A data frame containing 1002 rows and 7 variables measured on various scales,
including binary and ordered cateogrical (with varying numbers of categories).
There are also missing values in each variable
<ul>
<li><code>Inc</code> Income of the respondent in 1000s of dollars, binned into 21 ordered categories.</li>
<li><code>DEG</code> Highest degree ever obtained (none, HS, Associates, Bachelors, or Graduate)</li>
<li><code>CHILD</code> Number of children ever had.</li>
<li><code>PINC</code> Financial status of respondent's parents when respondent was 16 (on a 5-point scale).</li>
<li><code>PDEG</code> Maximum of mother's and father's highest degree</li>
<li><code>PCHILD</code> Number of siblings of the respondent plus one</li>
<li><code>AGE</code> Age of the respondent in years.</li>
</ul>

datasets

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

gss function

A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale
 fowlkes1988evaluatingBGGM. The variable descriptions were copied from
 @section 4, @hoff2007extending;textualBGGM

gss: Data: 1994 General Social Survey

Description

Usage

Arguments

Format

References

Examples