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

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

excursion sets, high dimensions, multivariate normal, multivariate Student-t, tlrmvnmvt

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.

Jian Cao

https://www.jstatsoft.org/article/view/v101i04

King Abdullah University of
Science and Technology

January 2022

Fajar bagus W

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English

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