New Features
* 'independence.test' is now also implemented with type "pearson_approx". Providing the fast p-value approximation developed in . For this also the functions 'pearson.qf' (a Gaussian quadratic form estimate based on mean, variance and skewness) and 'pearson.pvalue' (the corresponding p-value estimate based on new moment estimators) are introduced.
* In "cmd" one can now explicitly specify the use of "isotropic" continuous negative definite functions. This speeds up the calculation for this case by a factor of about 100.
Fixed
* the option "squared" works now also for multivariance with option "correlation=TRUE".
* 'multivariances.all' returns NA for 3-multivariance if only two variables are given.
Updates
* speed up of various functions
* various typos corrected
Changes in Version 1.1.0
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New Features
* 'm.multivariance' a function to calculate the m-multivariance
* 'multivariances.all' a function to calculate standard/total/m-multivariance simultaneously
* 'resample.multivariance' implements the resampling method which can be used to get less conservative tests than the distribution-free methods
* 'dependence.structure' a function to generate a graphical model of the dependence structure
* various examples of the use of 'dependence.structure'
Changes
* The standard output of 'multivariance' is now (distance multivariance squared) scaled by the sample size. Use 'Nscale = FALSE' to get the value without this scaling. The reason for this was twofold: 1. it is now the same setting as for 'total.multivariance'. 2. This is the only value which can (roughly) be interpreted without further calculations.
Updates
* improved documentation. In particular, it is now cleary stated that the squared values are the standard output of 'multivariance' and 'total.multivariance'
* some speed up
Changes in Version 1.0.5 2017-11-01
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Details
* Initial public release