Multivariate Normality Tests
Atkinson–Riani–Welsh (ARW) Adjusted Cutoff for Robust Mahalanobis Dist...
Descriptive Statistics for Numeric Data
Doornik-Hansen Test for Multivariate Normality
E-Statistic Test for Multivariate Normality (Energy Test)
Henze-Wagner High-Dimensional Test for Multivariate Normality
Henze-Zirkler Test for Multivariate Normality
Impute Missing Values
Mardia's Test for Multivariate Normality
Plot Multivariate Normal Diagnostics and Bivariate Kernel Density
Identify Multivariate Outliers via Robust Mahalanobis Distances
Comprehensive Multivariate Normality and Diagnostic Function
Plot Diagnostics for Multivariate Normality Analysis
Apply Power Transformation to Numeric Data
Royston's Multivariate Normality Test
Launch the MVN Shiny application
Summarize Multivariate Normality Analysis Results
Univariate Normality Tests
Diagnostic Plots for Univariate and Multivariate Data
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/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>.
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