MVN: Multivariate Normality Tests
A comprehensive suite for assessing multivariate normality using six statistical tests
(Mardia, Henze–Zirkler, Henze–Wagner, Royston, Doornik–Hansen, Energy).
Also includes univariate diagnostics, bivariate density visualization, robust outlier
detection, power transformations (e.g., Box–Cox, Yeo–Johnson), and imputation strategies
("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported
for selected tests to improve p-value accuracy in small samples.
Diagnostic plots are available via both 'ggplot2' and interactive 'plotly' visualizations. See Korkmaz et al. (2014) <https://journal.r-project.org/articles/RJ-2014-031/RJ-2014-031.pdf>.
| Version: |
6.3 |
| Imports: |
methods, nortest, moments, MASS, boot, car, dplyr, tidyr, purrr, stringr, tibble, ggplot2, viridis, cli, energy, plotly, mice |
| Suggests: |
DT, bslib, future, haven, jsonlite, readxl, shiny, yaml, promises, testthat (≥ 3.0.0), zip |
| Published: |
2026-01-13 |
| DOI: |
10.32614/CRAN.package.MVN |
| Author: |
Selcuk Korkmaz 
[aut, cre],
Dincer Goksuluk [aut],
Gokmen Zararsiz [aut] |
| Maintainer: |
Selcuk Korkmaz <selcukorkmaz at gmail.com> |
| BugReports: |
https://github.com/selcukorkmaz/MVN/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://biosoft.shinyapps.io/mvn-shiny-app/,
https://selcukorkmaz.github.io/mvn-tutorial/,
https://github.com/selcukorkmaz/MVN |
| NeedsCompilation: |
no |
| Citation: |
MVN citation info |
| Materials: |
README |
| CRAN checks: |
MVN results |
Documentation:
Downloads:
Reverse dependencies:
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