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powerbrmsINLA  

Bayesian Power Analysis Using 'brms' and 'INLA'
View on CRAN: Click here


Download and install powerbrmsINLA package within the R console
Install from CRAN:
install.packages("powerbrmsINLA")

Install from Github:
library("remotes")
install_github("cran/powerbrmsINLA")

Install by package version:
library("remotes")
install_version("powerbrmsINLA", "1.1.1")



Attach the package and use:
library("powerbrmsINLA")
Maintained by
Tony Myers
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-09-01
Latest Update: 2025-09-01
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) <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".
How to cite:
Tony Myers (2025). powerbrmsINLA: Bayesian Power Analysis Using 'brms' and 'INLA'. R package version 1.1.1, https://cran.r-project.org/web/packages/powerbrmsINLA. Accessed 21 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:43), 1.0.0 (2025-09-01 11:20), 1.1.1 (2025-11-16 20:30), 1.2.0 (2026-06-02 18:00)
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Complete documentation for powerbrmsINLA
Functions, R codes and Examples using the powerbrmsINLA R package
Full powerbrmsINLA package functions and examples
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