print.RoBTT

An implementation of Bayesian model-averaged t-test that allows
users to draw inference about the presence vs absence of the effect,
heterogeneity of variances, and outliers. The 'RoBTT' packages estimates model
ensembles of models created as a combination of the competing hypotheses and uses
Bayesian model-averaging to combine the models using posterior model probabilities.
Users can obtain the model-averaged posterior distributions and inclusion Bayes
factors which account for the uncertainty in the data generating process
(Maier et al., 2022, <doi:10.31234/osf.io/d5zwc>).
Users can define a wide range of informative priors for all parameters
of interest. The package provides convenient functions for summary, visualizations,
and fit diagnostics.

Franti<c5><a1>ek Barto<c5><a1>

RoBTT

Robust Bayesian T-Test

František Bartoš

Maximilian Maier

Henrik R Godmann

print.RoBTT function

<dl><dt>x</dt>
<dd>a fitted 'RoBTT' object.</dd>
<dt>...</dt>
<dd>additional arguments.</dd></dl>

Arguments

Prints a fitted 'RoBTT' object — print.RoBTT

<dl>

<dt>x</dt>
<dd>a fitted 'RoBTT' object.</dd>


<dt>...</dt>
<dd>additional arguments.</dd>

</dl>

print.RoBTT: Prints a fitted 'RoBTT' object

Description

Usage

Value

Arguments

See Also