Package: powerbrmsINLA Title: Bayesian Power Analysis Using 'brms' and 'INLA' Version: 1.3.0 Maintainer: Tony Myers Authors@R: person(given = "Tony", family = "Myers", role = c("aut", "cre"), email = "admyers@aol.com", comment = c(ORCID = "0000-0003-4516-4829")) Description: 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) "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) "brms: An R Package for Bayesian Multilevel Models using Stan". License: MIT + file LICENSE Encoding: UTF-8 RoxygenNote: 7.3.2 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 VignetteBuilder: knitr URL: https://github.com/Tony-Myers/powerbrmsINLA BugReports: https://github.com/Tony-Myers/powerbrmsINLA/issues Additional_repositories: https://inla.r-inla-download.org/R/stable NeedsCompilation: no Packaged: 2026-07-02 21:44:27 UTC; root Author: Tony Myers [aut, cre] (ORCID: ) Repository: https://cran.r-universe.dev Date/Publication: 2026-07-02 10:40:02 UTC RemoteUrl: https://github.com/cran/powerbrmsINLA RemoteRef: HEAD RemoteSha: 88fcb54eff6ad4f8635ce529fe69e4f33f6d7c0b