Bayesian Power Analysis Using 'brms' and 'INLA' R package powerbrmsINLA version 1.3.0 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". Software Depends: R (>= 4.1.0) Imports: brms (>= 2.19.0), dplyr (>= 1.1.0), ggplot2 (>= 3.4.0), pbapply, rlang (>= 1.1.0), tibble (>= 3.2.0), scales (>= 1.2.0), viridisLite (>= 0.4.0), stats, tools, utils, magrittr (>= 2.0.0) Suggests: INLA (>= 22.05.07), testthat (>= 3.0.0), rmarkdown, knitr, MASS, circular, sn Tony Myers <admyers@aol.com> Comprehensive R Archive Network (CRAN) Tony Myers [aut, cre] (ORCID: <https://orcid.org/0000-0003-4516-4829>) MIT + file LICENSE (https://CRAN.R-project.org/package=powerbrmsINLA/LICENSE) 2026-07-02 application/tgz https://CRAN.R-project.org/package=powerbrmsINLA doi:10.32614/CRAN.package.powerbrmsINLA