Statistical Methodology for Graphical Extreme Value Models
Censor dataset
Check input graph
Check input graph and partial matrix
Identify pairs of vertices that are split by a separator
HR parameter matrix checks
Transformation between and
Completion of decomposable Gamma matrices
DEMO-VERSION: Completion of non-decomposable Gamma matrices
Non-decomposable completion of variogram matrices
Non-decomposable completion of variogram matrices
Completion of two-clique decomposable Gamma matrices
Completion of Gamma matrices
Compute plot limits
Data standardization to multivariate Pareto scale
Learning extremal graph structure with latent variables
Learning extremal graph structure
Empirical estimation of extremal correlation
Empirical estimation of extremal correlation matrix
Estimation of the variogram matrix of a Huesler-Reiss distrib...
Fitting extremal minimum spanning tree
Performs Gaussian likelihood optimization under Laplacian matrix const...
Ensure numerical matrix symmetry/zero values
Fast computation of diag(y %% M %% t(y))
Helper function to combine par with fixed params (in init)
Find a separator set for two vertices
Experimental: Fit graph using empirical Theta matrix
Fit value(s) in interval
Convert flight counts to connection list
Parameter fitting for Huesler-Reiss graphical models
HR Parameter fitting - Helper functions
Parameter fitting for multivariate Huesler-Reiss Pareto distribution
Compute theoretical in 3D
Generate a random Gamma matrix
Generate a random Gamma matrix for a given graph
Generate a random Gamma matrix containing only integers
Generate random Huesler-Reiss Models
Generate a random symmetric positive definite matrix
Generate random graphs
Get alert function
Get Cliques and Separators of a graph
Get Cliques and Separators of a graph
Number of cores to be used in parallel computations
Tolerances to be used in computations
Get Danube flow graph
Get filtered flight delays
Get flight graph
Get package data
Get the submatrix corresponding to a subgraph
graphicalExtremes: Statistical methodology for graphical extreme value...
Graph equality
Compute the exponent measure density of HR distribution
Compute censored exponent measure
Full censored log-likelihood of HR model
Compute Huesler-Reiss log-likelihood, AIC, and BIC
Convert indices to numerical indices
Create a list of separators
Convert matrix to graph
Marginalize multivariate Pareto dataset
Order Cliques
Create Gamma or Theta from vector
Conversion between Huesler-Reiss parameter matrices
Factory: parToMatrices
Plot Danube River Flow Data
Plot flight data
Sampling of a multivariate Pareto distribution on a tree
Sampling of a multivariate Pareto distribution
Sampling of a multivariate max-stable distribution on a tree
Sampling of a multivariate max-stable distribution
Simulate Dirichlet extremal functions
Simulate HR extremal functions
Simulate logistic extremal functions
Simulate negative logistic extremal functions
Simulate Dirichlet extremal functions on a tree
Simulate HR extremal functions on a tree
Simulate logistic extremal functions on a tree
Split graph into invariant subgraphs
Uniform margin
Compute exponent measure
Computes the Z-matrix
Statistical methodology for sparse multivariate extreme value models. Methods are provided for exact simulation and statistical inference for multivariate Pareto distributions on graphical structures as described in the paper 'Graphical Models for Extremes' by Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.
Useful links