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extRemes (version 2.1)

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

5,429

Version

2.1

License

GPL (>= 2)

Maintainer

Eric Gilleland

Last Published

November 24th, 2020

Functions in extRemes (2.1)

Denmint

Denver Minimum Temperature
CarcasonneHeat

European Climate Assessment and Dataset
Tphap

Daily Maximum and Minimum Temperature in Phoenix, Arizona.
Denversp

Denver July hourly precipitation amount.
HEAT

Summer Maximum and Minimum Temperature: Phoenix, Arizona
BayesFactor

Estimate Bayes Factor
Flood

United States Total Economic Damage Resulting from Floods
Ozone4H

Ground-Level Ozone Order Statistics.
Rsum

Hurricane Frequency Dataset.
bvpotbooter

Bootstrap Functions for Bivariate POT
PORTw

Annual Maximum and Minimum Temperature
atdf

Auto-Tail Dependence Function
SantaAna

Santa Ana Winds Data
decluster

Decluster Data Above a Threshold
ci.fevd

Confidence Intervals
is.fixedfevd

Stationary Fitted Model Check
datagrabber.declustered

Get Original Data from an R Object
hwmi

Heat Wave Magnitude Index
extRemes internal

extRemes Internal and Secondary Functions
erlevd

Effective Return Levels
levd

Extreme Value Likelihood
extremalindex

Extemal Index
ci.rl.ns.fevd.bayesian

Confidence/Credible Intervals for Effective Return Levels
blockmaxxer

Find Block Maxima
extRemes-package

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

Heat Wave Magnitude Index
damage

Hurricane Damage Data
make.qcov

Covariate Matrix for Non-Stationary EVD Projections
Peak

Salt River Peak Stream Flow
mixbeta

Mixed Beta Dependence Model Likelihood
Potomac

Potomac River Peak Stream Flow Data.
findAllMCMCpars

Manipulate MCMC Output from fevd Objects
findpars

Get EVD Parameters
logistic

Logistic Dependence Model Likelihood
shiftplot

Shift Plot Between Two Sets of Data
lr.test

Likelihood-Ratio Test
ftcanmax

Annual Maximum Precipitation: Fort Collins, Colorado
strip

Strip Fitted EVD Object of Everything but the Parameter Estimates
fpois

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

Probabilities and Random Draws from Fitted EVDs
devd

Extreme Value Distributions
profliker

Profile Likelihood Function
rlevd

Return Levels for Extreme Value Distributions
qqnorm

Normal qq-plot with 95 Percent Simultaneous Confidence Bands
abba

Implementation of Stephenson-Shaby-Reich-Sullivan
revtrans.evd

Reverse Transformation
distill.fevd

Distill Parameter Information
qqplot

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

Posterior Mode from an MCMC Sample
return.level

Return Level Estimates
mrlplot

Mean Residual Life Plot
trans

Transform Data
fbvpot

Estimate the Bivariate Peaks-Over-Threshold (POT) Model
threshrange.plot

Threshold Selection Through Fitting Models to a Range of Thresholds
fevd

Fit An Extreme Value Distribution (EVD) to Data
xtibber

Test-Inversion Bootstrap for Extreme-Value Analysis
xbooter

Additional Bootstrap Functions for Univariate EVA
taildep.test

Tail Dependence Test
parcov.fevd

EVD Parameter Covariance
taildep

Tail Dependence
FCwx

Fort Collins, Colorado Weather Data
Fort

Daily precipitation amounts in Fort Collins, Colorado.