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extRemes (version 2.1-1)

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

3,108

Version

2.1-1

License

GPL (>= 2)

Maintainer

Last Published

May 24th, 2021

Functions in extRemes (2.1-1)

HEAT

Summer Maximum and Minimum Temperature: Phoenix, Arizona
Ozone4H

Ground-Level Ozone Order Statistics.
Denversp

Denver July hourly precipitation amount.
Flood

United States Total Economic Damage Resulting from Floods
Denmint

Denver Minimum Temperature
CarcasonneHeat

European Climate Assessment and Dataset
FCwx

Fort Collins, Colorado Weather Data
Fort

Daily precipitation amounts in Fort Collins, Colorado.
PORTw

Annual Maximum and Minimum Temperature
BayesFactor

Estimate Bayes Factor
atdf

Auto-Tail Dependence Function
extRemes-package

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

Extemal Index
abba

Implementation of Stephenson-Shaby-Reich-Sullivan
Tphap

Daily Maximum and Minimum Temperature in Phoenix, Arizona.
ftcanmax

Annual Maximum Precipitation: Fort Collins, Colorado
fpois

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

Santa Ana Winds Data
levd

Extreme Value Likelihood
damage

Hurricane Damage Data
ci.rl.ns.fevd.bayesian

Confidence/Credible Intervals for Effective Return Levels
Rsum

Hurricane Frequency Dataset.
Potomac

Potomac River Peak Stream Flow Data.
is.fixedfevd

Stationary Fitted Model Check
blockmaxxer

Find Block Maxima
devd

Extreme Value Distributions
Peak

Salt River Peak Stream Flow
findAllMCMCpars

Manipulate MCMC Output from fevd Objects
lr.test

Likelihood-Ratio Test
logistic

Logistic Dependence Model Likelihood
decluster

Decluster Data Above a Threshold
distill.fevd

Distill Parameter Information
datagrabber.declustered

Get Original Data from an R Object
fbvpot

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

Probabilities and Random Draws from Fitted EVDs
bvpotbooter

Bootstrap Functions for Bivariate POT
findpars

Get EVD Parameters
erlevd

Effective Return Levels
fevd

Fit An Extreme Value Distribution (EVD) to Data
hwmi

Heat Wave Magnitude Index
extRemes internal

extRemes Internal and Secondary Functions
mrlplot

Mean Residual Life Plot
ci.fevd

Confidence Intervals
hwmid

Heat Wave Magnitude Index
qqnorm

Normal qq-plot with 95 Percent Simultaneous Confidence Bands
threshrange.plot

Threshold Selection Through Fitting Models to a Range of Thresholds
profliker

Profile Likelihood Function
rlevd

Return Levels for Extreme Value Distributions
make.qcov

Covariate Matrix for Non-Stationary EVD Projections
mixbeta

Mixed Beta Dependence Model Likelihood
revtrans.evd

Reverse Transformation
postmode

Posterior Mode from an MCMC Sample
shiftplot

Shift Plot Between Two Sets of Data
trans

Transform Data
taildep

Tail Dependence
qqplot

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

Additional Bootstrap Functions for Univariate EVA
xtibber

Test-Inversion Bootstrap for Extreme-Value Analysis
taildep.test

Tail Dependence Test
strip

Strip Fitted EVD Object of Everything but the Parameter Estimates
parcov.fevd

EVD Parameter Covariance
return.level

Return Level Estimates