acepack
Thomas Lumley <tlumley@u.washington.edu>
1.3
Phil Spector, Jerome Friedman, Robert Tibshirani, Thomas Lumley
ACE and AVAS methods for choosing regression transformations.
ace() and avas() for selecting regression transformations
avas is public domain, ace is on Statlib

Phil Spector, Jerome Friedman, Robert Tibshirani, Thomas Lumley, Shawn Garbett, Jonathan Baron
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>].
ACE and AVAS for Selecting Multiple Regression Transformations

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.