{ "_id": "6a1fed80b401979e73440e99", "Package": "KMD", "Type": "Package", "Title": "Kernel Measure of Multi-Sample Dissimilarity", "Version": "0.1.0", "Authors@R": "person(\"Zhen\", \"Huang\", , \"zh2395@columbia.edu\", role = c(\"aut\", \"cre\"))", "Maintainer": "Zhen Huang ", "Description": "Implementations of the kernel measure of multi-sample\ndissimilarity (KMD) between several samples using K-nearest\nneighbor graphs and minimum spanning trees. The KMD measures\nthe dissimilarity between multiple samples, based on the\nobservations from them. It converges to the population quantity\n(depending on the kernel) which is between 0 and 1. A small\nvalue indicates the multiple samples are from the same\ndistribution, and a large value indicates the corresponding\ndistributions are different. The population quantity is 0 if\nand only if all distributions are the same, and 1 if and only\nif all distributions are mutually singular. The package also\nimplements the tests based on KMD for H0: the M distributions\nare equal against H1: not all the distributions are equal. Both\npermutation test and asymptotic test are available. These tests\nare consistent against all alternatives where at least two\nsamples have different distributions. For more details on KMD\nand the associated tests, see Huang, Z. and B. 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