dot-compute_weights_from_dist: Compute weights from a parametric prior distribution over an...

In powerbrmsINLA: Bayesian Power Analysis Using 'brms' and 'INLA'

Compute weights from a parametric prior distribution over an effect grid

Description

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

Usage

.compute_weights_from_dist(dist_spec, effect_values)

Arguments

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

Value

Named numeric vector of normalised weights (sums to 1).

powerbrmsINLA documentation built on July 2, 2026, 5:07 p.m.