Two nonparametric methods for multiple regression transform selection are provided.
The first, Alternating 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.
acepack is an R package that provides two
nonparametric methods for multiple regression transform selection.
The first, Alternating 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 Harrell's "Regression Modeling Strategies" in the Springer Series in Statistics.
This package is based on public domain S and FORTRAN code for AVAS by Tibshirani, and on FORTRAN code for ACE from Statlib, written by Spector and Friedman.
The FORTRAN code has been edited to use double precision, for compatibility with R, and the R code and documentation for ace() have been added by Thomas Lumley, based on that for avas().
Shawn Garbett has refactored with the assistance of ChatGPT to F90 and cleaned up the R interface to current standards.