hdi_of_icdf

Computes an HDI of given mass from any distribution for which you have a
quantile function (inverse CDF).

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'

hdi_of_icdf function

<dl><dt>qfun</dt>
<dd>Quantile function, e.g., <code>qbeta</code>, <code>qnorm</code>, ...</dd>
<dt>width</dt>
<dd>Desired HDI mass (e.g., 0.95).</dd>
<dt>tol</dt>
<dd>Optimizer tolerance.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code>qfun</code>.</dd></dl>

Arguments

Highest Density Interval from an Inverse CDF — hdi_of_icdf

<dl>

<dt>qfun</dt>
<dd>Quantile function, e.g., <code>qbeta</code>, <code>qnorm</code>, ...</dd>


<dt>width</dt>
<dd>Desired HDI mass (e.g., 0.95).</dd>


<dt>tol</dt>
<dd>Optimizer tolerance.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code>qfun</code>.</dd>

</dl>

hdi_of_icdf: Highest Density Interval from an Inverse CDF

Description

Usage

Value

Arguments