivariate normal variables.

Computes Mardia’s multivariate measures of skewness and kurtosis and tests for multivariate normality.

Moves any rows of a matrix with the IMSL missing value code NaN (not a number) in the specified columns to the last rows of the matrix.

Computes least squares estimates of the multichannel Wiener filter coefficients for two mutually stationary multichannel time series.