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Self-normalisation wrapper (SNSeg)

Source: R/wrap-robust.R
sn_wrapper.Rd

Wraps SNSeg::SNSeg_Uni() (Zhao, Jiang and Shao, 2022): self-normalised segmentation with nested local windows. Self-normalisation avoids estimating the long-run variance, is robust to temporal dependence, and detects changes in general parameters — mean, variance, quantiles, autocorrelation, or bivariate correlation — within one framework.

Usage

sn_wrapper(
  x,
  parameter = c("mean", "variance", "acf", "bivcor"),
  confidence = 0.9,
  grid_size = NULL,
  ...
)

Arguments

x

A numeric vector (or a two-column matrix for parameter = "bivcor").

parameter

Which parameter to test for changes: "mean", "variance", "acf", or "bivcor" (bivariate correlation). Defaults to "mean".

confidence

Confidence level of the self-normalised test, one of 0.9, 0.95, 0.99, 0.995 or 0.999. Defaults to 0.9.

grid_size

Grid size controlling the local-window sweep; when NULL the engine's default is used.

...

Additional arguments passed to SNSeg::SNSeg_Uni().

Value

A ggcpt object. About 20 observations are needed for the nested local windows at the default grid_size; a constant series returns an empty result rather than an engine error.

References

Zhao Z, Jiang F, Shao X (2022). “Segmenting time series via self-normalisation.” Journal of the Royal Statistical Society: Series B, 84(5), 1699–1725.

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(), smuce_wrapper(), strucchange_wrapper(), taylor_wrapper(), tguh_wrapper(), trend_wrapper(), var_wrapper(), wbs2_wrapper(), wbs_wrapper(), wbsts_wrapper()

Examples

# \donttest{
set.seed(2026)
res <- sn_wrapper(c(rnorm(150), rnorm(150, 3)))
res$changepoints
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   150    -1.56
# }