fastcpd_wrapper: fastcpd wrapper: fast changepoint detection via sequential...

In ggchangepoint: Combines Changepoint Analysis with 'ggplot2'

fastcpd wrapper: fast changepoint detection via sequential gradient descent

Description

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. Every family the engine documents is reachable except its user-supplied custom cost: Gaussian mean, variance and both; the count and binary families ("poisson", "binomial") and waiting times ("exponential"); linear and penalised regression ("lm", "lasso") and generalised linear models ("poisson", "binomial" with covariates); and the time-series models "ar", "arma", "arima", "garch" and, for a multivariate series, "var".

Usage

fastcpd_wrapper(
  x,
  family = c("mean", "variance", "meanvariance", "ar", "arma", "arima", "garch", "var",
    "lm", "lasso", "poisson", "binomial", "exponential"),
  order = NULL,
  covariates = NULL,
  ...
)

Arguments

x	A numeric vector, or (for family "mean", "variance", "meanvariance" and "var") a matrix with one row per time point for multivariate detection.
family	Model family. "mean" (the default), "variance", "meanvariance"; "poisson", "binomial", "exponential" (a change in the rate or probability of a count, binary or waiting-time series, or in a regression on covariates); "lm", "lasso" (regression, which needs covariates); "ar", "arma", "arima", "garch", "var".
order	Model order for "ar" and "var" (a single integer), "arma" and "garch" (length 2) or "arima" (length 3). Defaults to 1 for AR and VAR, c(1, 1) for ARMA and GARCH and c(1, 0, 0) for ARIMA.
covariates	Optional numeric matrix of regressors, one row per observation, for "lm", "lasso", "poisson" and "binomial". Include a column of ones for an intercept. Without it the count and binary families fit an intercept only: a change in the rate or probability. cpt_detect() builds it from a formula.
...	Additional arguments passed to the corresponding fastcpd::fastcpd.*() function (e.g. beta, trim). beta is fastcpd's penalty: a number, or one of its own names ("MBIC", the default, "BIC", "MDL"). It is recorded on the result, so print() and glance() report the penalty the fit actually used. cpt_detect() forwards a numeric penalty here and translates the three names it shares with fastcpd; see the penalty-semantics section of cpt_penalty().

Value

A ggcpt object. The engine's per-segment parameter estimates (thetas) are kept as $coefficients, one row per segment and parameter, for the families where they are coefficients (the regressions and the intercept-only count, binary and waiting-time models).

References

\insertRef

li2024fastcpdggchangepoint

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(), 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 change in a Poisson rate
counts <- fastcpd_wrapper(c(rpois(100, 3), rpois(100, 9)),
                          family = "poisson")
counts$changepoints

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