Package: acepack
Maintainer: Shawn Garbett <shawn.garbett@vumc.org>
Version: 1.4.2
Author: Phil Spector, Jerome Friedman, Robert Tibshirani, Thomas Lumley, Shawn Garbett, Jonathan Baron
Description: 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.
Title: ACE and AVAS for Selecting Multiple Regression Transformations
License: MIT + file LICENSE
Suggests: testthat
Packaged: 2023-08-21 17:30:03 UTC; garbetsp
Repository: CRAN
Date/Publication: 2023-08-22 09:10:02 UTC
NeedsCompilation: yes
Built: R 4.2.0; aarch64-apple-darwin20; 2023-08-27 02:10:11 UTC; unix
Archs: acepack.so.dSYM
