Collate: mvn.R hzTest.R mardiaTest.R roystonTest.R
Packaged: 2018-03-22 21:21:55 UTC; selcukkorkmaz

Packaged: 2018-04-20 09:12:53 UTC; selcukkorkmaz

Packaged: 2018-05-07 08:10:34 UTC; selcukkorkmaz

Packaged: 2018-06-09 18:29:45 UTC; selcukkorkmaz

Packaged: 2018-06-14 06:39:24 UTC; selcukkorkmaz

psych, boot, energy, kableExtra, magrittr, car
distributions through plots and tests, and performs multivariate Box-Cox transformation
(Korkmaz et al, (2014), <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).
Packaged: 2019-01-30 13:16:18 UTC; selcukkorkmaz

Packaged: 2019-03-18 13:29:18 UTC; selcukkorkmaz

Packaged: 2019-09-27 13:51:38 UTC; selcukkorkmaz

Authors@R: c(person(given = "Selcuk", family =  "Korkmaz", role = c("aut", "cre"),
person(given = "Dincer", family = "Goksuluk", role = "aut",
person(given = "Gokmen", family = "Zararsiz", role = "aut",
Imports: methods, nortest, moments, MASS, plyr, psych, boot, energy,
Packaged: 2021-06-30 07:33:28 UTC; selcukkorkmaz
Author: Selcuk Korkmaz [aut, cre] (<https://orcid.org/0000-0003-4632-6850>),

Title: Bayesian Analysis Framework for MVN (Mixture) Distribution
Description: Tools of Bayesian analysis framework using the method
multivariate normal (MVN) distribution and multivariate normal
a) calculating Bayesian posteriori of (Mix)MVN distribution;
b) generating random vectors of (Mix)MVN distribution;
c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.

Package: MVNtestchar
Type: Package
Title: Multivariate Normal Distribution Characterization Test
Version: 1.0.0
Date: 2020-03-21
Authors@R: person("William", "Fairweather", email = "wrf343@flowervalleyconsulting.com",
role = c("aut", "cre"))
Description: Provides a test of multivariate normality of a sample which does
not require estimation of the nuisance parameters, the mean and covariance
matrix.  Rather, a sequence of transformations removes these nuisance
parameters and results in a set of sample matrices that are positive
definite.  These matrices are uniformly distributed on the space of positive
definite matrices in the unit hyperrectangle if and only if the original
data is multivariate normal. The package performs a goodness of fit test of
this hypothesis. Four sample datasets are included: a bivariate and a
trivariate normal set and a bivariate and trivariate Bernoulli set. In
addition to the test, functions in the package give rotatable visualizations
of the support region of positive definite matrices for bivariate samples.
Depends: R (>= 2.10)
Imports: graphics, grDevices, Hmisc, stats, utils
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2020-03-27 17:57:47 UTC; No
Author: William Fairweather [aut, cre]
Maintainer: William Fairweather <wrf343@flowervalleyconsulting.com>
Repository: CRAN
Date/Publication: 2020-03-30 14:20:02 UTC