logm1

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

logm1 function

<dl><dt>x</dt>
<dd>A numeric or complex vector.</dd>
<dt>base</dt>
<dd>A positive or complex number: the base with respect to which
logarithms are computed. Defaults to e = <code>exp(1)</code>.</dd></dl>

Arguments

Logarithm with a minus one offset. — logm1

<dl>

<dt>x</dt>
<dd>A numeric or complex vector.</dd>


<dt>base</dt>
<dd>A positive or complex number: the base with respect to which
logarithms are computed. Defaults to e = <code>exp(1)</code>.</dd>

</dl>

Logarithm with a minus one offset.

logm1: Logarithm with a minus one offset.

Description

Usage

Arguments