compute_adaptive_threshold_priors

Calculate threshold priors based on empirical quantiles to match brms centering

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

compute_adaptive_threshold_priors function

<dl><dt>y_ordered</dt>
<dd>Ordered factor response variable</dd>
<dt>method</dt>
<dd>Method for computing adaptive priors ("quantile" or "cumulative_mean")</dd></dl>

Arguments

Compute Data-Driven Threshold Priors — compute_adaptive_threshold_priors

<dl>

<dt>y_ordered</dt>
<dd>Ordered factor response variable</dd>


<dt>method</dt>
<dd>Method for computing adaptive priors ("quantile" or "cumulative_mean")</dd>

</dl>

compute_adaptive_threshold_priors: Compute Data-Driven Threshold Priors

Description

Usage

Value

Arguments