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smoothbp

Hierarchical Piecewise Regression with Smoothed Change-Points

v0.2.7 · Jun 16, 2026 · MIT + file LICENSE

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>.

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OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 17, 2026
ERROR 12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Jun 14, 2026
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Maintainer: ‘Aidan D Bindoff <aidan.bindoff@utas.edu.au>’

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OK 8 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 3, 2026
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ERROR r-devel-linux-x86_64-fedora-clang

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Reverse Dependencies (1)

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Dependency Network

Dependencies Reverse dependencies posterior ggplot2 bridgesampling loo bayesplot fibr smoothbp

Version History

4 tracked
updated 0.2.7 ← 0.2.4 diff Jun 16, 2026
updated 0.2.4 ← 0.2.2 diff Jun 14, 2026
updated 0.2.2 ← 0.2.1 diff Jun 2, 2026
new 0.2.1 Jun 1, 2026