women_math

<p>A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale.</p>

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

women_math function

<p>A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale
        fowlkes1988evaluatingBGGM. These data have been analyzed in tarantola2004mcmc;textualBGGM
        and in madigan1994modelBGGM. The variable descriptions were copied from  (section 5.2 )
        @section 5.2, @talhouk2012efficientBGGM</p>

women_math: Data: Women and Mathematics

Description

Usage

Arguments

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Details

References

Examples