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extRemes (version 2.1-2)

Extreme Value Analysis

Description

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) and for bootstrapping, please see Gilleland (2020) .

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Version

Install

install.packages('extRemes')

Monthly Downloads

2,871

Version

2.1-2

License

GPL (>= 2)

Maintainer

Eric Gilleland

Last Published

May 4th, 2022

Functions in extRemes (2.1-2)

HEAT

Summer Maximum and Minimum Temperature: Phoenix, Arizona
Flood

United States Total Economic Damage Resulting from Floods
CarcasonneHeat

European Climate Assessment and Dataset
Denmint

Denver Minimum Temperature
Ozone4H

Ground-Level Ozone Order Statistics.
Fort

Daily precipitation amounts in Fort Collins, Colorado.
PORTw

Annual Maximum and Minimum Temperature
FCwx

Fort Collins, Colorado Weather Data
Denversp

Denver July hourly precipitation amount.
BayesFactor

Estimate Bayes Factor
Peak

Salt River Peak Stream Flow
ci.fevd

Confidence Intervals
bvpotbooter

Bootstrap Functions for Bivariate POT
blockmaxxer

Find Block Maxima
ci.rl.ns.fevd.bayesian

Confidence/Credible Intervals for Effective Return Levels
erlevd

Effective Return Levels
distill.fevd

Distill Parameter Information
Potomac

Potomac River Peak Stream Flow Data.
extRemes-package

extRemes -- Weather and Climate Applications of Extreme Value Analysis (EVA)
extRemes internal

extRemes Internal and Secondary Functions
extremalindex

Extemal Index
damage

Hurricane Damage Data
Tphap

Daily Maximum and Minimum Temperature in Phoenix, Arizona.
atdf

Auto-Tail Dependence Function
fbvpot

Estimate the Bivariate Peaks-Over-Threshold (POT) Model
Rsum

Hurricane Frequency Dataset.
datagrabber.declustered

Get Original Data from an R Object
decluster

Decluster Data Above a Threshold
SantaAna

Santa Ana Winds Data
logistic

Logistic Dependence Model Likelihood
levd

Extreme Value Likelihood
devd

Extreme Value Distributions
hwmid

Heat Wave Magnitude Index
is.fixedfevd

Stationary Fitted Model Check
findAllMCMCpars

Manipulate MCMC Output from fevd Objects
fevd

Fit An Extreme Value Distribution (EVD) to Data
qqnorm

Normal qq-plot with 95 Percent Simultaneous Confidence Bands
ftcanmax

Annual Maximum Precipitation: Fort Collins, Colorado
qqplot

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

Heat Wave Magnitude Index
findpars

Get EVD Parameters
pextRemes

Probabilities and Random Draws from Fitted EVDs
fpois

Fit Homogeneous Poisson to Data and Test Equality of Mean and Variance
lr.test

Likelihood-Ratio Test
rlevd

Return Levels for Extreme Value Distributions
parcov.fevd

EVD Parameter Covariance
profliker

Profile Likelihood Function
postmode

Posterior Mode from an MCMC Sample
xbooter

Additional Bootstrap Functions for Univariate EVA
trans

Transform Data
return.level

Return Level Estimates
make.qcov

Covariate Matrix for Non-Stationary EVD Projections
revtrans.evd

Reverse Transformation
shiftplot

Shift Plot Between Two Sets of Data
threshrange.plot

Threshold Selection Through Fitting Models to a Range of Thresholds
taildep.test

Tail Dependence Test
xtibber

Test-Inversion Bootstrap for Extreme-Value Analysis
mixbeta

Mixed Beta Dependence Model Likelihood
mrlplot

Mean Residual Life Plot
strip

Strip Fitted EVD Object of Everything but the Parameter Estimates
taildep

Tail Dependence