ifit

A data frame containing 8 variables and nearly 200 observations. There are
two subjects, each of which provided data every data for over 90 days. Six variables are from
the PANAS scale (positive and negative affect), the daily number of steps, and the subject id.
<ul>
<li><code>id</code> Subject id</li>
<li><code>interested</code></li>
<li><code>disinterested</code></li>
<li><code>excited</code></li>
<li><code>upset</code></li>
<li><code>strong</code></li>
<li><code>stressed</code></li>
<li><code>steps</code> steps recorded by a fit bit</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

ifit function

A data frame containing 197 observations and 8 variables. The data have been used in
o2020useBGGM and williams2019bayesianBGGM

ifit: Data: ifit Intensive Longitudinal Data

Description

Usage

Arguments

Format

References

Examples