Normalizing Transformation Functions
Overview
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.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
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- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-30
- ERROR13 OK, 1 ERROR2026-03-28
- OK14 OK2026-03-10
Documentation
- Help pages
- 19 (1 internal)includes a package overview page
- Examples that run
- 93%13 run in a plain check · 1 never run
- Exported functions with examples
- 88%
- Documented parameters
- 96%
- Return-value docs
- 94%
- Help pages citing references
- 26%
Downloads
Repository
- PRs merged (all time)
- 100%
- Median days to close, 50 most recently updated closed issues
- 5
- Median days to close, 50 most recently updated closed PRs
- 2
Stars over time
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Releases over time
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PRs over time
9 development-tooling and community-health practices detected across 5 families in the upstream repository
Checks run against github.com/petersonr/bestnormalize on 2026-10-22.
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GitHub Pages site last deployed 2023-03-21 · The repository has a site deploy workflow
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Dependencies
Installing bestNormalize adds 71 packages (33 with compiled code, 47 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Maintainer since 0.2.2 (2017)
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.9.2Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.9.1
- RR 4.3.0 released · 2023-04-21
- 1.9.0
- 1.8.32022-06-13 · diff ↗
- RR 4.2.0 released · 2022-04-22
- 1.8.22021-09-16 · diff ↗
- 1.8.12021-09-02 · diff ↗
- 1.8.02021-06-03 · diff ↗
- RR 4.1.0 released · 2021-05-18
- 1.7.0
- 1.6.12020-06-08 · diff ↗
- 1.6.02020-05-20 · diff ↗
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Package metadata
- First published
- 2017-11-14
- Total releases
- 20 / 9 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.1.0
- Bundled data
- 158 KB / 1 file
- Download size
- 1.1 MB
Cite
Cite this package
Run in R for the authors' entry:
citation("bestNormalize")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.32614/rj-2021-041, 10.1080/02664763.2019.1630372.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.