mvrnorm

Sampler derived using the eigendecomposition of the covariance
matrix <code>Sigma</code>. The function uses the Armadillo random normal generator

internal

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

Raphael Huser

Scott D. Grimshaw

Jin Zhang

Michael A. Stephens

Art B. Owen

mvrnorm function

<dl><dt>n</dt>
<dd>sample size</dd>
<dt>mu</dt>
<dd>mean vector. Will set the dimension</dd>
<dt>Sigma</dt>
<dd>a square covariance matrix, of same dimension as <code>mu</code>.
No sanity check is performed to validate that the matrix is positive definite, so use at own risk</dd></dl>

Arguments

Multivariate Normal distribution sampler — mvrnorm

<dl>

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


<dt>mu</dt>
<dd>mean vector. Will set the dimension</dd>


<dt>Sigma</dt>
<dd>a square covariance matrix, of same dimension as <code>mu</code>.
No sanity check is performed to validate that the matrix is positive definite, so use at own risk</dd>

</dl>

mvrnorm: Multivariate Normal distribution sampler

Description

Usage

Value

Arguments

Examples