This package is considered a duplicate. The official version of this package is found at:https://tony-myers.r-universe.dev/powerbrmsINLA

Package: powerbrmsINLA 1.3.0

Tony Myers

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

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".

Authors:Tony Myers [aut, cre]

powerbrmsINLA_1.3.0.tar.gz
powerbrmsINLA_1.3.0.tar.gz(r-4.7-any)powerbrmsINLA_1.3.0.tar.gz(r-4.6-any)
powerbrmsINLA_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
powerbrmsINLA/json (API)

# Install 'powerbrmsINLA' in R:

install.packages('powerbrmsINLA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tony-myers/powerbrmsinla/issues

On CRAN:

3.30 score 6 scripts 459 downloads 35 exports 76 dependencies

Last updated from:88fcb54eff. Checks:4 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	244
source / vignettes	OK	248
linux-release-x86_64	OK	234
wasm-release	OK	197

Exports:%||%add_decision_overlay assurance_prior_weights beta_binom_power beta_weights_on_grid brms_inla_power brms_inla_power_design brms_inla_power_parallel brms_inla_power_sequential brms_inla_power_two_stage brms_inla_sequential_trial compute_assurance decide_sample_size hdi_of_icdf min_n_beta_binom plot_assurance_curve plot_assurance_with_robustness plot_bf_assurance_curve plot_bf_assurance_curve_smooth plot_bf_expected_evidence plot_bf_heatmap plot_decision_assurance_curve plot_decision_threshold_contour plot_design_prior plot_interaction_surface plot_power_assurance_overlay plot_power_contour plot_power_heatmap plot_precision_assurance_curve plot_precision_fan_chart plot_sequential_monitor sequential_analysis sequential_design validate_inla_vs_brms validate_sd_spec

Dependencies:abind backports bayesplot BH bridgesampling brms Brobdingnag callr checkmate cli coda codetools cpp11 desc digest distributional dplyr farver future future.apply generics ggplot2 ggridges globals glue gridExtra gtable inline isoband labeling lattice lifecycle listenv loo magrittr Matrix matrixStats mgcv mvtnorm nleqslv nlme numDeriv otel parallelly pbapply pillar pkgbuild pkgconfig plyr posterior processx ps purrr QuickJSR R6 RColorBrewer Rcpp RcppEigen RcppParallel reshape2 rlang rstan rstantools S7 scales StanHeaders stringi stringr tensorA tibble tidyr tidyselect utf8 vctrs viridisLite withr

Validation against classical power and Bayesian assurance

Rendered fromvalidation.Rmdusingknitr::rmarkdown

Last update: 2026-06-02
Started: 2026-06-02

Help page	Topics
Add sample-size decision overlay to a conditional power contour	add_decision_overlay
Create prior weights over an effect grid for use with compute_assurance()	assurance_prior_weights
Analytic Assurance for Beta-Binomial Designs	beta_binom_power
Beta-Prior Weights Over an Effect Grid	beta_weights_on_grid
Core Bayesian Assurance / Power Simulation (Modern, Multi-Effect Ready)	brms_inla_power
Design-based wrapper for Bayesian power / assurance	brms_inla_power_design
Parallel wrapper for fixed-design Bayesian power / assurance simulations	brms_inla_power_parallel
Sequential Bayesian Assurance Simulation Engine (Modern, Multi-Effect Ready)	brms_inla_power_sequential
Two-Stage Bayesian Assurance Simulation (Multi-Effect, User-Friendly API)	brms_inla_power_two_stage
Simulate a sequential Bayesian trial with interim stopping rules	brms_inla_sequential_trial
Compute unconditional Bayesian assurance from simulation results	compute_assurance
Decide recommended sample size from power/assurance results	decide_sample_size
Highest Density Interval from an Inverse CDF	hdi_of_icdf
Minimum n for Target Assurance (Beta-Binomial)	min_n_beta_binom
Plot Assurance Curve(s) vs Sample Size	plot_assurance_curve
Plot Conditional Power with Robustness Ribbon (Multi-Effect Grid Friendly)	plot_assurance_with_robustness
Bayes-factor conditional power curve (user-facing wrapper)	plot_bf_assurance_curve
Conditional Bayesian power curve for the Bayes factor criterion with Wilson CIs (multi-effect grid friendly)	plot_bf_assurance_curve_smooth
Plot Expected Evidence (mean log10 BF10, Multi-Effect Grid Friendly)	plot_bf_expected_evidence
Plot Bayes Factor Heatmap (mean log10 BF10, Multi-Effect Grid Friendly)	plot_bf_heatmap
Plot Conditional Power Curve for a Decision Rule (Multi-Effect Grid Friendly)	plot_decision_assurance_curve
Plot Decision Threshold Contour (Multi-Effect Grid Friendly)	plot_decision_threshold_contour
Plot Design Prior Density over the Effect Grid	plot_design_prior
Plot Interaction Conditional Power Surface/Heatmap/Lines (Multi-Effect Grid Friendly)	plot_interaction_surface
Plot Conditional Power Curves with Assurance Overlay	plot_power_assurance_overlay
Draw a filled contour plot of conditional Bayesian power for a chosen metric, as a function of two effect grid columns and sample size.	plot_power_contour
Plot Conditional Bayesian Power Heatmap (Multi-Effect Grid Friendly)	plot_power_heatmap
Plot Precision Conditional Power Curve (Multi-Effect Grid Friendly)	plot_precision_assurance_curve
Precision conditional power as a function of sample size	plot_precision_fan_chart
Plot a sequential analysis trajectory with stopping boundaries	plot_sequential_monitor
Print method for brms_inla_power result objects	print.brms_inla_power
Print method for powerbrmsINLA_assurance objects	print.powerbrmsINLA_assurance
Print method for powerbrmsINLA_sample_size objects	print.powerbrmsINLA_sample_size
Print method for sequential design objects	print.powerbrmsINLA_seq_design
Print method for sequential analysis monitor objects	print.powerbrmsINLA_seq_monitor
Print method for sequential trial simulation results	print.powerbrmsINLA_seq_trial
Analyse accumulated real data at a sequential interim look	sequential_analysis
Prespecify a sequential Bayesian analysis design	sequential_design
Spot-check INLA posterior estimates against brms/Stan	validate_inla_vs_brms
Validate an SD Specification for error_sd or group_sd	validate_sd_spec

Package: powerbrmsINLA 1.3.0

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

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Help Manual

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