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. ]. 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. ]. A good introduction to these two methods is in chapter 16 of Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics.

Documentation

Manual: acepack.pdf
Vignette: None available.

Maintainer: Shawn Garbett <shawn.garbett at vanderbilt.edu>

Author(s): Phil Spector, Jerome Friedman, Robert Tibshirani, Thomas Lumley, Shawn Garbett, Jonathan Baron

Install package and any missing dependencies by running this line in your R console:

install.packages("acepack")

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Package acepack
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Version 1.4.1
Published 2016-10-29
License MIT + file LICENSE
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NeedsCompilation yes
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CRAN checks acepack check results
Package source acepack_1.4.1.tar.gz