| PackageDescription | Two nonparametric methods for multiple regression transform selection
are provided. The first, Alternative Conditional Expectations (ACE), is
an algorithm to find the fixed point of maximal correlation, i.e. it
finds a set of transformed response variables that maximizes R^2 using
smoothing functions [see Breiman, L., and J.H. Friedman. 1985.
"Estimating Optimal Transformations for Multiple Regression and
Correlation". Journal of the American Statistical Association.
80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is the
Additivity Variance Stabilization (AVAS) method which works better than
ACE when correlation is low [see Tibshirani, R.. 1986. "Estimating
Transformations for Regression via Additivity and Variance
Stabilization". Journal of the American Statistical Association.
83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction
to these two methods is in chapter 16 of Frank Harrel's "Regression
Modeling Strategies" in the Springer Series in Statistics. |