ReturnCurves-package {ReturnCurves}

Estimation of Return Curves

Description

Implements the estimation of the \(p\)-probability return curve (Murphy-Barltrop et al. 2023), as well as a pointwise and smooth estimation of the angular dependence function (Wadsworth and Tawn 2013).

Available functions

adf_est: Estimation of the Angular Dependence Function (ADF)

adf_gof: Goodness of fit of the Angular Dependence Function estimates

airdata: Air pollution data

marggpd: Assessing the Marginal Tail Fits

margtransf: Marginal Transformation

rc_est: Return Curve estimation

rc_gof: Goodness of fit of the Return Curve estimates

rc_unc: Uncertainty of the Return Curve estimates

runShiny: Complementary Shiny app for the ReturnCurves package

Author(s)

Maintainer: Lídia André [e-mail]

Authors:

• Callum Murphy-Barltrop [e-mail]

References

Murphy-Barltrop CJR, Wadsworth JL, Eastoe EF (2023). “New estimation methods for extremal bivariate return curves.” Environmetrics, 34(5). ISSN 1099095X, doi: 10.1002/env.2797.

Wadsworth JL, Tawn JA (2013). “A new representation for multivariate tail probabilities.” Bernoulli, 19(5B), 2689-2714. ISSN 13507265, doi: 10.3150/12-BEJ471.

Examples

library(ReturnCurves)

data(airdata)

n <- dim(airdata)[1]

# Marginal Transformation
margdata <- margtransf(airdata)

head(margdata@dataexp)

# Return Curves estimation

prob <- 1/n

retcurve <- rc_est(margdata = margdata, p = prob, method = "hill")

head(retcurve@rc)

# ADF estimation
lambda <- adf_est(margdata = margdata, method = "hill")

head(lambda@adf)