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.

AuthorPhil Spector, Jerome Friedman, Robert Tibshirani, Thomas Lumley, Shawn Garbett, Jonathan Baron
Date of publication2016-10-29 00:11:52
MaintainerShawn Garbett <shawn.garbett@vanderbilt.edu>
LicenseMIT + file LICENSE
Version1.4.1

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Files

acepack
acepack/inst
acepack/inst/README.avas
acepack/inst/ace.doc
acepack/inst/README
acepack/inst/README.ace
acepack/tests
acepack/tests/testthat.R
acepack/tests/testthat
acepack/tests/testthat/test_transform.R
acepack/src
acepack/src/ace.f
acepack/src/rlsmo.f
acepack/src/avas.f
acepack/NAMESPACE
acepack/NEWS
acepack/R
acepack/R/acepack.R acepack/R/zzz.R
acepack/MD5
acepack/DESCRIPTION
acepack/LICENSE.note
acepack/man
acepack/man/avas.Rd acepack/man/ace.Rd
acepack/LICENSE

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