ExGaussian

dexgaussian

pexgaussian

rexgaussian

Density, distribution function, and random generation
for the exponentially modified Gaussian distribution with
mean <code>mu</code> and standard deviation <code>sigma</code> of the gaussian
component, as well as scale <code>beta</code> of the exponential
component.

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

Aki Vehtari

Mattan S. Ben-Shachar

Hayden Rabel

Simon C. Mills

Stephen Wild

Ven Popov

ExGaussian function

<dl><dt>x, q</dt>
<dd>Vector of quantiles.</dd>
<dt>mu</dt>
<dd>Vector of means of the combined distribution.</dd>
<dt>sigma</dt>
<dd>Vector of standard deviations of the gaussian component.</dd>
<dt>beta</dt>
<dd>Vector of scales of the exponential component.</dd>
<dt>log</dt>
<dd>Logical; If <code>TRUE</code>, values are returned on the log scale.</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>n</dt>
<dd>Number of draws to sample from the distribution.</dd></dl>

Arguments

The Exponentially Modified Gaussian Distribution — ExGaussian

<dl>

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


<dt>mu</dt>
<dd>Vector of means of the combined distribution.</dd>


<dt>sigma</dt>
<dd>Vector of standard deviations of the gaussian component.</dd>


<dt>beta</dt>
<dd>Vector of scales of the exponential component.</dd>


<dt>log</dt>
<dd>Logical; If <code>TRUE</code>, values are returned on the log scale.</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>n</dt>
<dd>Number of draws to sample from the distribution.</dd>

</dl>

The Exponentially Modified Gaussian Distribution

ExGaussian: The Exponentially Modified Gaussian Distribution

Description

Usage

Arguments

Details