cpt_methods: Introspect available changepoint detection methods

In ggchangepoint: Combines Changepoint Analysis with 'ggplot2'

Introspect available changepoint detection methods

Description

Returns a tibble describing every method the package knows about (those that are wired, those a user has registered with cpt_register_method(), and those that are planned), along with their capabilities and installation status. Useful for discovering what can be run, what needs to be installed, and which methods expose the extras the diagnostics need (confidence intervals, a fitted signal, a posterior, a detector statistic, a solution path, a bandwidth to sweep).

Usage

cpt_methods(capabilities = TRUE)

Arguments

capabilities

Include the capability flag columns? Defaults to TRUE. Set FALSE for the compact 0.4.0-shaped table.

Value

A tibble with columns:

method

Method name as passed to cpt_detect().

change_in

What types of change the method can detect.

engine

The upstream R package that implements the method.

status

"available" (wired in this release), "registered" (supplied by the user this session; see cpt_register_method()), or "planned" (future).

installed

TRUE if the engine package is installed, FALSE if it is a Suggests engine that is missing, NA for planned and registered methods.

target_release

What a planned method is waiting on: a release, or "when on CRAN" when the engine package itself is not available from CRAN. NA for methods that are already wired. Asking cpt_detect() for a planned method reports this rather than claiming the name does not exist.

multivariate, univariate, online, ci, fitted, posterior, statistic, path, scale_space

Capability flags (omitted when capabilities = FALSE). ci means the engine supplies changepoint-location confidence intervals; fitted a length-n fitted signal; posterior a per-location posterior probability; statistic and path the internals rendered by ggcpt_statistic() and ggcpt_solution_path(), which error with the list of supporting engines when a result does not carry them.

scale_space is not one of those, despite sitting beside them. Nothing stores a scale space on a result: cpt_scale_space() computes one on demand by sweeping a multiscale detector's bandwidth over the series, so it works on any series and any result, a pelt fit included. What this column marks is the two engines that sweep can be run with, i.e. the domain of that function's own method argument: subset(cpt_methods(), scale_space)$method.

online means the algorithm is sequential (it consumes observations one at a time), and this table reports it because it governs how the method behaves in batch: an online detector's threshold is a rate per observation, so run over a whole series through cpt_detect() it reports roughly n / \mathrm{arl0} changepoints by construction. It does not mean the method can be passed to cpt_monitor(), which takes its own five: "edetector", "cpm", "ocd", "cusum" and "ewma". The two sets overlap without coinciding: bocpd is an online algorithm this table marks but the monitor does not offer, and edetector, cusum and ewma are native to this package rather than wrapped engines, so they have no row here at all.

families, choices, formula, min_segment, na_handling, cp_convention_upstream

The modelling vocabulary (omitted when capabilities = FALSE): the distribution families cpt_detect(family = ) accepts (see cpt_families(); NA for a distribution-free method); the engine's modelling-choice arguments and their legal values, which cpt_detect() validates; whether it takes a formula with covariates; the engine argument min_segment is translated into; what the engine does with a missing value, measured rather than read from its documentation ("native", "compacts", "silent_loss" or "reject"; see cpt_detect(na_action = )); and the engine's own changepoint convention, which the wrapper translates to "left" ("right": it reports the first observation after the change; "continuous": a location it estimates on a continuous scale; "design": rows of a lagged design).

scale_invariant, sequential, max_cp, tier, noise_model_arg, rate_arg, cost, max_n

Measured and recorded properties (omitted when capabilities = FALSE): whether the answer is the same at x, 10 * x and 0.1 * x; whether it depends on the direction of time; how many changepoints the method can return where that is fixed; "general" for the general-purpose workhorses; the argument that selects the noise model and the one that sets the false-alarm rate; the runtime class at n = 10{,}000 ("fast" under a second, "moderate" under ten, "slow"); and the longest series it finished within the measurement's time cap (0 when it did not finish 1,000 observations). The measurements behind them are the cpt_runtimes and cpt_invariances data sets.

See Also

cpt_install_engines() to install a whole family of the engines this table reports on; cpt_detect() to run one; cpt_register_method() to add your own.

Examples

cpt_methods()
# which methods can draw a confidence interval?
subset(cpt_methods(), ci)$method

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