Extreme Value Analysis
Auto-Tail Dependence Function
Estimate Bayes Factor
Find Block Maxima
Confidence Intervals
Confidence/Credible Intervals for Effective Return Levels
Get Original Data from an R Object
Decluster Data Above a Threshold
Extreme Value Distributions
Distill Parameter Information
Effective Return Levels
Extemal Index
extRemes Internal and Secondary Functions
extRemes -- Weather and Climate Applications of Extreme Value Analysis...
Fit An Extreme Value Distribution (EVD) to Data
Manipulate MCMC Output from fevd Objects
Get EVD Parameters
Fit Homogeneous Poisson to Data and Test Equality of Mean and Variance
Heat Wave Magnitude Index
Heat Wave Magnitude Index
Stationary Fitted Model Check
Extreme Value Likelihood
Likelihood-Ratio Test
Covariate Matrix for Non-Stationary EVD Projections
Mean Residual Life Plot
EVD Parameter Covariance
Probabilities and Random Draws from Fitted EVDs
Posterior Mode from an MCMC Sample
Profile Likelihood Function
Normal qq-plot with 95 Percent Simultaneous Confidence Bands
qq-plot Between Two Vectors of Data with 95 Percent Confidence Bands
Return Level Estimates
Reverse Transformation
Return Levels for Extreme Value Distributions
Shift Plot Between Two Sets of Data
Strip Fitted EVD Object of Everything but the Parameter Estimates
Tail Dependence
Tail Dependence Test
Threshold Selection Through Fitting Models to a Range of Thresholds
Transform Data
Additional Bootstrap Functions for Univariate EVA
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>.