{ "_id": "6a1018bcacfb0bcc41c85a74", "Package": "MVNtestchar", "Type": "Package", "Title": "Test for Multivariate Normal Distribution Based on a\nCharacterization", "Version": "1.1.3", "Date": "2020-07-14", "Authors@R": "person(\"William\", \"Fairweather\", email = \"wrf343@flowervalleyconsulting.com\",\nrole = c(\"aut\", \"cre\"))", "Description": "Provides a test of multivariate normality of an unknown\nsample that does not require estimation of the nuisance\nparameters, the mean and covariance matrix. Rather, a sequence\nof transformations removes these nuisance parameters and\nresults in a set of sample matrices that are positive definite.\nThese matrices are uniformly distributed on the space of\npositive definite matrices in the unit hyper-rectangle if and\nonly if the original data is multivariate normal (Fairweather,\n1973, Doctoral dissertation, University of Washington). The\npackage performs a goodness of fit test of this hypothesis. 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