kcp_wrapper: Kernel changepoint wrapper (KCP on running statistics)

In ggchangepoint: Combines Changepoint Analysis with 'ggplot2'

Kernel changepoint wrapper (KCP on running statistics)

Description

Wraps kcpRS::kcpRS() (Cabrieto et al., 2018; the KCP framework of Arlot, Celisse and Harchaoui, 2019). The data are mapped to a running statistic (mean, variance, autocorrelation, or correlation) computed on a sliding window, and a Gaussian-kernel change point analysis with a permutation significance test is run on the statistic. Detecting changes in running correlations or variances captures higher-order changes that mean-based methods miss. Multivariate input (matrix or data frame) is supported.

Usage

kcp_wrapper(
  x,
  running_stat = c("mean", "var", "autocorr", "corr"),
  wsize = 25,
  nperm = 1000,
  kmax = 10,
  alpha = 0.05,
  seed = NULL,
  ...
)

Arguments

x	A numeric vector, matrix, or data frame (columns are variables).
running_stat	Which running statistic to monitor: "mean", "var", "autocorr", or "corr" (correlation requires at least two columns). Defaults to "mean".
wsize	Sliding window size for the running statistic. Defaults to 25.
nperm	Number of permutations for the significance test, at least 2. Defaults to 1000. Fewer than two leaves the engine with no permutation distribution: it reports no changepoints at all for 0, and fails inside its own code for 1.
kmax	Maximum number of changepoints considered. Defaults to 10.
alpha	Significance level of the permutation test. Defaults to 0.05.
seed	Optional seed for reproducibility of the permutation test. 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 kcpRS::kcpRS().

Value

A ggcpt object. Reported locations refer to the centre of the sliding window in which the change occurs. The series must be at least wsize long to form one window. Constant coordinates make every running statistic NA, so they are dropped (with a warning) before detection and an all-constant input returns an empty result.

References

\insertRef

arlot2019kernelggchangepoint

\insertRef

cabrieto2018kcprsggchangepoint

See Also

Other changepoint engines: bcp_wrapper(), beast_wrapper(), bfast_wrapper(), binsegrcpp_wrapper(), bocpd_wrapper(), capa_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(), kwc_wrapper(), mcp_wrapper(), mosum_wrapper(), network_wrapper(), not_wrapper(), npmojo_wrapper(), nsp_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

res <- kcp_wrapper(c(rnorm(60), rnorm(60, 3)), nperm = 100, seed = 2026)
res$changepoints

ggchangepoint documentation built on Nov. 5, 2026, 9:06 a.m.