An implementation of the statistical methodology paper eng2019;textualgraphicalExtremes for sparse multivariate extreme value models. Includes exact simulation algorithms and statistical inference methods for multivariate Pareto distributions on graphical structures. Also contains implementations of statistical methods from eng2020;textualgraphicalExtremes, roe2021;textualgraphicalExtremes, and hen2022;textualgraphicalExtremes.
Maintainer: Sebastian Engelke sebastian.engelke@unige.ch
Authors:
Adrien S. Hitz
Nicola Gnecco
Manuel Hentschel
The following global options are used by functions in the package.
Their values can be changed using base::options().
"graphicalExtremes.mc.cores"The (maximal) number of cores to use in parallel tasks. Will always be overwritten by 1 on Windows.
"graphicalExtremes.tol.small"The "small" tolerance is used in internal computations for values that should mathematically be exactly equal to zero, but deviate due to inherent limitations of numerical computations. This value is used e.g. when checking matrices for symmetry and definiteness. In general, this value is used only as a "permissive" tolerance, in the sense that if a value has to be positive, it is compared to actual zero, but if it has to be zero, its absolute value is compared to this tolerance.
"graphicalExtremes.tol.large"The "large" tolerance is used for values that converge to zero, but are mathematically not supposed to be equal to zero. This value is used e.g. when converting a precision matrix \(\Theta\) to an adjacency matrix of a graph.
"graphicalExtremes.default.alertThe default alert function to be used in validity checks of Huesler-Reiss parameter matrix transformations.
Can be a function that takes an arbitrary number of strings as arguments (e.g. cat(), stop()),
FALSE to ignore the alerts, or TRUE/NULL to use the default function warning().
Useful links: