Package: acepack
Version: 1.6.3
Authors@R: c(person(given = "Phil",
                      family = "Spector",
                      role = "aut"),
               person(given = "Jerome",
                      family = "Friedman",
                      role = "aut"),
               person(given = "Robert",
                      family = "Tibshirani",
                      role = "aut"),
               person(given = "Thomas",
                      family = "Lumley",
                      role = "aut"),
               person("Shawn", "Garbett", email = "shawn.garbett@vumc.org",
                      comment = c(ORCID="0000-0003-4079-5621"),
                      role = c("cre","aut")),
               person(given = "Jonathan",
                      family = "Baron",
                      role = "aut"),
               person("Bernhard", "Klar",
                      email = "bernhard.klar@kit.edu",
                      role = "aut"),
               person("Scott", "Chasalow",
                      email = "Scott.Chasalow@users.pv.wau.nl",
                      role = "aut")
             )
Description: Two nonparametric methods for multiple regression
        transform selection are provided. The first, Alternating
        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 Harrell's
        "Regression Modeling Strategies" in the Springer Series in
        Statistics. A permutation independence test is included from
        [Holzmann, H., Klar, B. 2025. "Lancaster correlation - a new
        dependence measure linked to maximum correlation". Scandinavian
        Journal of Statistics. 52(1):145-169 <doi:10.1111/sjos.12733>].
Title: ACE and AVAS for Selecting Multiple Regression Transformations
License: MIT + file LICENSE
Suggests: testthat, roxygen2
NeedsCompilation: yes
RoxygenNote: 7.3.2
Encoding: UTF-8
Packaged: 2026-07-04 13:41:09 UTC; root
Author: Phil Spector [aut], Jerome Friedman [aut], Robert Tibshirani
        [aut], Thomas Lumley [aut], Shawn Garbett [cre, aut] (ORCID:
        <https://orcid.org/0000-0003-4079-5621>), Jonathan Baron [aut],
        Bernhard Klar [aut], Scott Chasalow [aut]
Maintainer: Shawn Garbett <shawn.garbett@vumc.org>
Repository: https://spgarbet.r-universe.dev
Date/Publication: 2025-08-29 14:30:08 UTC
RemoteUrl: https://github.com/cran/acepack
RemoteRef: HEAD
RemoteSha: d15fc8b0069775e772ba94a3318139ae9c9e2a9d