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power.transform (version 1.0.0)

Location and Scale Invariant Power Transformations

Description

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.

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Install

install.packages('power.transform')

Monthly Downloads

269

Version

1.0.0

License

EUPL

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Maintainer

Alex Zwanenburg

Last Published

September 13th, 2024

Functions in power.transform (1.0.0)

transformationYeoJohnson-class

Yeo-Johnson transformation object
power_transform

Transform values
ragn

Random Values from the Asymmetric Generalised Normal Distribution
assess_transformation

Assess normality of transformed data
get_residuals

Compute residuals of transformation to normality
plot_residual_plot

Create residual plot
power.transform

power.transform: Transform Data to Normality using Power Transformations
create_transformer_skeleton

Create transformation object skeleton
find_transformation_parameters

Set transformation parameters
huber_estimate

Huber M-estimate
set_lambda

Set lambda value
plot_qq_plot

Create Q-Q plot
get_lambda

Get lambda value
transformationPowerTransform-class

Generic transformation object
set_shift

Set shift value
get_transformation_method

Get transformation method
revert_power_transform

Revert transformation
set_scale

Set scale value
get_scale

Get scale value
transformationNone-class

No transformation object
get_shift

Get shift value
transformationBoxCox-class

Box-Cox transformation object