fastcpd wrapper — fast changepoint detection via sequential gradient descent

Wraps the fastcpd package (Li and Zhang, 2024), a modern PELT-family engine that pairs pruning with sequential gradient descent so that exact or near-exact segmentations of many model families run in near-linear time. This wrapper exposes the time-series families most useful alongside the other engines: mean, variance, mean-and-variance, and AR/ARMA/GARCH model changepoints.

Usage

fastcpd_wrapper(
  x,
  family = c("mean", "variance", "meanvariance", "ar", "arma", "garch"),
  order = NULL,
  ...
)

Arguments

x: A numeric vector, or (for family "mean", "variance", "meanvariance") a matrix with one row per time point for multivariate detection.
family: Model family: "mean", "variance", "meanvariance", "ar", "arma", or "garch". Defaults to "mean".
order: Model order for "ar" (a single integer), "arma" (length-2), or "garch" (length-2). Defaults to 1 for AR, c(1, 1) otherwise.
...: Additional arguments passed to the corresponding fastcpd::fastcpd.*() function (e.g. beta, trim).

Value

A ggcpt object.

References

Li X, Zhang X (2024). “fastcpd: Fast change point detection in R.” arXiv preprint arXiv:2404.05933.

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

Examples

set.seed(2026)
res <- fastcpd_wrapper(c(rnorm(100), rnorm(100, 4)))
res$changepoints
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.369