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>.
| Version: |
0.2.7 |
| 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 |
| Published: |
2026-06-16 |
| DOI: |
10.32614/CRAN.package.smoothbp |
| Author: |
Aidan D Bindoff 
[aut, cre] |
| Maintainer: |
Aidan D Bindoff <aidan.bindoff at utas.edu.au> |
| BugReports: |
https://github.com/ABindoff/smoothbp/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/ABindoff/smoothbp |
| NeedsCompilation: |
yes |
| SystemRequirements: |
Cargo (Rust's package manager), rustc >= 1.65.0 |
| Language: |
en-GB |
| Materials: |
README, NEWS |
| CRAN checks: |
smoothbp results |