Maximum A Posteriori Precision Matrix

Fit Bayesian Gaussian graphical models. The methods are separated into
two Bayesian approaches for inference: hypothesis testing and estimation. There are
extensions for confirmatory hypothesis testing, comparing Gaussian graphical models,
and node wise predictability. These methods were recently introduced in the Gaussian
graphical model literature, including
Williams (2019) <doi:10.31234/osf.io/x8dpr>,
Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Philippe Rast

BGGM

Bayesian Gaussian Graphical Models

Donald Williams

Joris Mulder

map function

<dl><dt>Y</dt>
<dd>Matrix (or data frame) of dimensions n (observations) by p (variables).</dd></dl>

Arguments

Maximum A Posteriori Precision Matrix — map

<dl>

<dt>Y</dt>
<dd>Matrix (or data frame) of dimensions n (observations) by p (variables).</dd>

</dl>

map: Maximum A Posteriori Precision Matrix

Description

Usage

Value

Arguments

Examples