extgp.G

Density, distribution function, quantile function and random number
generation for the carrier distributions of the extended Generalized Pareto distributions.

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.

Leo Belzile

Modelling of Extreme Values

Jennifer L. Wadsworth

Paul J. Northrop

Scott D. Grimshaw

Jin Zhang

Michael A. Stephens

Art B. Owen

Raphael Huser

extgp.G function

<dl><dt>u</dt>
<dd>vector of observations (<code>dextgp.G</code>), probabilities (<code>qextgp.G</code>) or quantiles (<code>pextgp.G</code>), in \([0,1]\)</dd>
<dt>prob</dt>
<dd>mixture probability for model <code>type</code> <code>4</code></dd>
<dt>kappa</dt>
<dd>shape parameter for <code>type</code> <code>1</code>, <code>3</code> and <code>4</code></dd>
<dt>delta</dt>
<dd>additional parameter for <code>type</code> <code>2</code>, <code>3</code> and <code>4</code></dd>
<dt>type</dt>
<dd>integer between 0 to 5 giving the model choice</dd>
<dt>log</dt>
<dd>logical; should the log-density be returned (default to <code>FALSE</code>)?</dd>
<dt>n</dt>
<dd>sample size</dd>
<dt>unifsamp</dt>
<dd>sample of uniform; if provided, the data will be used in place of new uniform random variates</dd>
<dt>censoring</dt>
<dd>numeric vector of length 2 containing the lower and upper bound for censoring</dd>
<dt>direct</dt>
<dd>logical; which method to use for sampling in model of <code>type</code> <code>4</code>?</dd></dl>

Arguments

<code>pextgp.G(u, type=1, prob, kappa, delta)</code>
<code>dextgp.G(u, type=1, prob=NA, kappa=NA, delta=NA, log=FALSE)</code>
<code>qextgp.G(u, type=1, prob=NA, kappa=NA, delta=NA)</code>
<code>rextgp.G(n, prob=NA, kappa=NA, delta=NA,
type=1, unifsamp=NULL, direct=FALSE, censoring=c(0,1))</code>

Usage

Author

Carrier distribution for the extended GP distributions of Naveau et al. — extgp.G

<dl>

<dt>u</dt>
<dd>vector of observations (<code>dextgp.G</code>), probabilities (<code>qextgp.G</code>) or quantiles (<code>pextgp.G</code>), in \([0,1]\)</dd>


<dt>prob</dt>
<dd>mixture probability for model <code>type</code> <code>4</code></dd>


<dt>kappa</dt>
<dd>shape parameter for <code>type</code> <code>1</code>, <code>3</code> and <code>4</code></dd>


<dt>delta</dt>
<dd>additional parameter for <code>type</code> <code>2</code>, <code>3</code> and <code>4</code></dd>


<dt>type</dt>
<dd>integer between 0 to 5 giving the model choice</dd>


<dt>log</dt>
<dd>logical; should the log-density be returned (default to <code>FALSE</code>)?</dd>


<dt>n</dt>
<dd>sample size</dd>


<dt>unifsamp</dt>
<dd>sample of uniform; if provided, the data will be used in place of new uniform random variates</dd>


<dt>censoring</dt>
<dd>numeric vector of length 2 containing the lower and upper bound for censoring</dd>


<dt>direct</dt>
<dd>logical; which method to use for sampling in model of <code>type</code> <code>4</code>?</dd>

</dl>

extgp.G: Carrier distribution for the extended GP distributions of Naveau et al.

Description

Arguments

Usage

Author

See Also