smoothbp
Hierarchical Piecewise Regression with Smoothed Change-Points
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.1 |
rolling linux/jammy R-4.5 | smoothbp_0.2.1.tar.gz |
1.3 MiB |
0.2.1 |
rolling linux/noble R-4.5 | smoothbp_0.2.1.tar.gz |
1.3 MiB |
0.2.1 |
rolling source/ R- | smoothbp_0.2.1.tar.gz |
2.8 MiB |
0.2.1 |
latest linux/jammy R-4.5 | smoothbp_0.2.1.tar.gz |
1.3 MiB |
0.2.1 |
latest linux/noble R-4.5 | smoothbp_0.2.1.tar.gz |
1.3 MiB |
0.2.1 |
latest source/ R- | smoothbp_0.2.1.tar.gz |
2.8 MiB |
0.2.1 |
2026-04-23 source/ R- | smoothbp_0.2.1.tar.gz |
0 B |