Self-normalisation wrapper (SNSeg) — sn

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.

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.

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
# }