Skip to content

bestNormalize

1.9.2Package record

Normalizing Transformation Functions

6packages dependsuggested by 2
71.1Kdownloads / year
89.0%test coverage
13/13flavors OK

Overview

About

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

CRAN checks
13OK
Slowest check: 4.3 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-09-08
  • ERROR12 OK, 1 ERROR2026-09-07
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-06-09
Show 4 earlier changesHide 4 earlier changes
  • ERROR12 OK, 1 ERROR2026-06-08
  • OK14 OK2026-03-30
  • ERROR13 OK, 1 ERROR2026-03-28
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 1.9.2
READMEYes · 455 wordsVignettesYes · 2 · dynamicNEWSYes · 100% structured · does not mention 1.9.2Citation fileYes · cites a publicationWebsiteYes · pkgdown
Cites an R Journal article
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

71.1K
CRAN downloads in the past year
Rank #1,690 · ~195/day · ~5.9K/mo
Daily download trend is not available in this view yet.
4.5K30 days
13K90 days
71.1K1 year
Compare downloads with other packages →
Also on595 r2u16 autocran1.9K conda_forge

Repository

43Stars
7Forks
0Open issues
0Open PRs
16Releases
192Commits
5Commit identities
192 commits · Last activity 2026-06-04 · 0% stars, 30d · Default branch at 1.9.3, ahead of CRAN 1.9.2 (checked 2026-10-22)
Responsiveness
How these are sampled
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

2025-08-04 · 412026-07-07 · 43
PracticesBeta

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.

Continuous integration (2)
GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel

Docs source (3)
README.Rmd

GitHub Pages site last deployed 2023-03-21 · The repository has a site deploy workflow

Coverage (1)
Coverage reporting (Codecov)
CRAN release process (2)
cran-comments.mdrevdep/
Show all practicesHide 1 practice
Lint, format, editor (1)
RStudio project
How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
19 external dependencies (excludes base and recommended)

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.

Depends (1)
R >= 3.1.0
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
6direct
0indirect
Imports 6
Suggested by 2
Depends, Imports or LinkingTo, most downloaded first
Suggests, most downloaded first

Datasets

People & History

People (1)
Author, Maintainer · added in 1.9.2

Maintainer since 0.2.2 (2017)

Listed in earlier versions (1)
no longer listed · 0.2.2 to 1.9.1
Releases (20)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.9.2Latest
    record
    2025-11-30 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 1.9.1
    record
    2023-08-18 · diff ↗ ·
    added 1 export
    bestLogConstant
  • R
    R 4.3.0 released · 2023-04-21
  • 1.9.0
    record
    2023-03-15 · diff ↗ ·
    added 2 exports
    double_reverse_log, required_pkgs
  • 1.8.3
    record
    2022-06-13 · diff ↗
  • R
    R 4.2.0 released · 2022-04-22
  • 1.8.2
    record
    2021-09-16 · diff ↗
  • 1.8.1
    record
    2021-09-02 · diff ↗
  • 1.8.0
    record
    2021-06-03 · diff ↗
  • R
    R 4.1.0 released · 2021-05-18
  • 1.7.0
    record
    2021-03-02 · diff ↗ ·
    added 3 exports
    center_scale, step_bestNormalize_new, step_best_normalize
  • 1.6.1
    record
    2020-06-08 · diff ↗
  • 1.6.0
    record
    2020-05-20 · diff ↗
Show 14 earlier eventsHide 14 earlier events

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.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for bestNormalize version 1.9.2 [Data set]. HJJB, LLC. Data release v2026-10-24. https://doi.org/10.5281/zenodo.21843040

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.

Report a problem with this page →

Privacy choices

These apply to this browser and are stored on this device only. Nothing about your choice is sent to us.

Read the privacy policy