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) <arXiv:2210.00634>.

Version: 0.1.0
Depends: R (≥ 4.0.0), data.table
Imports: RANN, proxy, mlpack, boot, igraph
Published: 2022-10-06
Author: Zhen Huang [aut, cre]
Maintainer: Zhen Huang <zh2395 at columbia.edu>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: KMD results

Documentation:

Reference manual: KMD.pdf

Downloads:

Package source: KMD_0.1.0.tar.gz
Windows binaries: r-devel: KMD_0.1.0.zip, r-release: KMD_0.1.0.zip, r-oldrel: KMD_0.1.0.zip
macOS binaries: r-release (arm64): KMD_0.1.0.tgz, r-oldrel (arm64): KMD_0.1.0.tgz, r-release (x86_64): KMD_0.1.0.tgz

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