SMUCE / HSMUCE wrapper — multiscale changepoint inference

Wraps stepR::stepFit() for the Simultaneous MUltiscale Changepoint Estimator (SMUCE) of Frick, Munk and Sieling (2014) and its heterogeneous extension HSMUCE (Pein, Sieling and Munk, 2017). SMUCE estimates a step function subject to a simultaneous multiscale test at level alpha; the level bounds the probability of over-estimating the number of changepoints, and the fit delivers confidence intervals for every changepoint location, which populate the ci_lower/ci_upper columns of the result and render via autoplot(show_ci = TRUE) or geom_cpt_ci().

Usage

smuce_wrapper(x, alpha = 0.5, family = c("gauss", "hsmuce"), ...)

Arguments

x

A numeric vector.

alpha

Significance level of the multiscale test in \((0, 1)\); smaller values yield more conservative (fewer-changepoint) fits. Defaults to 0.5, the upstream recommendation for estimation.

family

Noise model: "gauss" (SMUCE, homogeneous Gaussian noise) or "hsmuce" (HSMUCE, segment-wise variance). Defaults to "gauss". The remaining stepR families ("jsmurf", "mDependentPS", ...) all require a filter or covariance specification; call stepR::stepFit() directly for those. "hsmuce" additionally refuses a series whose point-to-point variation lies more than about seven orders of magnitude below its own scale — a globally flat series, or a step whose segments are numerically constant, as cpt_simulate(sd = 0) produces once any rounding is added. stepR's heterogeneous variance estimator aborts the R session on such input rather than raising an error, so it cannot be caught. "gauss" handles the whole range.

...

Additional arguments passed to stepR::stepFit().

Value

A ggcpt object. The changepoints tibble carries ci_lower/ci_upper (confidence interval for each changepoint location) and the data tibble carries the SMUCE step fit in its fitted column.

References

Frick K, Munk A, Sieling H (2014). “Multiscale change point inference.” Journal of the Royal Statistical Society: Series B, 76(3), 495–580.

Pein F, Sieling H, Munk A (2017). “Heterogeneous change point inference.” Journal of the Royal Statistical Society: Series B, 79(4), 1207–1227.

See also

Other changepoint engines: bcp_wrapper(), beast_wrapper(), bfast_wrapper(), binsegrcpp_wrapper(), bocpd_wrapper(), cpm_wrapper(), cpop_wrapper(), cpt_wrapper(), decafs_wrapper(), ecp_wrapper(), envcpt_wrapper(), esac_wrapper(), fabisearch_wrapper(), fastcpd_wrapper(), fcov_wrapper(), fmean_wrapper(), fpop_wrapper(), geomcp_wrapper(), hdcov_wrapper(), hdreg_wrapper(), idetect_wrapper(), inspect_wrapper(), kcp_wrapper(), kwc_wrapper(), mcp_wrapper(), mosum_wrapper(), network_wrapper(), not_wrapper(), npmojo_wrapper(), nsp_wrapper(), ocd_wrapper(), pilliat_wrapper(), segmented_wrapper(), sn_wrapper(), strucchange_wrapper(), taylor_wrapper(), tguh_wrapper(), trend_wrapper(), var_wrapper(), wbs2_wrapper(), wbs_wrapper(), wbsts_wrapper()

Examples

# \donttest{
set.seed(2026)
x <- c(rnorm(100), rnorm(100, 3))
res <- smuce_wrapper(x)
res$changepoints
#> # A tibble: 1 × 4
#>      cp cp_value ci_lower ci_upper
#>   <int>    <dbl>    <int>    <int>
#> 1   100    0.369       99      101
ggplot2::autoplot(res, show_ci = TRUE)

# }