bestNormalize: Normalizing Transformation Functions
Estimate a suite of normalizing transformations, including
a new adaptation of a technique based on ranks which can guarantee
normally distributed transformed data if there are no ties: ordered
quantile normalization (ORQ). ORQ normalization combines a rank-mapping
approach with a shifted logit approximation that allows
the transformation to work on data outside the original domain. It is
also able to handle new data within the original domain via linear
interpolation. The package is built to estimate the best normalizing
transformation for a vector consistently and accurately. It implements
the Box-Cox transformation, the Yeo-Johnson transformation, three types
of Lambert WxF transformations, and the ordered quantile normalization
transformation. It estimates the normalization efficacy of other
commonly used transformations, and it allows users to specify
custom transformations or normalization statistics. Finally, functionality
can be integrated into a machine learning workflow via recipes.
| Version: |
1.8.3 |
| Depends: |
R (≥ 3.1.0) |
| Imports: |
LambertW (≥ 0.6.5), nortest, dplyr, doParallel, foreach, doRNG, recipes, tibble, methods, butcher, purrr |
| Suggests: |
knitr, rmarkdown, MASS, testthat, mgcv, parallel, ggplot2, scales, rlang, covr |
| Published: |
2022-06-13 |
| Author: |
Ryan Andrew Peterson
 [aut, cre] |
| Maintainer: |
Ryan Andrew Peterson <ryan.a.peterson at cuanschutz.edu> |
| License: |
GPL-3 |
| URL: |
https://petersonr.github.io/bestNormalize/,
https://github.com/petersonR/bestNormalize |
| NeedsCompilation: |
no |
| Citation: |
bestNormalize citation info |
| Materials: |
README NEWS |
| CRAN checks: |
bestNormalize results |
Documentation:
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