mvnchol_bamlss

BAMLSS Families for MVN with Cholesky Parameterization

Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
<doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021)
<doi:10.18637/jss.v100.i04>.

Nikolaus Umlauf

bamlss

Bayesian Additive Models for Location, Scale, and Shape (and
Beyond)

Nadja Klein

Achim Zeileis

Meike Koehler

Thorsten Simon

Stanislaus Stadlmann

Alexander Volkmann

mvnchol_bamlss function

<dl><dt>k</dt>
<dd>integer. The dimension of the multivariate distribution.</dd>
<dt>type</dt>
<dd>character. Choose <code>"basic"</code> Cholesky decomposition or <code>"modified"</code>
Cholesky decomposition. (For back compatibility <code>"chol"</code> is identical to <code>"basic"</code>.)</dd>
<dt>...</dt>
<dd>not used.</dd></dl>

Arguments

Cholesky MVN — mvnchol_bamlss

<dl>

<dt>k</dt>
<dd>integer. The dimension of the multivariate distribution.</dd>


<dt>type</dt>
<dd>character. Choose <code>"basic"</code> Cholesky decomposition or <code>"modified"</code>
Cholesky decomposition. (For back compatibility <code>"chol"</code> is identical to <code>"basic"</code>.)</dd>


<dt>...</dt>
<dd>not used.</dd>

</dl>

mvnchol_bamlss: Cholesky MVN

Description

Usage

Value

Arguments

Details

See Also