ggchangepoint: A Unified Tidy Interface for Changepoint Analysis in R

Youzhi Yu
University of Chicago

Abstract

ggchangepoint is an R package that provides a unified, tidy interface to changepoint detection across the methodological spectrum. It introduces a single S3 result class, ggcpt, with broom-style methods (tidy(), glance(), augment()) (Robinson 2017), a central dispatcher cpt_detect() covering 31 detection methods across five algorithmic families, and native ggplot2 (Wickham 2016) visualisation through autoplot() and a set of composable geoms. Where a method quantifies its uncertainty — the simultaneous confidence intervals of SMUCE (Frick et al. 2014), the break-date intervals of Bai–Perron (Bai and Perron 1998), the posterior probabilities of Bayesian detectors (Barry and Hartigan 1993; Adams and MacKay 2007) — the result object carries that uncertainty and the plotting layer can draw it. The package further supplies a penalty-path diagnostic (CROPS), batch detection over panels of series, bootstrap stability diagnostics, accuracy metrics aligned with modern benchmarking conventions (van den Burg and Williams 2020), ground-truth simulation, and per-method citations. This article describes the statistical background, the design of the package, and each method family in turn, with worked examples throughout.

Introduction

Changepoint analysis — locating the instants at which the stochastic behaviour of an ordered sequence changes — is one of the oldest problems in statistics, dating back at least to the quality-control charts of Page (1954), and one of its most active: modern surveys catalogue dozens of families of methods (Truong et al. 2020; Aminikhanghahi and Cook 2017). Its applications span virtually every domain that produces sequential data, including genomics (Picard et al. 2005), finance (Athey et al. 2022), climate science (Haslett and Raftery 1989), and signal processing (Lavielle 2005).

The R ecosystem mirrors this breadth. Penalised optimal partitioning lives in changepoint (Killick and Eckley 2014) and fpop (Maidstone et al. 2017); wild binary segmentation in wbs and breakfast (Fryzlewicz 2014, 2020); multiscale inference in stepR (Frick et al. 2014); Bayesian analysis in bcp (Erdman and Emerson 2007) and ocp (Adams and MacKay 2007); structural breaks in strucchange (Zeileis et al. 2002); and so on. Each of these packages is excellent at what it does, and each returns a different object, follows a different indexing convention, and draws (or does not draw) its own plots.

An analyst who wants to compare a PELT segmentation with a Bayesian posterior and a multiscale confidence set — a routine task in applied work — must therefore learn several APIs, reconcile several conventions, and write custom plotting code for each. ggchangepoint removes that friction. Its design goals are:

1. One vocabulary. A single front door, cpt_detect(x, method, change_in, penalty, ...), whose arguments mean the same thing for every engine.
2. One result type. Every detector returns a ggcpt object with a stable tidy contract, whatever the upstream engine returned.
3. One rendering path. Every result — point estimates, confidence intervals, fitted signals, posteriors, penalty paths — draws with autoplot() and extends with ordinary ggplot2 layers.
4. Wrap, don’t reinvent. All detection is delegated to the peer-reviewed upstream engines; optional engines live in Suggests and are loaded only when requested.

The changepoint problem

Let $y_{1:n} = (y_1, \dots, y_n)$ be an ordered sequence. A segmentation with $m$ changepoints is an ordered set $\tau_{1:m}$ with $0 = \tau_0 < \tau_1 < \dots < \tau_m < \tau_{m+1} = n$, partitioning the data into $m + 1$ segments $y_{(\tau_{i-1}+1):\tau_i}$. Throughout the package a changepoint $\tau$ is reported as the last index of the left segment (the convention of the changepoint package); results from engines using the opposite convention are normalised on the way in, and the convention is recorded on every result object.

Penalised cost minimisation

The classical formulation chooses the segmentation minimising a penalised cost, $$\min_{m,\ \tau_{1:m}} \; \sum_{i=1}^{m+1} \mathcal{C}\!\left(y_{(\tau_{i-1}+1):\tau_i}\right) \;+\; \beta m,$$ where $\mathcal{C}$ is a segment cost (for a change in mean under Gaussian noise, the residual sum of squares; more generally twice the negative maximised log-likelihood) and $\beta > 0$ penalises each additional changepoint. Common choices of $\beta$ are AIC, BIC/SIC (Yao 1988), and modified BIC variants. Solving this optimisation exactly by dynamic programming costs $O(n^2)$; PELT (Killick et al. 2012) prunes candidate changepoints to achieve linear expected cost while remaining exact, and FPOP (Maidstone et al. 2017) achieves comparable speed by functional pruning.

Search-based and multiscale methods

A complementary family locates changepoints by scanning test statistics. Binary segmentation (Scott and Knott 1974; Vostrikova 1981) recursively splits the series at the maximal CUSUM statistic; wild binary segmentation (Fryzlewicz 2014) and its successor WBS2 (Fryzlewicz 2020) draw random subintervals so that short segments are not masked; narrowest-over-threshold (NOT) (Baranowski et al. 2019) favours the narrowest interval on which the contrast exceeds a threshold, which generalises cleanly to changes in slope; MOSUM (Eichinger and Kirch 2018) scans a moving-sum statistic at a fixed bandwidth; Isolate–Detect (Anastasiou and Fryzlewicz 2022) isolates each changepoint in an expanding interval; and TGUH (Fryzlewicz 2022) performs a tail-greedy bottom-up merge. SMUCE (Frick et al. 2014) occupies a special place: it estimates the step function with the fewest jumps that passes a simultaneous multiscale test at level $\alpha$, and in doing so delivers confidence intervals for every changepoint location — uncertainty statements most competitors cannot make. HSMUCE (Pein et al. 2017) extends this to heterogeneous noise.

Beyond the mean

Changes need not be in the mean: the package’s change_in argument spans variance, mean-and-variance, slope, and full distributional change. Nonparametric engines (energy statistics (Matteson and James 2014), nonparametric cost functions (Haynes et al. 2017), kernel running statistics (Arlot et al. 2019; Cabrieto et al. 2018), joint characteristic functions (McGonigle and Cho 2025), self-normalisation (Zhao et al. 2022)) detect distributional change without likelihood assumptions; Bayesian engines (Barry and Hartigan 1993; Adams and MacKay 2007; Zhao et al. 2019) return posteriors instead of point sets; high-dimensional engines (Wang and Samworth 2018; Chen et al. 2022; Grundy et al. 2020) aggregate evidence across coordinates; and regression engines (Bai and Perron 1998; Muggeo 2003) date breaks in model coefficients. The tour below visits each family with runnable code.

Design of the package

The ggcpt result contract

Every detector returns an object of class ggcpt containing:

• changepoints: a tibble with one row per changepoint. Columns cp (location, “left” convention) and cp_value (the data value at cp) are always present; engines add ci_lower/ci_upper (SMUCE, HSMUCE, strucchange, segmented), posterior_prob (bcp, BEAST), detection_time (CPM), strength (inspect), declared_at (ocd), or mapping (geomcp) when they have more to say.
• segments: a tibble of the induced segments (seg_id, start, end, n, param_estimate).
• data: the analysed series as a tibble (index, value), plus a fitted column when the engine estimates a signal (SMUCE, DeCAFS, CPOP, segmented, bcp, BEAST).
• method, change_in, penalty (a list(type, value) descriptor), cp_convention (always "left"), runtime (seconds, measured by cpt_detect()), and fit (the untouched upstream object, for experts).

Multivariate results additionally carry a data_wide tibble with one column per coordinate, which autoplot() renders as faceted small-multiples.

Tidy methods and the plotting layer

The class implements the full complement of generics R users expect:

set.seed(2022)
x <- c(rnorm(100, 0, 1), rnorm(100, 10, 1))
res <- cpt_detect(x, method = "pelt", change_in = "mean")
res
#> ggcpt (changepoint detection result)
#>   Method:          pelt 
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         MBIC = NA 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
tidy(res)
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
glance(res)
#> # A tibble: 1 × 9
#>       n n_changepoints method change_in penalty_type penalty_value cp_convention
#>   <int>          <int> <chr>  <chr>     <chr>                <dbl> <chr>        
#> 1   200              1 pelt   mean      MBIC                    NA left         
#> # ℹ 2 more variables: total_cost <dbl>, runtime <dbl>
head(augment(res))
#> # A tibble: 6 × 6
#>   index  value seg_id .fitted .resid is_changepoint
#>   <int>  <dbl>  <int>   <dbl>  <dbl> <lgl>         
#> 1     1  0.900      1   0.139  0.761 FALSE         
#> 2     2 -1.17       1   0.139 -1.31  FALSE         
#> 3     3 -0.897      1   0.139 -1.04  FALSE         
#> 4     4 -1.44       1   0.139 -1.58  FALSE         
#> 5     5 -0.331      1   0.139 -0.470 FALSE         
#> 6     6 -2.90       1   0.139 -3.04  FALSE

autoplot() draws the series, the changepoint rules, and — on request — the fitted segment means (show_segments), the engine’s fitted signal (show_fit), and changepoint-location confidence intervals (show_ci):

autoplot(res, show_segments = TRUE)

Composable layers (geom_changepoint(), geom_cpt_segment(), geom_cpt_ci(), stat_changepoint()), a theme (theme_ggcpt()), and segment shading (annotate_segments()) let the same results be built into bespoke graphics; summary(), as_tibble(), as.data.frame(), format(), and plot() complete the S3 surface.

Design principles

The package follows seven principles, unchanged since 0.2.0:

• P1 — Wrap, don’t reinvent: bind to peer-reviewed CRAN engines.
• P2 — Tidy in, tidy out: stable column names across all methods.
• P3 — ggplot2 all the way down: every result renders and extends.
• P4 — One vocabulary: x, method, change_in, penalty, ....
• P5 — Progressive disclosure: beginners call cpt_detect() + autoplot(); experts reach the upstream fit via $fit.
• P6 — No surprises: the 0.1.0 functions still work unchanged.
• P7 — Document everything you ship: every export is introduced in the README and a vignette.

Release 0.4.0 adds an eighth: P8 — carry the uncertainty. Where the method quantifies uncertainty, the ggcpt object records it and autoplot() can draw it.

The unified dispatcher

cpt_detect() dispatches by method name; cpt_methods() reports every method the package knows, its engine, what it can detect, and whether the engine is installed:

cpt_methods()
#> # A tibble: 35 × 6
#>    method   change_in                   engine   status target_release installed
#>    <chr>    <chr>                       <chr>    <chr>  <chr>          <lgl>    
#>  1 pelt     mean, var, meanvar          changep… avail… NA             TRUE     
#>  2 binseg   mean, var, meanvar          changep… avail… NA             TRUE     
#>  3 segneigh mean, var, meanvar          changep… avail… NA             TRUE     
#>  4 amoc     mean, var, meanvar          changep… avail… NA             TRUE     
#>  5 np       distribution                changep… avail… NA             TRUE     
#>  6 ecp      distribution (multivariate) ecp      avail… NA             TRUE     
#>  7 fpop     mean                        fpop     avail… NA             TRUE     
#>  8 wbs      mean                        wbs      avail… NA             TRUE     
#>  9 wbs2     mean                        breakfa… avail… NA             TRUE     
#> 10 not      mean, var, slope            not      avail… NA             TRUE     
#> # ℹ 25 more rows

Requests are validated against this capability matrix: asking a mean-only engine for a variance change is an error with the legal alternatives named — never a silent substitution. Univariate methods likewise refuse multi-column input rather than flattening it.

Penalty semantics differ across engines, and cpt_penalty() documents and constructs the standard values:

cpt_penalty("BIC", n = 200)
#> [1] 5.298317
cpt_penalty("MBIC", n = 200)
#> [1] 10.59663
cpt_penalty("Hannan-Quinn", n = 200)
#> [1] 3.334779

Character penalties ("MBIC", "BIC", …) pass through to the changepoint-family engines natively and are resolved to numeric values for functional-pruning engines (fpop, cpop, decafs); search-based engines (WBS, NOT, MOSUM, …) use their own model-selection criteria and ignore the argument.

A tour of the method families

Throughout, we use simulated series with known truth so results can be checked by eye. The simulator and canonical test signals (signal_blocks() — the Donoho–Johnstone blocks signal (Donoho and Johnstone 1994) — signal_fms(), signal_teeth(), signal_stairs(), signal_mix()) are described in the comparison vignette.

set.seed(2026)
x_mean  <- c(rnorm(100), rnorm(100, 4))                # mean shift at 100
x_multi <- c(rnorm(100), rnorm(100, 3), rnorm(100, -1)) # shifts at 100, 200
x_slope <- cumsum(c(rep(0.4, 100), rep(-0.3, 100))) + rnorm(200) # kink at 100

Penalised and optimal partitioning

PELT (Killick et al. 2012), binary segmentation (Scott and Knott 1974), segment neighbourhoods, and AMOC come from the changepoint package; FPOP (Maidstone et al. 2017) from fpop:

tidy(cpt_detect(x_multi, method = "pelt"))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   200    1.90
tidy(cpt_detect(x_multi, method = "binseg"))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   201    0.337

tidy(cpt_detect(x_multi, method = "fpop"))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   200    1.90

The Achilles heel of penalised methods is the choice of $\beta$. Rather than committing to one value, cpt_crops() computes every optimal segmentation as $\beta$ ranges over an interval — the CROPS algorithm of Haynes, Eckley and Fearnhead (2017), as implemented by changepoint — and turns penalty selection into a diagnostic:

path <- cpt_crops(x_multi)
path
#> ggcpt_path (CROPS penalty path)
#>   Change in:       mean 
#>   Penalty range:  [5.704, 57.04]
#>   Series length:   300 
#>   Distinct segmentations: 5 
#> 
#> # A tibble: 5 × 3
#>   penalty n_cpts  cost
#>     <dbl>  <int> <dbl>
#> 1    6.69      2  327.
#> 2    6.27      5  307.
#> 3    6.19      8  288.
#> 4    5.76      9  282.
#> 5    5.70     10  276.
autoplot(path)

The elbow at the true number of changepoints (here 2) is the standard reading. autoplot(path, type = "segmentations") shows the candidate models themselves, and autoplot(path, type = "path") the map from penalty to model size:

autoplot(path, type = "segmentations")

The modern fastcpd engine (Li and Zhang 2024) brings the same penalised formulation to a wide family of models — mean, variance, mean-and-variance, and AR/ARMA/GARCH model changes — with sequential-gradient-descent speed:

tidy(fastcpd_wrapper(x_multi, family = "mean"))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   200    1.90

Multiscale and search methods

The randomised and multiscale searchers are one wrapper call each:

tidy(wbs_wrapper(x_multi, seed = 1))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   200    1.90

tidy(not_wrapper(x_multi, seed = 1))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   200    1.90

tidy(mosum_wrapper(x_multi))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   200    1.90

tidy(idetect_wrapper(x_multi, seed = 1))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   200    1.90
tidy(wbs2_wrapper(x_multi))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   200    1.90
tidy(tguh_wrapper(x_multi))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100   -0.100
#> 2   199    4.53

SMUCE (Frick et al. 2014) is the family’s inferential flagship: its level $\alpha$ bounds the probability of overestimating the number of changepoints, and every location comes with a confidence interval, stored in ci_lower/ci_upper and drawn by show_ci = TRUE as whiskers beneath the series (the step fit is drawn by show_fit = TRUE):

res_smuce <- smuce_wrapper(x_multi)
tidy(res_smuce)
#> # A tibble: 2 × 4
#>      cp cp_value ci_lower ci_upper
#>   <int>    <dbl>    <int>    <int>
#> 1   100   -0.100       98      103
#> 2   200    1.90       199      202
autoplot(res_smuce, show_ci = TRUE, show_fit = TRUE)

For heterogeneous noise, smuce_wrapper(x, family = "hsmuce") (or cpt_detect(x, method = "hsmuce")) runs HSMUCE (Pein et al. 2017).

Changes in slope

A kink in the trend is not a jump in the level, and running a mean-change detector on a trending series over-detects notoriously. CPOP (Fearnhead et al. 2019; Fearnhead and Grose 2024) solves the change-in-slope problem exactly under an $L_0$ penalty, returning a continuous piecewise-linear fit:

res_cpop <- cpop_wrapper(x_slope)
tidy(res_cpop)
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100     38.6
autoplot(res_cpop, show_fit = TRUE)

NOT with its linear contrast (Baranowski et al. 2019) offers a search-based alternative; the dispatcher routes cpt_detect(x, method = "not", change_in = "slope") to it automatically:

tidy(cpt_detect(x_slope, method = "not", change_in = "slope"))
#> # A tibble: 2 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1    85     36.8
#> 2   101     40.7

Bayesian detection

The Barry–Hartigan product partition model (Barry and Hartigan 1993), via the bcp package (Erdman and Emerson 2007), returns a posterior probability of a changepoint at every location along with posterior segment means. Locations clearing prob_threshold populate the changepoints tibble (with their probabilities), and ggcpt_posterior() draws the classic two-panel display:

res_bcp <- bcp_wrapper(x_mean, seed = 2026)
tidy(res_bcp)
#> # A tibble: 1 × 3
#>      cp cp_value posterior_prob
#>   <int>    <dbl>          <dbl>
#> 1   100    0.369              1
ggcpt_posterior(res_bcp)

Bayesian online changepoint detection (Adams and MacKay 2007) instead tracks the posterior over the current run length — the time since the last change — updating recursively as data arrive. Its signature graphic is the run-length heatmap, where a change appears as the posterior mass collapsing to zero:

res_bocpd <- bocpd_wrapper(x_mean)
tidy(res_bocpd)
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.369
ggcpt_runlength(res_bocpd)

A third Bayesian engine, BEAST (Zhao et al. 2019) via Rbeast, averages over models rather than conditioning on one, and is wired as cpt_detect(x, method = "beast") (or beast_wrapper()); it too reports posterior_prob and renders with ggcpt_posterior().

Nonparametric and sequential detection

When no parametric form is trustworthy, the nonparametric cost approach of changepoint.np (Haynes et al. 2017) and the energy-statistics E-Divisive of ecp (Matteson and James 2014; James and Matteson 2014) detect general distributional change:

set.seed(2022)
tidy(cpt_detect(x_mean, method = "np"))
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.369
tidy(cpt_detect(x_mean, method = "ecp", seed = 1))
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.369

The cpm package (Ross 2015) runs sequential two-sample tests (Mann–Whitney, Mood, Lepage, Kolmogorov–Smirnov, …) as an online monitor; its results distinguish where a change happened (cp) from when it was detected (detection_time), the inherent lag of sequential monitoring:

tidy(cpm_wrapper(x_mean, cpm_type = "Mann-Whitney"))
#> # A tibble: 3 × 3
#>      cp cp_value detection_time
#>   <int>    <dbl>          <int>
#> 1    23   -1.39              30
#> 2    50    0.426             67
#> 3   100    0.369            104

Three further nonparametric engines are wired and worth knowing: kernel change-point analysis on running statistics (kcp_wrapper(), engine kcpRS), which detects changes in running means, variances, autocorrelations, or correlations (Arlot et al. 2019; Cabrieto et al. 2018); NP-MOJO (npmojo_wrapper(), engine CptNonPar), which detects changes in the marginal or lagged joint distribution while remaining valid under serial dependence (McGonigle and Cho 2025); and self-normalised segmentation (sn_wrapper(), engine SNSeg), which avoids long-run variance estimation altogether and tests changes in means, variances, autocorrelations, or bivariate correlations (Zhao et al. 2022).

Robustness to drift, autocorrelation, and model ambiguity

The most common failure of naive mean-change detection in practice is not a subtle statistical issue: it is running a Gaussian-mean detector on data whose baseline drifts or whose noise is autocorrelated, and reporting a changepoint wherever the model is wrong. DeCAFS (Romano et al. 2022) models exactly this regime — abrupt changes superimposed on random-walk drift and AR(1) noise — and separates the two:

res_decafs <- decafs_wrapper(x_mean)
tidy(res_decafs)
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.369
autoplot(res_decafs, show_fit = TRUE)

EnvCpt (Beaulieu and Killick 2018) attacks the same confusion by model selection: it fits up to twelve competing descriptions — constant mean or trend, with or without changepoints, with white or AR(1)/AR(2) noise — and reports changepoints only if a changepoint model wins on information criteria:

res_env <- envcpt_wrapper(x_mean, models = c("mean", "meancpt", "trendcpt"))
glance(res_env)
#> # A tibble: 1 × 9
#>       n n_changepoints method change_in penalty_type penalty_value cp_convention
#>   <int>          <int> <chr>  <chr>     <chr>                <dbl> <chr>        
#> 1   200              1 envcpt mean      AIC: meancpt          568. left         
#> # ℹ 2 more variables: total_cost <dbl>, runtime <dbl>

The winning model’s name is recorded in the penalty descriptor (penalty_type above), so “no changepoints, it’s just autocorrelation” is a first-class answer.

Multivariate and high-dimensional detection

Multivariate methods accept a matrix (rows are time points) directly. The energy-statistics E-Divisive of ecp was built for this (Matteson and James 2014); for high-dimensional data whose change is confined to a sparse subset of coordinates, inspect (Wang and Samworth 2018) finds an optimal sparse projection of the CUSUM matrix and reports the projected evidence (strength). Multivariate results render as faceted small-multiples with shared changepoint rules:

set.seed(2026)
X <- cbind(a = c(rnorm(80), rnorm(80, 3)),
           b = c(rnorm(80), rnorm(80, -2)),
           c = rnorm(160))
res_hd <- inspect_wrapper(X)
tidy(res_hd)
#> # A tibble: 1 × 3
#>      cp cp_value strength
#>   <int>    <dbl>    <dbl>
#> 1    80    0.785     21.9
autoplot(res_hd)

Two further engines complete the family: geomcp_wrapper() (engine changepoint.geo) maps each observation to its distance from and angle to a reference point and segments the two mapped series, catching magnitude and orientation changes respectively (Grundy et al. 2020); and ocd_wrapper() (engine ocd) monitors a high-dimensional stream online with worst-case detection-delay guarantees (Chen et al. 2022), reporting both the declaration time and (by construction of the wrapper) handling baseline estimation and post-declaration restarts.

Structural breaks in regression

Econometric practice dates breaks in regression coefficients. The Bai–Perron estimator (Bai and Perron 1998, 2003), via strucchange (Zeileis et al. 2002), returns break dates with confidence intervals; called on a bare series it dates mean shifts, and called with a formula it dates breaks in arbitrary regressions:

res_bp <- strucchange_wrapper(x_mean)
tidy(res_bp)
#> # A tibble: 1 × 4
#>      cp cp_value ci_lower ci_upper
#>   <int>    <dbl>    <int>    <int>
#> 1   100    0.369       99      101
autoplot(res_bp, show_ci = TRUE)

Where the regression function is continuous — a kink rather than a jump — segmented (Muggeo 2003, 2008) estimates broken-line relationships with standard errors for the breakpoints:

res_seg <- segmented_wrapper(x_slope, npsi = 1, seed = 1)
tidy(res_seg)
#> # A tibble: 1 × 4
#>      cp cp_value ci_lower ci_upper
#>   <int>    <dbl>    <int>    <int>
#> 1   100     38.6       98      101
autoplot(res_seg, show_fit = TRUE, show_ci = TRUE)

Beyond detection

Batch detection over many series

Applied work rarely stops at one series. cpt_batch() runs a detector over every column of a matrix or data frame (or every element of a list) and returns a tidy tibble of results, with future-based parallelism available via future::plan():

set.seed(2026)
panel <- cbind(shifted = x_mean, quiet = rnorm(200))
batch <- cpt_batch(panel, method = "pelt")
batch
#> ggcpt_batch (2 series, method: pelt)
#> 
#> # A tibble: 2 × 2
#>   series  n_changepoints
#>   <chr>            <int>
#> 1 shifted              1
#> 2 quiet                0
tidy(batch)
#> # A tibble: 1 × 3
#>   series     cp cp_value
#>   <chr>   <int>    <dbl>
#> 1 shifted   100    0.369
autoplot(batch)

Stability diagnostics

Most engines report a point set with no measure of its fragility. cpt_stability() resamples residuals within the fitted segments, re-runs the detector on each replicate, and reports how often each location is re-detected — a cheap, model-agnostic confidence signal available for every engine, including the many with no native intervals:

st <- cpt_stability(x_mean, method = "pelt", B = 50, seed = 1)
st
#> ggcpt_stability (50 bootstrap replicates, method: pelt)
#> 
#> Original changepoints and their re-detection frequency:
#> # A tibble: 1 × 2
#>      cp stability
#>   <int>     <dbl>
#> 1   100         1
autoplot(st)

Evaluation, interactivity, and citations

When ground truth is known, cpt_metrics() computes precision, recall and F1 under one-to-one matching, the covering metric, Hausdorff distance, and adjusted Rand index, following the conventions of the modern benchmarking literature (van den Burg and Williams 2020); ggcpt_eval() draws the agreement, and ggcpt_compare() juxtaposes methods. These are the subject of the companion vignette vignette("comparison", package = "ggchangepoint").

Any result renders as an interactive HTML widget with ggcpt_interactive(res) (engine plotly, in Suggests); the static autoplot() path is untouched.

Finally, because every method here is someone’s published work, cpt_cite() returns the reference(s) behind a result, so analyses can cite the right paper without leaving R:

cpt_cite("pelt")
#> [pelt] Killick, R., Fearnhead, P. and Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590-1598.

Discussion

ggchangepoint does not contribute a new detection algorithm; it contributes a surface. The value of a common contract compounds with the number of methods behind it: the same tidy() pipeline, the same plot, and the same evaluation code now span penalised, multiscale, nonparametric, Bayesian, high-dimensional, and regression-based detection — 31 methods in this release. Methods whose engines are currently absent from CRAN (graph-constrained gfpop (Hocking et al. 2020), robust segmentation (Fearnhead and Rigaill 2019), FOCuS, sparsified binary segmentation) are tracked as planned in cpt_methods() and will slot into the same wrapper pattern when their engines return.

Two practical notes. First, wrapped engines are run with sensible defaults, but each wrapper forwards ... to its engine, and the raw fit is always in $fit — the package is a front door, not a cage. Second, detection quality is the engines’; the package’s own additions (metrics, stability, penalty paths) are deliberately engine-agnostic, so conclusions drawn with them transfer.

Acknowledgements

This package stands on the shoulders of the authors of the wrapped engines and of the R (R Core Team 2024), ggplot2 (Wickham 2016), and broom (Robinson 2017) projects.

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