The goal of 'BayesPower' is to provide tools for Bayesian sample size determination and power analysis across a range of common hypothesis testing scenarios using Bayes factors. The main function, BayesPower_BayesFactor(), launches an interactive 'shiny' application for performing these analyses. The application also provides command-line code for reproducibility. Details of the methods are described in the tutorial by Wong, Pawel, and Tendeiro (2025) <doi:10.31234/osf.io/pgdac_v3>.

Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for generalized linear models.
  Detailed examples of applying the package are available at <doi:10.32614/RJ-2023-016>.
  Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'.
  The Bayesian clinical trial design methodology is described in Chen et al. (2011) 
  <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) 
  <doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006) 
  <doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.

Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for proportional hazards models
  with piecewise constant hazard. The methodology and examples of 
  applying the package are detailed in <doi:10.48550/arXiv.2404.05118>.
  The Bayesian clinical trial design methodology is described in 
  Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, 
  and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. 
  The proportional hazards model with piecewise constant hazard is detailed in 
  Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.

Bayesian fitting of projection pursuit regression model. 
  Built to handle continuous and categorical inputs and
  scalar output (Collins et al., 2023 <DOI:10.1007/s11222-023-10334-z>).

Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) <doi:10.1007/s11222-020-09966-2>) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms.