brms_inla_power_design

Dispatches to one of the three engines depending on <code>design</code>.
This function must accept <code>...</code> and pass it on unchanged.

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'

brms_inla_power_design function

<dl><dt>design</dt>
<dd>Character scalar: "fixed", "two_stage", or "sequential".</dd>
<dt>...</dt>
<dd>Arguments passed on to the corresponding engine.</dd></dl>

Arguments

Design-based wrapper for Bayesian power / assurance — brms_inla_power_design

<dl>

<dt>design</dt>
<dd>Character scalar: "fixed", "two_stage", or "sequential".</dd>


<dt>...</dt>
<dd>Arguments passed on to the corresponding engine.</dd>

</dl>

brms_inla_power_design: Design-based wrapper for Bayesian power / assurance

Description

Usage

Value

Arguments