acepack: 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.

Version: 1.4.1
Suggests: testthat
Published: 2016-10-29
Author: Phil Spector, Jerome Friedman, Robert Tibshirani, Thomas Lumley, Shawn Garbett, Jonathan Baron
Maintainer: Shawn Garbett <shawn.garbett at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: NEWS
In views: SocialSciences
CRAN checks: acepack results


Reference manual: acepack.pdf
Package source: acepack_1.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: acepack_1.4.1.tgz
OS X Mavericks binaries: r-oldrel: acepack_1.4.1.tgz
Old sources: acepack archive

Reverse dependencies:

Reverse depends: nlts
Reverse imports: Hmisc


Please use the canonical form to link to this page.