Consensus changepoints across several detectors

Runs a set of detectors on one series and reports the locations they agree on. Two detections count as the same changepoint when they fall within tolerance of each other — the same tolerance window cpt_metrics() matches on (van den Burg and Williams, 2020), so the package has one notion of "close enough" and not two. The grouping necessarily differs: cpt_metrics() matches two sets one-to-one, while consensus has to cluster \(K\) of them, so detections are swept in order and a new cluster opens as soon as one lies more than tolerance from the cluster's first member. That cap stops a chain of near-neighbours merging into one arbitrarily wide cluster.

Usage

cpt_consensus(
  x,
  methods = c("pelt", "binseg", "amoc"),
  tolerance = 5,
  min_votes = 2,
  change_in = "mean",
  index = NULL,
  seed = NULL,
  ...
)

# S3 method for class 'ggcpt_consensus'
autoplot(object, plot_type = c("series", "agreement"), ...)

# S3 method for class 'ggcpt_consensus'
print(x, ...)

Arguments

x: A ggcpt_consensus object (for print()).
methods: Character vector of method names.
tolerance: Matching window, in positions. Defaults to 5.
min_votes: Minimum number of methods that must find a location for it to enter the consensus. Defaults to 2; pass a fraction in \((0, 1)\) for a proportion of the methods that ran.
change_in: Passed to each detector.
index: Optional time index, carried onto the result.
seed: Optional seed for reproducibility.
...: Passed on to autoplot.ggcpt() for plot_type = "series".
object: A ggcpt_consensus object (for autoplot()).
plot_type: "series" (the consensus segmentation, votes shown by line width) or "agreement" (a method-by-location dot matrix showing exactly who voted for what).

Value

A ggcpt object (so it plots and tidies like any other result) whose changepoints tibble carries votes and methods (a comma-separated list of the methods that found each location), with the per-method detections kept in a consensus attribute and printed by autoplot(type = "agreement").

Consensus is not inference

Agreement among detectors is not a p-value, and a location found by six of seven methods is not thereby significant at any level: the methods are run on the same data and are strongly correlated, several of them share an engine, and none of the votes is independent. Read the vote count as a robustness display — "this feature does not depend on which detector I picked" — and use nsp_wrapper() or cpt_confint() when you need a guarantee.

References

van den Burg GJJ, Williams CKI (2020). “An evaluation of change point detection algorithms.” arXiv preprint arXiv:2003.06222. doi:10.48550/arXiv.2003.06222 .

Examples

set.seed(2026)
x <- c(rnorm(80), rnorm(80, 4))
cons <- cpt_consensus(x, methods = c("pelt", "binseg", "amoc"))
tidy(cons)
#> # A tibble: 1 × 5
#>      cp cp_value votes methods            spread
#>   <int>    <dbl> <int> <chr>               <int>
#> 1    80    0.785     3 amoc, binseg, pelt      0
ggplot2::autoplot(cons)