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smoothbp  

Hierarchical Piecewise Regression with Smoothed Change-Points
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("smoothbp", "0.2.8")



Attach the package and use:
library("smoothbp")
Maintained by
Aidan D Bindoff
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-01
Latest Update: 2026-06-02
Description:
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>.
How to cite:
Aidan D Bindoff (2026). smoothbp: Hierarchical Piecewise Regression with Smoothed Change-Points. R package version 0.2.8, https://cran.r-project.org/web/packages/smoothbp. Accessed 24 Oct. 2026.
Previous versions and publish date:
(2026-08-28 23:31), 0.2.1 (2026-06-01 10:30), 0.2.2 (2026-06-02 08:40), 0.2.4 (2026-06-14 07:20), 0.2.7 (2026-06-16 09:20)
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Functions, R codes and Examples using the smoothbp R package
Full smoothbp package functions and examples
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