Generates regressors for a general linear model

Fits a multiple linear regression model and optionally produces summary statistics for a regression model

Computes predicted values, confidence intervals, and diagnostics

All best regressions

Stepwise regression

Fits a polynomial regression model

Computes predicted values, confidence intervals, and diagnostics

Fits a nonlinear regression model.

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

Computes the matrix of sums of squares and cross products for the multivariate general linear hypothesis HbU = G given the regression fit.

Performs tests for a multivariate general linear hypothesis HbU = G given the hypothesis sums of squares and cross products matrix SH.

Fit a nonlinear regression model using Powell's algorithm.

LAV, Lpnorm, and LMV criteria regression