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license: MIT
title: 'acepack: ACE and AVAS for Selecting Multiple Regression Transformations'
version: 1.4.2
doi: 10.32614/CRAN.package.acepack
abstract: 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. <https://doi.org/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. <https://doi.org/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.
authors:
- family-names: Garbett
  given-names: Shawn
  email: shawn.garbett@vumc.org
- family-names: Spector
  given-names: Phil
- family-names: Friedman
  given-names: Jerome
- family-names: Tibshirani
  given-names: Robert
- family-names: Lumley
  given-names: Thomas
- family-names: Baron
  given-names: Jonathan
repository: https://CRAN.R-project.org/package=acepack
date-released: '2023-08-22'
contact:
- family-names: Garbett
  given-names: Shawn
  email: shawn.garbett@vumc.org