qbrmb

Fits a regularised binomial (or Bernoulli) mixed-effects model using INLA,
with enhanced diagnostics, stability checks and strategy selection.

Provides a 'brms'-like interface for fitting Bayesian regression models
using 'INLA' (Integrated Nested Laplace Approximations) and 'TMB' (Template Model Builder).
The package offers faster model fitting while maintaining familiar 'brms' syntax and
output formats. Supports fixed and mixed effects models, multiple probability distributions,
conditional effects plots, and posterior predictive checks with summary methods compatible
with 'brms'. 'TMB' integration provides fast ordinal regression capabilities. Implements
methods adapted from 'emmeans' for marginal means estimation and 'bayestestR' for Bayesian
inference assessment. Methods are based on Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x>,
Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, Lenth (2016) <doi:10.18637/jss.v069.i01>,
Bürkner (2017) <doi:10.18637/jss.v080.i01>, Makowski et al. (2019) <doi:10.21105/joss.01541>,
and Kruschke (2014, ISBN:9780124058880).

Tony Myers

qbrms

Quick Bayesian Regression Models Using 'INLA' with 'brms' Syntax

qbrmb function

<dl><dt>formula</dt>
<dd>Model formula with random effects in lme4-style syntax.</dd>
<dt>data</dt>
<dd>Data frame containing the variables in the model.</dd>
<dt>family</dt>
<dd>Model family (currently <code>"binomial"</code> or <code>"bernoulli"</code>;
default <code>"binomial"</code>).</dd>
<dt>strategy</dt>
<dd>Fitting strategy: <code>"auto"</code>, <code>"enhanced"</code>,
<code>"aggressive"</code>, or <code>"minimal"</code>.</dd>
<dt>regularisation_strength</dt>
<dd>Regularisation strength in the interval
\([0, 1]\) (default <code>0.1</code>).</dd>
<dt>use_data_augmentation</dt>
<dd>Logical; if <code>TRUE</code>, add pseudo-observations
for additional numerical stability.</dd>
<dt>min_group_size</dt>
<dd>Minimum group size before triggering diagnostic
warnings.</dd>
<dt>verbose</dt>
<dd>Logical; if <code>TRUE</code>, show detailed progress and
diagnostics while fitting.</dd>
<dt>diagnostics</dt>
<dd>Logical; if <code>TRUE</code>, compute and store extended
diagnostics in the returned object.</dd>
<dt>silent</dt>
<dd>Logical; if <code>TRUE</code>, suppress printed output except
errors.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code>INLA::inla()</code>.</dd></dl>

Arguments

Enhanced binomial mixed-effects modelling — qbrmb

<dl>

<dt>formula</dt>
<dd>Model formula with random effects in lme4-style syntax.</dd>


<dt>data</dt>
<dd>Data frame containing the variables in the model.</dd>


<dt>family</dt>
<dd>Model family (currently <code>"binomial"</code> or <code>"bernoulli"</code>;
default <code>"binomial"</code>).</dd>


<dt>strategy</dt>
<dd>Fitting strategy: <code>"auto"</code>, <code>"enhanced"</code>,
<code>"aggressive"</code>, or <code>"minimal"</code>.</dd>


<dt>regularisation_strength</dt>
<dd>Regularisation strength in the interval
\([0, 1]\) (default <code>0.1</code>).</dd>


<dt>use_data_augmentation</dt>
<dd>Logical; if <code>TRUE</code>, add pseudo-observations
for additional numerical stability.</dd>


<dt>min_group_size</dt>
<dd>Minimum group size before triggering diagnostic
warnings.</dd>


<dt>verbose</dt>
<dd>Logical; if <code>TRUE</code>, show detailed progress and
diagnostics while fitting.</dd>


<dt>diagnostics</dt>
<dd>Logical; if <code>TRUE</code>, compute and store extended
diagnostics in the returned object.</dd>


<dt>silent</dt>
<dd>Logical; if <code>TRUE</code>, suppress printed output except
errors.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code>INLA::inla()</code>.</dd>

</dl>

qbrmb: Enhanced binomial mixed-effects modelling

Description

Usage

Value

Arguments