powerbrmsINLA1.1.1 package

Bayesian Power Analysis Using 'brms' and 'INLA'

brms_inla_power_parallel

Parallel wrapper for fixed-design Bayesian power / assurance simulatio...

brms_inla_power_sequential

Sequential Bayesian Assurance Simulation Engine (Modern, Multi-Effect ...

dot-compute_assurance

Compute Mean Assurance for a Given Metric (Multi-Effect Compatible) Su...

dot-geom_line_lw

Create a ggplot2 Line Layer with Version-Compatible Width

dot-geom_point_lw

Create a ggplot2 Point Layer with Version-Compatible Width

dot-gg_line_arg

Compute Mean Assurance for a Given Metric (Modern, Multi-Effect Compat...

dot-map_brms_priors_to_inla

Map brms Priors to INLA Priors (Multi-Fixed)

dot-parse_re_terms

Parse brms-like Random Effects Terms (Modern Robust)

dot-plot_decision_assurance_curve_from_summary

Plot decision/assurance curve across n

dot-scale_fill_viridis_continuous

Scale Fill for Viridis Continuous Data

dot-scale_fill_viridis_discrete

Scale Fill for Viridis Discrete Data

dot-should_stop_binom

Wilson Confidence Interval Early Stopping Rule Determines whether to s...

dot-to_inla_family

Map a brms Family to an INLA Family (Modern, Robust)

hdi_of_icdf

Highest Density Interval from an Inverse CDF

min_n_beta_binom

Minimum n for Target Assurance (Beta-Binomial)

or_or

Internal Coalesce Operator Returns the left-hand side if it is not NUL...

plot_assurance_with_robustness

Plot Assurance with Robustness Ribbon (Multi-Effect Grid Friendly)

plot_bf_assurance_curve_smooth

Bayes-factor assurance curve with Wilson CIs (multi-effect grid friend...

plot_bf_assurance_curve

Bayes-factor assurance curve (user-facing wrapper)

plot_bf_expected_evidence

Plot Expected Evidence (mean log10 BF10, Multi-Effect Grid Friendly)

plot_bf_heatmap

Plot Bayes Factor Heatmap (mean log10 BF10, Multi-Effect Grid Friendly...

add_decision_overlay

Add sample-size decision overlay to an assurance contour

beta_binom_power

Analytic Assurance for Beta-Binomial Designs

beta_weights_on_grid

Beta-Prior Weights Over an Effect Grid

brms_inla_power_design

Design-based wrapper for Bayesian power / assurance

brms_inla_power_two_stage

Two-Stage Bayesian Assurance Simulation (Multi-Effect, User-Friendly A...

brms_inla_power

Core Bayesian Assurance / Power Simulation (Modern, Multi-Effect Ready...

decide_sample_size

Decide recommended sample size from power/assurance results

dot-add_contour_lines

Add Contour Lines to a ggplot2 Plot

dot-auto_data_generator

Automatic Data Generator for brms + INLA Simulation (Multi-Effect Read...

dot-brms_to_inla_formula2

Convert brms Formula to INLA Formula (Multi-Fixed Support)

plot_decision_assurance_curve

Plot Decision Assurance Curve (Multi-Effect Grid Friendly)

plot_decision_threshold_contour

Plot Decision Threshold Contour (Multi-Effect Grid Friendly)

plot_interaction_surface

Plot Interaction Assurance Surface/Heatmap/Lines (Multi-Effect Grid Fr...

plot_power_contour

Draw a filled contour plot of assurance for a chosen metric, as a func...

plot_power_heatmap

Plot Bayesian Power / Assurance Heatmap (Multi-Effect Grid Friendly)

plot_precision_assurance_curve

Plot Precision Assurance Curve (Multi-Effect Grid Friendly)

plot_precision_fan_chart

Precision assurance as a function of sample size

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".

  • Maintainer: Tony Myers
  • License: MIT + file LICENSE
  • Last published: 2025-11-16