# Buildsheet autogenerated by ravenadm tool -- Do not edit. NAMEBASE= R-acepack VERSION= 1.6.3 KEYWORDS= cran VARIANTS= std SDESC[std]= ACE and AVAS multiple regression transformations HOMEPAGE= none CONTACT= CRAN_Automaton[cran@ironwolf.systems] DOWNLOAD_GROUPS= main SITES[main]= CRAN/src/contrib https://loki.dragonflybsd.org/cranfiles/ DISTFILE[1]= acepack_1.6.3.tar.gz:main DIST_SUBDIR= CRAN DF_INDEX= 1 SPKGS[std]= single OPTIONS_AVAILABLE= none OPTIONS_STANDARD= none USES= cran gmake DISTNAME= acepack GENERATED= yes INSTALL_REQ_TOOLCHAIN= yes [FILE:1553:descriptions/desc.single] acepack: ACE and AVAS for Selecting Multiple Regression Transformations 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>]. [FILE:104:distinfo] 653093e308f0dea5ec2719985a01aff700d5795074b3f5239b34632bf45ebadf 35206 CRAN/acepack_1.6.3.tar.gz