power.transform: Location and Scale Invariant Power Transformations

Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations allow for transforming variables with distributions distant from 0 to normality. Transformers are implemented as S4 objects. These allow for transforming new instances to normality after optimising fitting parameters on other data. A test for central normality allows for rejecting transformations that fail to produce a suitably normal distribution, independent of sample number.

Version:	1.0.0
Depends:	R (≥ 2.10)
Imports:	data.table, methods, rlang (≥ 1.0.0), nloptr
Suggests:	ggplot2 (≥ 3.4.0), testthat (≥ 3.0.0)
Published:	2024-09-13
DOI:	10.32614/CRAN.package.power.transform
Author:	Alex Zwanenburg [aut, cre], Steffen Löck [aut], German Cancer Research Center (DKFZ) [cph]
Maintainer:	Alex Zwanenburg <alexander.zwanenburg at nct-dresden.de>
BugReports:	https://github.com/oncoray/power.transform/issues
License:	EUPL
URL:	https://github.com/oncoray/power.transform
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	power.transform results

Documentation:

Reference manual:

power.transform.pdf

Downloads:

Package source:	power.transform_1.0.0.tar.gz
Windows binaries:	r-devel: power.transform_1.0.0.zip, r-release: power.transform_1.0.0.zip, r-oldrel: power.transform_1.0.0.zip
macOS binaries:	r-release (arm64): power.transform_1.0.0.tgz, r-oldrel (arm64): power.transform_1.0.0.tgz, r-release (x86_64): power.transform_1.0.0.tgz, r-oldrel (x86_64): power.transform_1.0.0.tgz

Reverse dependencies:

Reverse suggests:

familiar

Linking:

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