# Buildsheet autogenerated by ravenadm tool -- Do not edit.

NAMEBASE=		R-acepack
VERSION=		1.4.2
KEYWORDS=		cran
VARIANTS=		standard
SDESC[standard]=	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_1.4.2.tar.gz:main
DIST_SUBDIR=		CRAN
DF_INDEX=		1
SPKGS[standard]=	single

OPTIONS_AVAILABLE=	none
OPTIONS_STANDARD=	none

USES=			cran gmake

DISTNAME=		acepack

GENERATED=		yes

INSTALL_REQ_TOOLCHAIN=	yes

[FILE:1244:descriptions/desc.single]
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. &lt;<a
href="https://doi.org/10.1080%2F01621459.1985.10478157"
target="_top">doi:10.1080/01621459.1985.10478157</a>&gt;]. 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. &lt;<a
href="https://doi.org/10.1080%2F01621459.1988.10478610"
target="_top">doi:10.1080/01621459.1988.10478610</a>&gt;]. A good
introduction to these two methods is in chapter 16 of Frank Harrel's
"Regression Modeling Strategies" in the Springer Series in Statistics.


[FILE:104:distinfo]
5bffcd12b783f372bb6c50e35317744ac31597c91b6433442a7b0dce2f66ac91        37644 CRAN/acepack_1.4.2.tar.gz