RVtrunc2cor

compute correlation matrix from 2-truncated R-vine

Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) <doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015) <doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024) <doi:10.1016/j.jmva.2023.105263>, one factor truncated vine models in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) <doi:10.1080/10485252.2017.1407414> and estimating tail dependence parameter.

Pavel Krupskii

FactorCopulaModel

Factor Copula Models

Harry Joe

Xinyao Fan

Allan Macleod

Robert Gentleman

Ross Ihaka

RVtrunc2cor function

<dl><dt>RVobj</dt>
<dd>R-vine object with vine array and partial correlation matrix variable 1 is the latent variable; list with $Varray (d+1)x(d+1), $PCor 2x(d+1)</dd></dl>

Arguments

compute correlation matrix from 2-truncated R-vine — RVtrunc2cor

<dl>

<dt>RVobj</dt>
<dd>R-vine object with vine array and partial correlation matrix variable 1 is the latent variable; list with $Varray (d+1)x(d+1), $PCor 2x(d+1)</dd>

</dl>

RVtrunc2cor: compute correlation matrix from 2-truncated R-vine

Description

Usage

Value

Arguments

Details