chol.rvar

Cholesky decomposition of an <code>rvar</code> containing a matrix.

Provides useful tools for both users and developers of packages
for fitting Bayesian models or working with output from Bayesian models.
The primary goals of the package are to:
(a) Efficiently convert between many different useful formats of
draws (samples) from posterior or prior distributions.
(b) Provide consistent methods for operations commonly performed on draws,
for example, subsetting, binding, or mutating draws.
(c) Provide various summaries of draws in convenient formats.
(d) Provide lightweight implementations of state of the art posterior
inference diagnostics. References: Vehtari et al. (2021)
<doi:10.1214/20-BA1221>.

PaulChristian Buerkner

posterior

Tools for Working with Posterior Distributions

Paul-Christian Bürkner

Jonah Gabry

Matthew Kay

Aki Vehtari

Måns Magnusson

Rok Češnovar

Ben Lambert

Ozan Adıgüzel

Jacob Socolar

Noa Kallioinen

chol.rvar function

<dl><dt>x</dt>
<dd>(rvar) A 2-dimensional <code>rvar</code>.</dd>
<dt>...</dt>
<dd>Additional parameters passed on to <code>chol.tensor()</code></dd></dl>

Arguments

Cholesky decomposition of random matrix — chol.rvar

<dl>

<dt>x</dt>
<dd>(rvar) A 2-dimensional <code>rvar</code>.</dd>


<dt>...</dt>
<dd>Additional parameters passed on to <code>chol.tensor()</code></dd>

</dl>

Cholesky decomposition of random matrix

chol.rvar: Cholesky decomposition of random matrix

Description

Usage

Value

Arguments