Hierarchical Piecewise Regression with Smoothed Change-Points R package smoothbp version 0.2.7 Fits Bayesian hierarchical piecewise regression models with multiple logistic-smoothed change-points. Non-linear parameters (change-point locations and transition sharpness) and linear parameters can each be conditioned on covariates and factors via flexible design matrices. A random-intercept structure is supported for any parameter. Spike-and-slab regularization is supported for selecting the number of breakpoints. Posterior inference uses a Metropolis-within-Gibbs sampler implemented in 'Rust' for speed. Methods are based on the smooth transition piecewise regression model of Bacon and Watts (1971) <doi:10.2307/2334389> and variable selection spike-and-slab priors of Kuo and Mallick (1998) <https://www.jstor.org/stable/25053023>. Software Depends: R (>= 4.2) Imports: posterior, ggplot2, stats, bridgesampling, loo, bayesplot Suggests: testthat (>= 3.0.0), withr, knitr, rmarkdown, brms, mcp, dplyr, gt, tidyr, scales, rjags Aidan D Bindoff <aidan.bindoff@utas.edu.au> Comprehensive R Archive Network (CRAN) Aidan D Bindoff [aut, cre] (ORCID: <https://orcid.org/0000-0002-0943-2702>) MIT + file LICENSE (https://CRAN.R-project.org/package=smoothbp/LICENSE) 2026-06-16 application/tgz https://CRAN.R-project.org/package=smoothbp doi:10.32614/CRAN.package.smoothbp en-GB