Analyzes a balanced complete experimental design for a fixed, random, or mixed model.

Analyzes a balanced incomplete block design or a balanced lattice design.

Computes the sample autocorrelation function of a stationary time series.

Returns a character given its ASCII value

Produces population and cohort life tables.

Performs an Anderson‑Darling test for normality.

Evaluates the cumulative distribution function of the one‑sided Kolmogorov‑Smirnov goodness‑of‑fit D+ or D− test statistic based on continuous data for one sample.

Evaluates the cumulative distribution function of the Kolmogorov‑Smirnov goodness‑of‑fit D test statistic based on continuous data for two samples

Analyzes a Latin square design.

Evaluates the lognormal cumulative probability distribution function.

This function evaluates the inverse of the lognormal cumulative probability distribution function.

Evaluates the lognormal probability density function.

Retrieves machine constants.

Evaluates Mill's ratio (the ratio of the ordinate to the upper tail area of the standardized normal distribution).

Analyzes a completely nested random model with possibly unequal numbers in the subgroups.

Evaluates the normal probability density function.

Evaluates the standard normal (Gaussian) cumulative distribution function.

Evaluates the inverse of the standard normal (Gaussian) cumulative distribution function.

Analyzes a balanced n‑way classification model with fixed effects.

Analyzes a one‑way classification model with covariates.

Analyzes a one‑way classification model.

Calculates the rational power spectrum for an ARMA model.

Computes method of moments estimates of the autoregressive parameters of an ARMA model.

Analyzes a randomized block design or a two‑way balanced design.

Automatically identifies time series outliers, determines parameters of a multiplicative seasonal ARIMA (p,0,q) × (0,d,0)s model, and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.

Automatic selection and fitting of a multivariate autoregressive time series model using Akaike’s Multivariate Final Prediction Error (MFPE) criteria.

Automatic selection and fitting of a univariate autoregressive time series model using Akaike’s Final Prediction Error (FPE) criteria.

Automatic selection and fitting of a multivariate autoregressive time series model.

Estimates structural breaks in non‑stationary univariate time series.

Automatic selection and fitting of a multivariate autoregressive time series model.