PROC TRANSREG fits linear models to data. In addition, PROC TRANSREG can find nonlinear transformations of the data and fit a linear model to the transformed data. This is in contrast to PROC REG and PROC GLM, which fit linear models to data, and PROC NLIN, which fits nonlinear models to data. PROC TRANSREG fits a variety of models, including the following:
ordinary regression and ANOVA
metric and nonmetric vector and ideal point preference mapping
simple, multiple, and multivariate regression with optional variable transformations
canonical correlation analysis with optional variable transformations
simple and multiple regression models with a Box-Cox (1964) transformation of the dependent variable
regression models with penalized B-splines (Eilers and Marx, 1996)