NSP wrapper: Narrowest Significance Pursuit

Wraps the nsp package (Fryzlewicz 2024). NSP inverts the usual framing of post-selection inference: rather than estimating changepoint locations and then asking whether they are real, it returns a set of intervals, each of which contains at least one changepoint, with the guarantee holding globally across all intervals simultaneously at level alpha. The guarantee is exact and finite-sample, and the self-normalised and autoregressive variants keep it under heavy tails, heteroscedasticity and serial dependence.

Usage

nsp_wrapper(
  x,
  alpha = 0.1,
  variant = c("poly", "selfnorm", "ar", "tvreg"),
  change_in = c("mean", "slope"),
  deg = NULL,
  M = 1000,
  covariates = NULL,
  ord = 1,
  seed = NULL,
  ...
)

Arguments

x

A numeric vector.

alpha

Global significance level: with probability at least \(1 - \alpha\), every returned interval contains a changepoint. Defaults to 0.1.

variant

Which NSP procedure to run:

"poly": (default) nsp::nsp_poly(): piecewise polynomial signal, Gaussian noise of constant variance.
"selfnorm": nsp::nsp_poly_selfnorm(): self-normalised, for heavy tails and heteroscedasticity. Slower.
"ar": nsp::nsp_poly_ar(): autoregressive noise of order ord.
"tvreg": nsp::nsp_tvreg(): a general linear model whose coefficients change; requires covariates.

change_in

"mean" (a piecewise-constant signal, deg = 0) or "slope" (piecewise linear, deg = 1). Ignored when deg is given explicitly, and when variant = "tvreg" (which takes its model from covariates).

deg

Degree of the piecewise polynomial. Derived from change_in when NULL.

M

Number of intervals drawn. Defaults to 1000; the engine's own default.

covariates

A design matrix for variant = "tvreg", with one row per observation.

ord

AR order for variant = "ar". Defaults to 1.

seed

Optional seed. NSP draws random intervals, so a run is reproducible only with one. The seed is scoped to this call: .Random.seed is saved and restored, so a seeded call inside a simulation loop does not pin the loop's own stream.

...

Additional arguments passed to the underlying nsp function.

Value

A ggcpt object with a populated regions slot.

What `cp` means here, and what it does not

NSP produces no point estimates. This wrapper still fills the cp column (with the midpoint of each interval) because every downstream consumer in the package (augment(), cpt_metrics(), cpt_consensus(), autoplot()) is built on that column, and a result with an empty cp would silently score as "found nothing". The midpoint is not an estimate of the changepoint location and must not be reported as one: the interval is the inferential object. The result therefore

carries the intervals in a regions slot, read with cpt_regions();
marks itself, so print() says the cp column is a midpoint and autoplot() shades the bands by default;
adds a cp_source column reading "region_midpoint" to the changepoints tibble.

References

Fryzlewicz P (2024). “Narrowest significance pursuit: inference for multiple change-points in linear models.” Journal of the American Statistical Association, 119(546), 1633–1646. doi:10.1080/01621459.2023.2211733 .

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(), ocd_wrapper(), pilliat_wrapper(), segmented_wrapper(), smuce_wrapper(), sn_wrapper(), strucchange_wrapper(), taylor_wrapper(), tguh_wrapper(), trend_wrapper(), var_wrapper(), wbs2_wrapper(), wbs_wrapper(), wbsts_wrapper()

Examples

set.seed(2026)
x <- c(rnorm(100), rnorm(100, 4))
fit <- nsp_wrapper(x, M = 100, seed = 1)
cpt_regions(fit)
#> # A tibble: 1 × 4
#>   start   end length value
#>   <int> <int>  <int> <dbl>
#> 1    96   105     10  4.54
ggplot2::autoplot(fit)