person("Aurélie", "Calabrèse", email = "aurelie.calabrese@inria.fr", role = c("aut", "cre")),
See Calabrese et al. 2018 for more details <doi:10.1167/18.1.8>.

Title: Multivariate Nonparametric Statistical Process Control
Author: Martin Bezener <bezener@stat.umn.edu> and Peihua Qiu
Description: Statistical process control for multivariate data that is
not necessarily Gaussian distributed. A function is provided to
categorize components of multivariate response vectors. Tools
for setting up a CUSUM procedure for the transformed data are
included. The CUSUM scheme can also be applied to the case when
some (or all) of the multivariate response components are

Title: Affine Invariant Tests of Multivariate Normality
Authors@R: c(person("Lucas", "Butsch", email="lucas.butsch@gmail.com", role=c("aut")), person("Bruno", "Ebner", email="bruno.ebner@kit.edu", role = c("aut", "cre")), person("Jaco", "Visagie", email="jaco.visagie@nwu.ac.za", role=c("ctb")), person("Johann", "Siemens", email="johann-siemens@web.de", role=c("ctb")))
Description: Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) <arXiv:2004.07332> titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the 'energy' test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns).