mBICV

a fitted <code>vinecop</code> object.

object

baseline prior probability of a non-independence copula.

psi0

calculates the vine copula Bayesian information criterion (vBIC), which is
defined as
$$\mathrm{BIC} = -2\, \mathrm{loglik} + \nu \ln(n), - 2 * 
\sum_{t=1}^(d - 1) \{q_t log(\psi_0^t) - (d - t - q_t) log(1 - \psi_0^t)\}
$$
where $\mathrm{loglik}$ is the log-liklihood and $\nu$ is the
(effective) number of parameters of the model, $t$ is the tree level
$\psi_0$ is the prior probability of having a non-independence copula and
$q_t$ is the number of non-independence copulas in tree $t$.
The vBIC is a consistent model
selection criterion for parametric sparse vine copula models.

Provides an interface to 'vinecopulib', a C++ library for vine
copula modeling based on 'Boost' and 'Eigen'. The 'rvinecopulib'
package implements the core features of the popular 'VineCopula' package, in
particular inference algorithms for both vine copula and bivariate copula
models. Advantages over 'VineCopula' are a sleeker and more modern API,
improved performances, especially in high dimensions, nonparametric and
multi-parameter families. The 'rvinecopulib' package includes 'vinecopulib' as
header-only C++ library (currently version 0.2.6). Thus
users do not need to install 'vinecopulib' itself in order to use
'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under
the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.

Thomas Nagler

rvinecopulib

High Performance Algorithms for Vine Copula Modeling

Thibault Vatter

mBICV function

calculates the vine copula Bayesian information criterion (vBIC), which is
defined as
$$\mathrm{BIC} = -2\, \mathrm{loglik} +  \nu \ln(n), - 2 * 
\sum_{t=1}^(d - 1) \{q_t log(\psi_0^t) - (d - t - q_t) log(1 - \psi_0^t)\}
$$
where $\mathrm{loglik}$ is the log-liklihood and $\nu$ is the
(effective) number of parameters of the model, $t$ is the tree level
$\psi_0$ is the prior probability of having a non-independence copula and
$q_t$ is the number of non-independence copulas in tree $t$.
The vBIC is a consistent model
selection criterion for parametric sparse vine copula models. — mBICV

calculates the vine copula Bayesian information criterion (vBIC), which is
defined as
$$\mathrm{BIC} = -2\, \mathrm{loglik} +  \nu \ln(n), - 2 * 
\sum_{t=1}^(d - 1) \{q_t log(\psi_0^t) - (d - t - q_t) log(1 - \psi_0^t)\}
$$
where $\mathrm{loglik}$ is the log-liklihood and $\nu$ is the
(effective) number of parameters of the model, $t$ is the tree level
$\psi_0$ is the prior probability of having a non-independence copula and
$q_t$ is the number of non-independence copulas in tree $t$.
The vBIC is a consistent model
selection criterion for parametric sparse vine copula models.

Description

Usage

Arguments