tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student- t Probabilities with Low-Rank Methods in R

Jian Cao, Marc G. Genton, David E. Keyes, George M. Turkiyyah

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper introduces the usage and performance of the R package tlrmvnmvt, aimed at computing high-dimensional multivariate normal and Student-t probabilities. The package implements the tile-low-rank methods with block reordering and the separationof-variable methods with univariate reordering. The performance is compared with two other state-of-the-art R packages, namely the mvtnorm and the TruncatedNormal packages. Our package has the best scalability and is likely to be the only option in thousands of dimensions. However, for applications with high accuracy requirements, the TruncatedNormal package is more suitable. As an application example, we show that the excursion sets of a latent Gaussian random field can be computed with the tlrmvnmvt package without any model approximation and hence, the accuracy of the produced excursion sets is improved.
Original languageEnglish (US)
JournalJournal of Statistical Software
Volume101
Issue number4
DOIs
StatePublished - 2022

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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