.eval_prior_density

Evaluate a prior density over a continuous x grid

internal

Provides tools for Bayesian power analysis and assurance calculations using the statistical frameworks of 'brms' and 'INLA'. Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) <doi:10.1177/0272989X0102100307> "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) <doi:10.1177/2515245918771304> "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) <doi:10.18637/jss.v080.i01> "brms: An R Package for Bayesian Multilevel Models using Stan".

Tony Myers

powerbrmsINLA

Bayesian Power Analysis Using 'brms' and 'INLA'

.eval_prior_density function

<dl><dt>spec</dt>
<dd>List with <code>$dist</code> and distribution parameters.</dd>
<dt>x</dt>
<dd>Numeric vector of evaluation points.</dd>
<dt>effect_grid</dt>
<dd>Numeric effect-grid values (used for beta rescaling range).</dd></dl>

Arguments

Evaluate a prior density over a continuous x grid — .eval_prior_density

<dl>

<dt>spec</dt>
<dd>List with <code>$dist</code> and distribution parameters.</dd>


<dt>x</dt>
<dd>Numeric vector of evaluation points.</dd>


<dt>effect_grid</dt>
<dd>Numeric effect-grid values (used for beta rescaling range).</dd>

</dl>

.eval_prior_density: Evaluate a prior density over a continuous x grid

Description

Usage

Value

Arguments