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powerbrmsINLA

Overview

powerbrmsINLA provides tools for Bayesian power analysis and assurance calculations using the statistical frameworks of brms and INLA.

It includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE, Bayes factors, precision), sequential and two-stage adaptive designs, and a comprehensive suite of visualisation functions.

What’s New in 1.3.0

• Sequential Bayesian analysis module: sequential_design() for prespecifying a sequential analysis (with an MD5 fingerprint of all decision-relevant fields for preregistration), sequential_analysis() for interim monitoring of real accumulating data with an auditable decision trail, plot_sequential_monitor() for trajectory plots, and brms_inla_sequential_trial() for simulating a sequential design’s operating characteristics (stopping probabilities, expected sample size, early-stop exaggeration).
• Validation hardening: brms_inla_power() now raises an error when effect_name does not match a formula-level fixed-effect term and the built-in data generator is in use (previously such a name was silently ignored).
• decide_sample_size() in conditional mode now requires at least one decision target, and no longer mistakes the per-cell SD-moment summary columns for effect-grid columns.
• Breaking change: in brms_inla_power_sequential() summaries, the column previously named assurance is now conditional_power (the old name was statistically misleading).
• Bug fix: brms_inla_power_two_stage() no longer errors when called with default error_sd / obs_per_group.
• All engines now fail early with an informative message when INLA is absent.

What’s New in 1.2.0

• Unconditional Bayesian assurance via compute_assurance() — averages conditional power over a design prior on the effect size (O’Hagan & Stevens, 2001).
• assurance_prior_weights() for constructing normalised design-prior weights (normal, uniform, beta) over an effect grid.
• decide_sample_size() with both assurance mode (design prior) and conditional mode for recommending sample sizes from simulation output.
• validate_inla_vs_brms() for spot-checking INLA posterior estimates against brms/Stan.
• brms-to-INLA prior translation with full audit trail — specify analysis priors using brms::prior() syntax.
• Variance uncertainty integration — error_sd and group_sd now accept distributional specifications (halfnormal, lognormal, uniform) so that power is integrated over variance uncertainty.
• Marginal-likelihood Bayes factors (bf_method = "marglik") alongside the existing Savage-Dickey method.
• Automatic INLA thread detection when inla_num_threads = NULL.
• 15 new plotting functions for assurance, Bayes factor, decision-rule, precision, and multi-effect visualisation.
• Print methods for brms_inla_power, powerbrmsINLA_assurance, and powerbrmsINLA_sample_size objects.

See NEWS.md for the full changelog.

Installation

Install from CRAN:

install.packages("powerbrmsINLA")

INLA is listed under Suggests and must be installed separately:

if (!requireNamespace("INLA", quietly = TRUE)) {
  install.packages(
    "INLA",
    repos = c(getOption("repos"),
              INLA = "https://inla.r-inla-download.org/R/stable"),
    dep = TRUE
  )
}

To install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("Tony-Myers/powerbrmsINLA")

Quick Example

library(powerbrmsINLA)

# Step 1: Conditional power simulation
results <- brms_inla_power(
  formula      = y ~ treatment,
  effect_name  = "treatment",
  effect_grid  = c(0.2, 0.5, 0.8),
  sample_sizes = c(50, 100),
  nsims        = 50,
  seed         = 123
)
results$summary

# Step 2: Unconditional assurance (new in 1.2.0)
assurance <- compute_assurance(
  results,
  prior_weights = list(dist = "normal", mean = 0.5, sd = 0.2),
  metric = "direction"
)
print(assurance)

# Step 3: Sample size recommendation
decide_sample_size(
  results,
  direction = 0.80,
  prior_weights = list(dist = "normal", mean = 0.5, sd = 0.2)
)

Model Complexity Considerations

For optimal performance:

• Simple to moderate models: All sample sizes supported.
• Complex random effects (e.g., (1 + time | subject)): Recommend n >= 50 subjects.
• Large effect grids: Consider starting with fewer simulations (nsims = 50-100) for initial exploration, or use the sequential/two-stage engines.

Citation

If you use powerbrmsINLA in published work, please cite:

Myers, T. (2026). powerbrmsINLA: Bayesian Power Analysis Using ‘brms’ and ‘INLA’. R package version 1.2.0. https://cran.r-project.org/package=powerbrmsINLA

License

This package is released under the MIT License. See the LICENSE file for details.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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