Create a ggcpt object

Usage

new_ggcpt(
  changepoints = tibble::tibble(cp = integer(), cp_value = numeric()),
  segments = tibble::tibble(seg_id = integer(), start = integer(), end = integer(), n =
    integer(), param_estimate = numeric()),
  data = tibble::tibble(index = integer(), value = numeric()),
  method = NA_character_,
  change_in = NA_character_,
  penalty = list(type = NA_character_, value = NA_real_),
  fit = NULL,
  call = NULL,
  cp_convention = "left",
  runtime = NA_real_
)

Arguments

changepoints: A tibble with columns cp and cp_value.
segments: A tibble with segment information: seg_id, start, end, n, param_estimate.
data: A tibble with index and value.
method: Character. The detection method used. A length-one string; defaults to NA_character_. (A zero-length value would make glance() return zero rows instead of its documented single row, because every other column would be recycled against it.)
change_in: Character. What was detected (e.g. "mean", "var", "meanvar"). A length-one string; defaults to NA_character_.
penalty: A list with type and value.
fit: The raw upstream object. Every wrapper stores one except "ecp": ecp::e.agglo() returns a cluster-progression matrix that is quadratic in the series length, so keeping it by default would make the result object explode on a long series. Call ecp::e.divisive() or ecp::e.agglo() directly if you need it. A few of the engines that are kept are still large relative to the data — measured on a 2000-point series, strucchange costs about 135 MB (a triangular $O(n^2)$ RSS matrix), bfast about 53 MB and bocpd about 31 MB, while every other engine stays under 4 MB. That is the engine's own object, not overhead this package adds, and it matters mainly when many results are held at once: cpt_batch(keep_fit = FALSE) drops them, or assign res$fit <- NULL yourself.
call: The matched call.
cp_convention: Character. The convention for reporting changepoint locations: "left" (last index of left segment, used by changepoint) or "right" (first index of right segment, used by ecp). Defaults to "left".
runtime: Numeric. Elapsed detection time in seconds, if measured. Defaults to NA.

Value

An object of class ggcpt.

Optional slots

Beyond the components in the signature, a ggcpt may carry any of these, each present only when something supplied it and each safe to test for with is.null():

data_wide: index plus one column per coordinate, for a multivariate result.
index, index_label: a time index (one value per observation) and its axis label; see the index argument of cpt_detect().
regions: a tibble of significance regions (start, end, ...) for the interval-valued methods — see nsp_wrapper() and geom_cpt_region().
diagnostics: a named list of engine internals rendered by ggcpt_statistic(), ggcpt_solution_path() and ggcpt_scale_space().
registered: TRUE when the result came from a user-registered detector rather than a wired engine.

as_ggcpt() is the validating way to build one of these from the outside; this constructor does not check its arguments.

Examples

set.seed(2026)
new_ggcpt(
  changepoints = tibble::tibble(cp = 50L, cp_value = 0.1),
  data = tibble::tibble(index = 1:100,
                        value = c(rnorm(50), rnorm(50, 4))),
  method = "manual", change_in = "mean")
#> ggcpt (changepoint detection result)
#>   Method:         manual
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         NA 
#>   Series length:   100 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1    50      0.1