.compute_weights_from_dist

Evaluates a named distribution at each value in <code>effect_values</code>, normalises
the result to sum to 1, and returns a named numeric vector compatible with
<code>compute_assurance()</code> and <code>beta_weights_on_grid()</code>.

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'

.compute_weights_from_dist function

<dl><dt>dist_spec</dt>
<dd>A list with at minimum a <code>$dist</code> element naming the
distribution. Remaining elements are distribution parameters:<ul>
<li><code>"normal"</code> — <code>mean</code>, <code>sd</code></li>
<li><code>"uniform"</code> — <code>min</code>, <code>max</code> (defaults: range of <code>effect_values</code>)</li>
<li><code>"beta"</code> — either <code>shape1</code>, <code>shape2</code> or <code>mode</code>, <code>n</code> (concentration;
must be &gt; 2). Effects are rescaled to $[0, 1]$ automatically when
outside that interval.</li>
</ul></dd>
<dt>effect_values</dt>
<dd>Numeric vector of effect-grid values at which to
evaluate the density.</dd></dl>

Arguments

Compute weights from a parametric prior distribution over an effect grid — .compute_weights_from_dist

<dl>

<dt>dist_spec</dt>
<dd>A list with at minimum a <code>$dist</code> element naming the
distribution. Remaining elements are distribution parameters:<ul>
<li><code>"normal"</code> — <code>mean</code>, <code>sd</code></li>
<li><code>"uniform"</code> — <code>min</code>, <code>max</code> (defaults: range of <code>effect_values</code>)</li>
<li><code>"beta"</code> — either <code>shape1</code>, <code>shape2</code> or <code>mode</code>, <code>n</code> (concentration;
must be &gt; 2). Effects are rescaled to $[0, 1]$ automatically when
outside that interval.</li>
</ul></dd>


<dt>effect_values</dt>
<dd>Numeric vector of effect-grid values at which to
evaluate the density.</dd>

</dl>

.compute_weights_from_dist: Compute weights from a parametric prior distribution over an effect grid

Description

Usage

Value

Arguments