handle_missing_data

Process missing data in model variables, similar to brms handling.

internal

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

handle_missing_data function

<dl><dt>formula</dt>
<dd>Model formula.</dd>
<dt>data</dt>
<dd>Data frame.</dd>
<dt>verbose</dt>
<dd>Logical, whether to print messages.</dd></dl>

Arguments

Handle Missing Data — handle_missing_data

<dl>

<dt>formula</dt>
<dd>Model formula.</dd>


<dt>data</dt>
<dd>Data frame.</dd>


<dt>verbose</dt>
<dd>Logical, whether to print messages.</dd>

</dl>

handle_missing_data: Handle Missing Data

Description

Usage

Value

Arguments