StudentT

dstudent_t

pstudent_t

qstudent_t

rstudent_t

Density, distribution function, quantile function and random generation
for the Student-t distribution with location <code>mu</code>, scale <code>sigma</code>,
and degrees of freedom <code>df</code>.

Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include both theory-driven and
data-driven non-linear terms, auto-correlation structures, censoring and
truncation, meta-analytic standard errors, and quite a few more.
In addition, all parameters of the response distribution can be predicted
in order to perform distributional regression. Prior specifications are
flexible and explicitly encourage users to apply prior distributions that
actually reflect their prior knowledge. Models can easily be evaluated and
compared using several methods assessing posterior or prior predictions.
References: Bürkner (2017) <doi:10.18637/jss.v080.i01>;
Bürkner (2018) <doi:10.32614/RJ-2018-017>;
Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017)
<doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

brms

Bayesian Regression Models using 'Stan'

Paul-Christian Bürkner

Jonah Gabry

Sebastian Weber

Andrew Johnson

Martin Modrak

Hamada S. Badr

Frank Weber

Mattan S. Ben-Shachar

Hayden Rabel

Simon C. Mills

Stephen Wild

StudentT function

<dl><dt>x</dt>
<dd>Vector of quantiles.</dd>
<dt>df</dt>
<dd>Vector of degrees of freedom.</dd>
<dt>mu</dt>
<dd>Vector of location values.</dd>
<dt>sigma</dt>
<dd>Vector of scale values.</dd>
<dt>log</dt>
<dd>Logical; If <code>TRUE</code>, values are returned on the log scale.</dd>
<dt>q</dt>
<dd>Vector of quantiles.</dd>
<dt>lower.tail</dt>
<dd>Logical; If <code>TRUE</code> (default), return P(X &lt;= x).
Else, return P(X &gt; x) .</dd>
<dt>log.p</dt>
<dd>Logical; If <code>TRUE</code>, values are returned on the log scale.</dd>
<dt>p</dt>
<dd>Vector of probabilities.</dd>
<dt>n</dt>
<dd>Number of draws to sample from the distribution.</dd></dl>

Arguments

The Student-t Distribution — StudentT

<dl>

<dt>x</dt>
<dd>Vector of quantiles.</dd>


<dt>df</dt>
<dd>Vector of degrees of freedom.</dd>


<dt>mu</dt>
<dd>Vector of location values.</dd>


<dt>sigma</dt>
<dd>Vector of scale values.</dd>


<dt>log</dt>
<dd>Logical; If <code>TRUE</code>, values are returned on the log scale.</dd>


<dt>q</dt>
<dd>Vector of quantiles.</dd>


<dt>lower.tail</dt>
<dd>Logical; If <code>TRUE</code> (default), return P(X &lt;= x).
Else, return P(X &gt; x) .</dd>


<dt>log.p</dt>
<dd>Logical; If <code>TRUE</code>, values are returned on the log scale.</dd>


<dt>p</dt>
<dd>Vector of probabilities.</dd>


<dt>n</dt>
<dd>Number of draws to sample from the distribution.</dd>

</dl>

The Student-t Distribution

StudentT: The Student-t Distribution

Description

Usage

Arguments

Details

See Also