JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1034 10.6339/22-JDS1034 Computing in Data Science Computational Challenges of t and Related Copulas HintzErikerik.hintz@uwaterloo.ca1∗ https://orcid.org/0000-0001-8009-4665 HofertMarius1 LemieuxChristiane1 Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada Corresponding author. Email: erik.hintz@uwaterloo.ca. 202222202220195110 Supplementary Material

This paper can be reproduced with the R script reproduce.R and the R package nvmix, version 0.0-7.

512022612022 2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2022 Open access article under the CC BY license.

The present paper addresses computational and numerical challenges when working with t copulas and their more complicated extensions, the grouped t and skew t copulas. We demonstrate how the R package nvmix can be used to work with these copulas. In particular, we discuss (quasi-)random sampling and fitting. We highlight the difficulties arising from using more complicated models, such as the lack of availability of a joint density function or the lack of an analytical form of the marginal quantile functions, and give possible solutions along with future research ideas.

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