KMD: Kernel Measure of Multi-Sample Dissimilarity
Implementations of the kernel measure of multi-sample dissimilarity (KMD) between
several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD
measures the dissimilarity between multiple samples, based on the observations from them.
It converges to the population quantity (depending on the kernel) which is between 0 and 1.
A small value indicates the multiple samples are from the same distribution, and a large value
indicates the corresponding distributions are different. The population quantity is 0 if and only
if all distributions are the same, and 1 if and only if all distributions are mutually singular.
The package also implements the tests based on KMD for H0: the M distributions are equal
against H1: not all the distributions are equal. Both permutation test and asymptotic test are
available. These tests are consistent against all alternatives where at least two samples have
different distributions. For more details on KMD and the associated tests, see Huang, Z. and
B. Sen (2022) <doi:10.48550/arXiv.2210.00634>.
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