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MVN: An R Package for Assessing Multivariate Normality

Overview

The MVN R package provides a comprehensive framework for assessing multivariate normality, a key assumption in many parametric multivariate statistical methods such as:

• MANOVA
• Linear Discriminant Analysis
• Principal Component Analysis
• Canonical Correlation Analysis

Multivariate normality assessment is critical, and no single method is universally optimal. MVN combines several numerical and graphical tools to offer reliable evaluations across diverse scenarios.

Features

• Multivariate Normality Tests:

  • Mardia’s Test
  • Henze-Zirkler’s Test
  • Royston’s Test
  • Doornik-Hansen’s Test
  • Energy Test

• Graphical Diagnostics:

  • Chi-square Q-Q Plots
  • 3D Perspective Plots
  • Contour Plots

• Multivariate Outlier Detection:

  • Robust Mahalanobis distance-based methods

• Univariate Normality Checks:

  • Multiple tests and visualizations for marginal distributions

• Data Transformation Options:

  • Logarithmic
  • Square Root
  • Square
  • Box-Cox (with lambda selection)

• Descriptive Statistics Output

• Subset (grouped) analysis support

Installation

To install the latest version from CRAN:

install.packages("MVN")

To install the development version from GitHub:

devtools::install_github("selcukorkmaz/MVN")

Usage Example

library(MVN)

# Run MVN tests and diagnostics on iris data
result <- mvn(
  data = iris[1:50, 1:3],
  mvn_test = "hz"
)

# View results
summary(result)

Web Application

A web-based graphical user interface is available here: http://biosoft.erciyes.edu.tr/app/MVN

Citation

Korkmaz S, Goksuluk D, Zararsiz G. MVN: An R Package for Assessing Multivariate Normality. The R Journal. 2014; 6(2):151-162. https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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