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texmex (version 1.3)
Threshold exceedences and multivariate extremes
Description
Conditional multivariate extreme value modelling using the approach of Heffernan and Tawn.
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Install
install.packages('texmex')
Monthly Downloads
636
Version
1.3
License
GPL (>= 2) | BSD
Maintainer
Harry Southworth
Last Published
August 13th, 2012
Functions in texmex (1.3)
Search functions
mexDependence
Estimate the dependence parameters in a conditional multivariate extreme values model
thinAndBurn
Process Metropolis output from GPD fitting to discard unwanted observations.
bgpdSetSeed
Set the seed from a fitted bgpd object.
edf
Compute empirical distribution function
gpdRangeFit
Estimate generalized Pareto distribution parameters over a range of values
migpd
Fit multiple independent generalized Pareto models
methods
Methods for texmex objects
validate.texmex
Validate the texmex package
mrlPlot
Mean residual life plot
mex
Conditional multivariate extreme values modelling
chi
Measures of extremal dependence
copula
Calculate the copula of a matrix of variables
MCS
Multivariate conditional Spearman's rho
migpdCoefs
Change values of parameters in a migpd object
bootgpd
Parametric bootstrap for generalized Pareto models
texmex-package
Conditional multivariate extreme values modelling.
liver
Liver related laboratory data
texmex-internal
Internal functions for texmex
bootmex
Bootstrap a conditional multivariate extreme values model
mexRangeFit
Estimate dependence parameters in a conditional multivariate extreme values model over a range of thresholds.
summer and winter data
Air pollution data, separately for summer and winter months
extremalIndex
Extremal index estimation and automatic declustering
dgpd
Density, cumulative density, quantiles and random number generation for the generalized Pareto distribution
rl
Return levels
gpd
Generalized Pareto distribution modelling
endPoint
Calculate upper end point for fitted GPD
rain and wavesurge
Rain and wavesurge datasets.
predict.gpd
Predict return levels from Generalized Pareto Distribution models, or obtain the linear predictors.