A comprehensive suite for assessing multivariate normality using six statistical tests (Mardia, HenzeâZirkler, HenzeâWagner, Royston, DoornikâHansen, Energy). Also includes univariate diagnostics, bivariate density visualization, robust outlier detection, power transformations (e.g., BoxâCox, YeoâJohnson), 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>.
Version: 6.1 Imports: methods, nortest, moments, MASS, boot, car, dplyr, tidyr, purrr, stringr, tibble, ggplot2, viridis, cli, energy, plotly, mice Published: 2025-06-10 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://selcukorkmaz.github.io/mvn-tutorial/, https://github.com/selcukorkmaz/MVN, http://biosoft.erciyes.edu.tr/app/MVN NeedsCompilation: no Citation: MVN citation info Materials: README CRAN checks: MVN results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=MVN to link to this page.
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