An implementation of the statistical methodology paper Engelke and Hitz (2020) for sparse multivariate extreme value models. Includes exact simulation algorithms and statistical inference methods for multivariate Pareto distributions on graphical structures.
Contains also implementations of statistical methods from Engelke and Volgushev (2022), Röttger et al. (2021), Engelke et al. (2022), Hentschel et al. (2022), and Engelke and Taeb (2024), as well as data sets from Asadi et al. (2015) and Hentschel et al. (2022).
Installation
The latest CRAN version can be installed using:
install.packages("graphicalExtremes")The latest GitHub version can be installed using:
# install.packages("devtools")
devtools::install_github("sebastian-engelke/graphicalExtremes")References
Asadi, P., Davison, A. C., and Engelke, S. (2015). Extremes on river networks. Ann. Appl. Stat., 9(4), 2023–2050. https://doi.org/10.1214/15-AOAS863
Engelke, S., and Hitz, A. S. (2020). Graphical models for extremes (with discussion). J. R. Stat. Soc. Ser. B Stat. Methodol., 82, 871–932.
Engelke, S., Lalancette, M., and Volgushev, S. (2022). Learning extremal graphical structures in high dimensions. arXiv. https://doi.org/10.48550/ARXIV.2111.00840
Engelke, S., and Taeb, A. (2024). Extremal graphical modeling with latent variables. https://arxiv.org/abs/2403.09604
Engelke, S., and Volgushev, S. (2022). Structure learning for extremal tree models. J. R. Stat. Soc. Ser. B Stat. Methodol. https://doi.org/https://doi.org/10.1111/rssb.12556
Hentschel, M., Engelke, S., and Segers, J. (2022). Statistical inference for Hüsler-Reiss graphical models through matrix completions. arXiv. https://doi.org/10.48550/ARXIV.2210.14292
Röttger, F., Engelke, S., and Zwiernik, P. (2021). Total positivity in multivariate extremes. arXiv. https://doi.org/10.48550/ARXIV.2112.14727