smoothbp_ss: Fit a smooth change-point model with spike-and-slab variable...

In smoothbp: Hierarchical Piecewise Regression with Smoothed Change-Points

Fit a smooth change-point model with spike-and-slab variable selection

Description

Fit a smooth change-point model with spike-and-slab variable selection

Usage

smoothbp_ss(
  formula,
  b0 = ~1,
  b1 = ~1,
  deltas = list(~1),
  omega = list(~1),
  rho = list(~1),
  data,
  priors = smoothbp_priors(),
  spike = prior_spike_slab(),
  b1_spike = FALSE,
  hierarchical = NULL,
  chains = 4L,
  iter = 2000L,
  warmup = 1000L,
  seed = NULL,
  step_om = 0.3,
  step_rho = 0.3,
  target_accept = 0.9,
  cores = getOption("smoothbp.cores", 1L),
  reparameterise = c("none", "omega"),
  .verbose = TRUE
)

Arguments

formula	A two-sided formula.
b0	One-sided formula for b0.
b1	One-sided formula for b1.
deltas	List of formulas for slope changes.
omega	List of formulas for change-points. Can also contain fixed() values.
rho	List of formulas for sharpness. Can also contain fixed() values.
data	A data frame.
priors	A smoothbp_priors object.
spike	A prior_spike_slab() object.
b1_spike	Logical; should b1 coefficients be eligible for spike-and-slab?
hierarchical	Character vector specifying which parameters should be hierarchical. Currently only "omega" is supported.
chains	Number of chains.
iter	Total iterations.
warmup	Warmup iterations.
seed	Random seed.
step_om, step_rho, target_accept	HMC/MH tuning parameters.
cores	Number of CPU cores.
reparameterise	Character specifying the parameterisation for random change-points: "none" (centred) or "omega" (fully non-centred). Default is "none". Only used if random effects are present.
.verbose	Print progress.

Value

A smoothbp_fit object.

smoothbp documentation built on June 14, 2026, 9:06 a.m.