extRemes2.2 package

Extreme Value Analysis

atdf

Auto-Tail Dependence Function

BayesFactor

Estimate Bayes Factor

blockmaxxer

Find Block Maxima

ci.fevd

Confidence Intervals

ci.rl.ns.fevd.bayesian

Confidence/Credible Intervals for Effective Return Levels

datagrabber

Get Original Data from an R Object

decluster

Decluster Data Above a Threshold

devd

Extreme Value Distributions

distill.fevd

Distill Parameter Information

erlevd

Effective Return Levels

extremalindex

Extemal Index

extRemes-internal

extRemes Internal and Secondary Functions

extRemes-package

extRemes -- Weather and Climate Applications of Extreme Value Analysis...

fevd

Fit An Extreme Value Distribution (EVD) to Data

findAllMCMCpars

Manipulate MCMC Output from fevd Objects

findpars

Get EVD Parameters

fpois

Fit Homogeneous Poisson to Data and Test Equality of Mean and Variance

hwmi

Heat Wave Magnitude Index

hwmid

Heat Wave Magnitude Index

is.fixedfevd

Stationary Fitted Model Check

levd

Extreme Value Likelihood

lr.test

Likelihood-Ratio Test

make.qcov

Covariate Matrix for Non-Stationary EVD Projections

mrlplot

Mean Residual Life Plot

parcov.fevd

EVD Parameter Covariance

pextRemes

Probabilities and Random Draws from Fitted EVDs

postmode

Posterior Mode from an MCMC Sample

profliker

Profile Likelihood Function

qqnorm

Normal qq-plot with 95 Percent Simultaneous Confidence Bands

qqplot

qq-plot Between Two Vectors of Data with 95 Percent Confidence Bands

return.level

Return Level Estimates

revtrans.evd

Reverse Transformation

rlevd

Return Levels for Extreme Value Distributions

shiftplot

Shift Plot Between Two Sets of Data

strip

Strip Fitted EVD Object of Everything but the Parameter Estimates

taildep

Tail Dependence

taildep.test

Tail Dependence Test

threshrange.plot

Threshold Selection Through Fitting Models to a Range of Thresholds

trans

Transform Data

xbooter

Additional Bootstrap Functions for Univariate EVA

xtibber

Test-Inversion Bootstrap for Extreme-Value Analysis

General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.

  • Maintainer: Eric Gilleland
  • License: GPL (>= 2)
  • Last published: 2024-11-29