We introduce and showcase mvpd (an acronym for multivariate product distributions), a package that uses a product distribution approach to calculating multivariate subgaussian stable distribution functions. The family of multivariate subgaussian stable distributions are elliptically contoured multivariate stable distributions that contain the multivariate Cauchy and the multivariate normal distribution. These distributions can be useful in modeling data and phenomena that have heavier tails than the normal distribution (more frequent occurrence of extreme values). Application areas include log returns for stocks, signal processing for radar and sonar data, astronomy, and hunting patterns of sharks.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-056.zip
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For attribution, please cite this work as
Swihart & Nolan, "Multivariate Subgaussian Stable Distributions in R", The R Journal, 2022
BibTeX citation
@article{RJ-2022-056,
author = {Swihart, Bruce J. and Nolan, John P.},
title = {Multivariate Subgaussian Stable Distributions in R},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-056},
doi = {10.32614/RJ-2022-056},
volume = {14},
issue = {3},
issn = {2073-4859},
pages = {293-302}
}