ggchangepoint

One interface. 50 changepoint methods. Every result tidy, every result plottable.

R has excellent changepoint packages, but each one takes its own input, returns its own result object, and has its own idea of a plot — so switching methods means rewriting your analysis. ggchangepoint puts them behind one interface: cpt_detect() runs any of 50 detection methods, every result comes back as the same tidy ggcpt object, and autoplot() draws it, with confidence intervals, significance regions, posteriors, detector statistics, penalty paths and accuracy metrics when you need them. When the detector you want is not one of the 50 — because it is not on CRAN, or lives in Python, or is your own — cpt_register_method() brings it into the same grammar.

Installation

Install the released version from CRAN:

install.packages("ggchangepoint")

Or the development version from GitHub:

# install.packages("devtools")
devtools::install_github("PursuitOfDataScience/ggchangepoint")

Quick start

library(ggchangepoint)
library(ggplot2)

Generate a series with a mean shift:

set.seed(2022)
x <- c(rnorm(100, 0, 1), rnorm(100, 10, 1))

Detect changepoints with the unified cpt_detect():

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 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467

The result is a ggcpt S3 object, so the broom verbs work on it: tidy() gives one row per changepoint, glance() a one-row model summary, and augment() the original series with segment labels, fitted values and residuals.

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>

Visualise with autoplot():

autoplot(res)

Why ggchangepoint

• Detect with one call — cpt_detect(x, method = "...") dispatches to 50 methods, from classic PELT to Bayesian online detection, functional data and dynamic networks.
• Tidy everywhere — every method returns the same ggcpt object, with tidy(), glance(), and augment(), and a time index that survives the round trip.
• Plot everything — autoplot() draws any result: changepoints, confidence intervals, significance regions, fitted signals, posteriors, multivariate facets, and (type =) the detector’s own statistic, solution path or scale space.
• Trust the answer — intervals from four provenances, honest tests, criteria that actually select K, influence and sensitivity diagnostics, consensus across methods, and a benchmark harness.
• Extend it — as_ggcpt() and cpt_register_method() bring any external detector into the same tidy, plottable grammar.

Family	Methods
Penalised / optimal partitioning	PELT · BinSeg · SegNeigh · AMOC · FPOP · binsegRcpp (fast) · CPOP (slope) · fastcpd (mean/var/AR/ARMA/GARCH)
Multiscale / search	WBS · WBS2 · TGUH · NOT · MOSUM · Isolate-Detect · SMUCE · HSMUCE (the last two with CIs) · WBSTS (nonstationary)
Inference	NSP — significance regions, each guaranteed to contain a change at a global level
Nonparametric / kernel	ED-PELT · E-Divisive / E-Agglo · kernel running stats · NP-MOJO · sequential CPM · self-normalisation · KWC depth ranks
Bayesian	bcp posteriors · online BOCPD · BEAST model averaging · mcp (formula-based, full posteriors)
High-dimensional	inspect · ocd · geomcp · ESAC · Pilliat · high-dimensional covariance · VAR(1) · high-dimensional regression
Functional & network	functional mean · functional covariance · KWC · fabisearch (NMF network structure) · changepoints networks
Regression, trend & season	Bai–Perron · segmented · EnvCpt · DeCAFS · BFAST (season + trend)
Classical single-change tests	Pettitt · Buishand · SNHT · Taylor’s analyzer

Run cpt_methods() for the live table with engines, installation status and capability flags. Only three engines are required — changepoint, changepoint.np and ecp; every other engine lives in Suggests and is loaded on demand, so a plain install stays light. cpt_install_engines("bayesian") installs a whole family at once.

Unified detection across engines

cpt_detect() dispatches to any supported method by name:

cpt_detect(x, method = "binseg", change_in = "mean")
#> ggcpt (changepoint detection result)
#>   Method:         binseg
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         MBIC 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
cpt_detect(x, method = "wbs", change_in = "mean")
#> ggcpt (changepoint detection result)
#>   Method:         wbs
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         sSIC 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
cpt_detect(x, method = "fpop", change_in = "mean")
#> ggcpt (changepoint detection result)
#>   Method:         fpop
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         Manual = 17.846 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467

Use cpt_methods() to see all available and planned methods with their engine packages and installation status:

cpt_methods()
#> # A tibble: 55 × 15
#>    method   change_in        engine status installed target_release multivariate
#>    <chr>    <chr>            <chr>  <chr>  <lgl>     <chr>          <lgl>       
#>  1 pelt     mean, var, mean… chang… avail… TRUE      <NA>           FALSE       
#>  2 binseg   mean, var, mean… chang… avail… TRUE      <NA>           FALSE       
#>  3 segneigh mean, var, mean… chang… avail… TRUE      <NA>           FALSE       
#>  4 amoc     mean, var, mean… chang… avail… TRUE      <NA>           FALSE       
#>  5 np       distribution     chang… avail… TRUE      <NA>           FALSE       
#>  6 ecp      distribution (m… ecp    avail… TRUE      <NA>           TRUE        
#>  7 fpop     mean             fpop   avail… TRUE      <NA>           FALSE       
#>  8 wbs      mean             wbs    avail… TRUE      <NA>           FALSE       
#>  9 wbs2     mean             break… avail… TRUE      <NA>           FALSE       
#> 10 not      mean, var, slope not    avail… TRUE      <NA>           FALSE       
#> # ℹ 45 more rows
#> # ℹ 8 more variables: univariate <lgl>, online <lgl>, ci <lgl>, fitted <lgl>,
#> #   posterior <lgl>, statistic <lgl>, path <lgl>, scale_space <lgl>

Dates, not indices

Real series have dates, and “changepoint at index 147” is a translation step the analyst should not have to do. Pass an index — or a ts, xts, zoo or tsibble — and detection still runs on positions (every engine assumes an equally spaced sequence) while everything reported comes back on your scale:

dates <- as.Date("2020-01-01") + seq_along(x) - 1
dated <- cpt_detect(x, method = "pelt", index = dates)
tidy(dated)
#> # A tibble: 1 × 3
#>      cp cp_index   cp_value
#>   <int> <date>        <dbl>
#> 1   100 2020-04-09    0.467
autoplot(dated)

as_cpt_series() is the same conversion on its own, for code that needs the values and the index before detection runs.

There is a data-frame interface too, taking a bare column name, a string, or a position:

df <- data.frame(day = dates, value = x)
cpt_detect(df, y = value, index = day, method = "pelt")
#> ggcpt (changepoint detection result)
#>   Method:         pelt
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         MBIC 
#>   Series length:   200 
#>   Index:           2020-01-01 to 2020-07-18 
#> 
#> Changepoints:
#> # A tibble: 1 × 3
#>      cp cp_index   cp_value
#>   <int> <date>        <dbl>
#> 1   100 2020-04-09    0.467

Detection with uncertainty: confidence intervals and posteriors

SMUCE (smuce_wrapper(), via stepR) delivers a confidence interval for every changepoint location; Bai–Perron (strucchange_wrapper()) and broken-line regression (segmented_wrapper()) do the same for regression breaks. The intervals live in ci_lower/ci_upper columns and render with autoplot(show_ci = TRUE) or the geom_cpt_ci() layer:

res_smuce <- smuce_wrapper(x)
tidy(res_smuce)
#> # A tibble: 2 × 4
#>      cp cp_value ci_lower ci_upper
#>   <int>    <dbl>    <int>    <int>
#> 1     7   -1.06         2       94
#> 2   100    0.467      100      100
autoplot(res_smuce, show_ci = TRUE, show_fit = TRUE)

Read the intervals, not just the locations: the genuine shift is pinned to a single index, while the spurious early changepoint carries an interval nearly a hundred observations wide — exactly the distinction a bare list of locations hides.

Bayesian engines return posterior probabilities instead: bcp_wrapper() (posterior probability of a change at every location), beast_wrapper() (Bayesian model averaging), and bocpd_wrapper() (online run-length posterior). Two dedicated displays accompany them — ggcpt_posterior() and ggcpt_runlength():

res_bcp <- bcp_wrapper(x, seed = 1)
tidy(res_bcp)
#> # A tibble: 1 × 3
#>      cp cp_value posterior_prob
#>   <int>    <dbl>          <dbl>
#> 1   100    0.467              1
ggcpt_posterior(res_bcp)

The penalty path: CROPS

Instead of guessing one penalty, cpt_crops() computes every optimal segmentation over a penalty range (Haynes, Eckley and Fearnhead, 2017) and plots the elbow diagnostic, the penalty path, or the candidate segmentations themselves:

path <- cpt_crops(c(rnorm(100), rnorm(100, 3), rnorm(100, -1)))
path
#> ggcpt_path (CROPS penalty path)
#>   Change in:       mean 
#>   Penalty range:  [5.704, 57.04]
#>   Series length:   300 
#>   Distinct segmentations: 2 
#> 
#> # A tibble: 2 × 3
#>   penalty n_cpts  cost
#>     <dbl>  <int> <dbl>
#> 1    7.68      2  274.
#> 2    5.70      4  259.
autoplot(path)                          # cost elbow

autoplot(path, type = "segmentations")  # see the actual candidate models

Inference: a region, not a point

Narrowest Significance Pursuit (Fryzlewicz 2024) inverts the usual framing: instead of estimating locations and then asking whether they are real, it returns intervals, each guaranteed to contain at least one changepoint, with the guarantee holding globally across all of them. That is a different visual object, and it gets its own layer:

res_nsp <- nsp_wrapper(x, M = 100, seed = 1)
cpt_regions(res_nsp)
#> # A tibble: 1 × 4
#>   start   end length value
#>   <int> <int>  <int> <dbl>
#> 1   100   101      2  4.66
autoplot(res_nsp)

The cp column of an NSP fit is the interval midpoint, is labelled as such in cp_source, and is never presented as an estimate — the region is the inferential object.

For every other engine, cpt_confint() answers “where could this changepoint be?” behind one contract, and says which of four routes it used:

cpt_confint(res, method = "bootstrap", B = 50, seed = 1)
#> # A tibble: 1 × 6
#>      cp ci_lower ci_upper level source    n_replicates
#>   <int>    <int>    <int> <dbl> <chr>            <int>
#> 1   100      100      100  0.95 bootstrap           50

cpt_test() attaches a test to each changepoint, using the engine’s own where it has one and an explicitly unadjusted two-sample test where it does not — with a selection_adjusted column so the two are never confused:

suppressWarnings(cpt_test(res))
#> # A tibble: 1 × 6
#>      cp estimate statistic   p_value method                   selection_adjusted
#>   <int>    <dbl>     <dbl>     <dbl> <chr>                    <lgl>             
#> 1   100     9.66      67.1 4.48e-138 Welch two-sample t (una… FALSE

Choosing the number of changepoints

cpt_crops() draws the penalty path; cpt_select() actually chooses, by BIC, the Zhang–Siegmund segment-length mBIC, AIC, an explicit knee rule on the CROPS curve, order-preserved cross-validation (the criterion with a consistency proof), or bootstrap stability:

x3 <- c(rnorm(120), rnorm(120, 4), rnorm(120, 1))
sel <- cpt_select(x3, criterion = "mbic", k_max = 6)
sel
#> ggcpt_selection (criterion: mbic, method: pelt)
#>   Candidates scored: K = 0 to 6
#>   Chosen K:          2
#>   Locations:         119, 240
#> 
#> # A tibble: 7 × 4
#>       k value    cost chosen
#>   <int> <dbl>   <dbl> <lgl> 
#> 1     0 530.  530.    FALSE 
#> 2     1 398.  382.    FALSE 
#> 3     2  53.1  21.0   TRUE  
#> 4     3  61.6  15.2   FALSE 
#> 5     4  71.3   9.97  FALSE 
#> 6     5  79.0   4.14  FALSE 
#> 7     6  89.5  -0.362 FALSE
autoplot(sel, plot_type = "ladder", max_facets = 5)

Seeing the statistic

Every detector computes something — a MOSUM statistic at each bandwidth, a set of random intervals with contrast values, a nested sequence of splits — and keeping only the argmax throws away the explanation. Three accessors give it back:

res_mosum <- cpt_detect(c(rnorm(200), rnorm(200, 3)), method = "mosum")
autoplot(res_mosum, type = "statistic")

ggcpt_scale_space(res_mosum, bandwidths = c(20, 40, 80))

cpt_statistic() returns the profile as a tibble and ggcpt_statistic() draws it on its own; cpt_solution_path() and ggcpt_solution_path() do the same for the order in which candidates entered a binary-segmentation or wild-binary-segmentation model.

Which observation is driving this?

cpt_stability() answers “would I find this again?”. cpt_influence() answers the sharper question — perturbing one observation at a time and re-fitting, the Wilms–Killick–Matteson diagnostic family rendered in ggplot2:

inf <- cpt_influence(res, engine = "recompute", subset = seq(1, 200, by = 5))
head(cpt_leverage(inf), 3)
#> # A tibble: 3 × 5
#>   index delta_n_cp max_shift param_shift leverage
#>   <int>      <int>     <dbl>       <dbl>    <dbl>
#> 1     6          0         0      0.0307     3.74
#> 2   141          0         0      0.0180     1.66
#> 3   181          0         0      0.0179     1.64
autoplot(inf)

cpt_sensitivity() is the tuning-parameter analogue, and the direct answer to “is this robust to the penalty?”:

autoplot(cpt_sensitivity(x3, method = "pelt", over = list(penalty = c(4, 20, 80))))

Supervised detection: learn the penalty from labels

An expert marks intervals as containing a change or not, accuracy is measured in label errors, and the penalty is learned rather than assumed (Hocking et al., 2013). The central object is a rectangle drawn over a time series — a ggplot2-native idea:

labs <- cpt_labels(c(80, 140), c(120, 190), c("one_change", "no_change"))
ggplot(data.frame(t = seq_along(x), y = x), aes(t, y)) +
  geom_cpt_label(aes(xmin = start, xmax = end, fill = change), data = labs) +
  geom_line() +
  scale_fill_cpt_label() +
  theme_ggcpt()

cpt_label_error(res, labs)
#> cpt_label_error (2 label(s))
#>   correct: 2   false positives: 0   false negatives: 0
#>   total label errors: 0
#> 
#> # A tibble: 2 × 7
#>   label_id series start   end change     n_changes status 
#>      <int> <chr>  <int> <int> <chr>          <int> <chr>  
#> 1        1 <NA>      80   120 one_change         1 correct
#> 2        2 <NA>     140   190 no_change          0 correct

cpt_label_error_curve() sweeps the penalty and reports false positives, false negatives and total label error at each value — the curve the learned penalty is chosen to minimise. cpt_learn_penalty() fits the max-margin interval regression over a set of labelled series, and the model plugs straight into cpt_detect():

series <- list(a = c(rnorm(60), rnorm(60, 4)), b = c(rnorm(80), rnorm(80, 2)))
labels <- list(a = as_cpt_labels(60, n = 120), b = as_cpt_labels(80, n = 160))
model <- cpt_learn_penalty(series, labels, penalties = c(1, 4, 16, 64, 256))
cpt_detect(series$a, method = "pelt", penalty = model)
#> ggcpt (changepoint detection result)
#>   Method:         pelt
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         Manual = 43.454 
#>   Series length:   120 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1    60     1.01

Which method should I use?

cpt_recommend() turns the capability matrix into advice, and is explicit about what each choice assumes:

cpt_recommend(noise = "autocorrelated")
#> Recommended methods for: univariate series, change in mean, autocorrelated noise
#> 
#> 1. decafs (DeCAFS)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 2. envcpt (EnvCpt)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 3. nsp (nsp)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 4. sn (SNSeg)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 5. wbsts (wbsts)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 
#> (27 further candidate(s); the full table is the return value.)
#> 
#> Cite the method you use with cpt_cite(). Cross-check the choice with
#> cpt_consensus() and cpt_sensitivity().

cpt_consensus() runs several detectors and reports the locations they agree on — a robustness display, not a significance test:

cons <- cpt_consensus(x3, methods = c("pelt", "binseg", "wbs", "amoc"))
tidy(cons)
#> # A tibble: 2 × 5
#>      cp cp_value votes methods                 spread
#>   <int>    <dbl> <int> <chr>                    <int>
#> 1   120     1.90     4 amoc, binseg, pelt, wbs      1
#> 2   240     6.01     3 binseg, pelt, wbs            0
autoplot(cons, plot_type = "agreement")

From a changepoint to a finding

“Changepoint at index 147” is not a finding. cpt_annotate_events() matches detections to known events and reports all three outcomes — and the unexplained changepoints are where the interesting analysis starts:

events <- data.frame(when = as.Date(c("2020-04-09", "2020-06-01")),
                     what = c("policy change", "supply shock"))
cpt_annotate_events(dated, events)
#> ggcpt_events (tolerance 5 position(s))
#>   Changepoints explained by an event: 1
#>   Changepoints with no event:         0
#>   Events with no changepoint:         1
#> 
#> Matched:
#> # A tibble: 1 × 6
#>      cp cp_index   event         event_value event_position distance
#>   <int> <date>     <chr>         <date>               <int>    <int>
#> 1   100 2020-04-09 policy change 2020-04-09             100        0
#> 
#> Events the detector did not find:
#> # A tibble: 1 × 3
#>   event        event_value event_position
#>   <chr>        <date>               <int>
#> 1 supply shock 2020-06-01             153

cpt_report() assembles the whole analysis — method, citation, penalty, locations with intervals, segments, the call and sessionInfo() — into one reproducible artifact, and cpt_gt() renders a publication-ready table.

Benchmarking

cpt_benchmark() runs a method-by-dataset grid, scores every cell, and records an engine failure instead of losing the run:

bm <- cpt_benchmark(cpt_datasets(n = 300, seed = 1),
                    methods = c("pelt", "binseg", "wbs", "amoc"),
                    progress = FALSE)
bm
#> ggcpt_benchmark (9 dataset(s) x 4 method(s), tolerance 5)
#> 
#> Mean rank across datasets (1 = best):
#> # A tibble: 4 × 3
#>   method mean_rank n_datasets
#>   <chr>      <dbl>      <int>
#> 1 pelt        2.11          9
#> 2 wbs         2.11          9
#> 3 binseg      2.5           9
#> 4 amoc        3.28          9
#> 
#> # A tibble: 36 × 4
#>    dataset  method covering    f1
#>    <chr>    <chr>     <dbl> <dbl>
#>  1 blocks   pelt      0.993 1    
#>  2 fms      pelt      0.135 0    
#>  3 mix      pelt      0.420 0    
#>  4 teeth    pelt      1     1    
#>  5 stairs   pelt      1     1    
#>  6 step     pelt      0.993 1    
#>  7 ar1      pelt      1     1    
#>  8 heavy    pelt      0.817 0.5  
#>  9 varshift pelt      0.552 0    
#> 10 blocks   binseg    0.699 0.625
#> 11 fms      binseg    0.135 0    
#> 12 mix      binseg    0.422 0    
#> # ℹ 24 more rows
autoplot(bm)

cpt_datasets() is offline and deterministic. For the real thing, cpt_load_tcpd() downloads and caches the Turing Change Point Dataset, multi-annotator ground truth intact, and cpt_annotations() lays that ground truth out one row per annotator per changepoint.

Online monitoring

cpm, ocd and BOCPD are online methods, and for an online method “did you find the location?” is the wrong question. cpt_monitor() is a stateful detector you feed as data arrive; cpt_delay() scores it on detection delay and false alarms:

mon <- cpt_replay(c(rnorm(200), rnorm(200, 3)), method = "edetector")
alarms(mon)
#> # A tibble: 2 × 3
#>    time statistic threshold
#>   <int>     <dbl>     <dbl>
#> 1    62      105.       100
#> 2   103    10104.       100
cpt_delay(mon, truth = 200)
#> ggcpt_delay
#>   True changes:      1
#>   Detected:          1
#>   Mean delay:        3
#>   Median delay:      3
#>   False alarms:      1
#>   Average run length: 400
#> 
#> # A tibble: 1 × 4
#>   truth alarm delay detected
#>   <int> <int> <dbl> <lgl>   
#> 1   200   203     3 TRUE

cpt_replay() is the batch convenience; in production you build the monitor once with cpt_monitor() and push each new observation through cpt_update() as it arrives.

"edetector" is a native implementation of the mixture Shiryaev–Roberts e-detector (Shin, Ramdas and Rinaldo, 2023) — the one method here that is implemented rather than wrapped, because no R package implements it and it carries a finite-sample average-run-length guarantee of at least 1 / alpha with no calibration run: the shift mixture is a uniform average, so M_t - t is a mean-zero martingale under the null and optional stopping at the alarm time bounds the in-control run length.

Would I even detect it?

The question that should precede the analysis:

pw <- cpt_power(n = 300, jump = c(0.25, 0.5, 1, 2), n_sim = 50, seed = 1)
pw
#> ggcpt_power (method: pelt, change in mean, gauss noise, tolerance 5)
#>   4 scenario(s), 50 replicates each
#> 
#> # A tibble: 4 × 9
#>       n  jump sigma location power  mc_se mean_abs_error false_positive_rate
#>   <int> <dbl> <dbl>    <int> <dbl>  <dbl>          <dbl>               <dbl>
#> 1   300  0.25     1      150  0    0              NaN                   0   
#> 2   300  0.5      1      150  0.38 0.0686           2.11                0.22
#> 3   300  1        1      150  0.72 0.0635           1.83                0.28
#> 4   300  2        1      150  1    0                0.38                0   
#> # ℹ 1 more variable: n_sim <int>
#> 
#> Monte Carlo standard errors are in `mc_se`; a power of 0.80 from 50
#> replicates is only known to about +/- 0.11.
autoplot(pw)

cpt_min_detectable() inverts it and returns the smallest change reaching a target power — the number that belongs in a pre-registration. cpt_scenarios() builds the matching grid of labelled synthetic series, so the same design can be fed straight to cpt_benchmark().

Bring your own detector

Not every method is on CRAN, and some of the interesting ones never will be. cpt_register_method() teaches cpt_detect() about anything callable from R, and the whole toolchain follows:

cpt_register_method(
  "biggest_jump",
  fn = function(x, ...) which.max(abs(diff(x))),
  engine = "example",
  citation = "No citation supplied (illustration only)."
)
cpt_detect(x, method = "biggest_jump")
#> ggcpt (changepoint detection result)
#>   Method:         biggest_jump  [user-registered]
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         MBIC 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
#> 
#> This result came from a user-registered detector; the package validated
#> its shape, not its statistics. See ?cpt_register_method.
subset(cpt_methods(), status == "registered")
#> # A tibble: 1 × 15
#>   method       change_in engine  status    installed target_release multivariate
#>   <chr>        <chr>     <chr>   <chr>     <lgl>     <chr>          <lgl>       
#> 1 biggest_jump mean      example register… NA        <NA>           FALSE       
#> # ℹ 8 more variables: univariate <lgl>, online <lgl>, ci <lgl>, fitted <lgl>,
#> #   posterior <lgl>, statistic <lgl>, path <lgl>, scale_space <lgl>
cpt_unregister_method("biggest_jump")

Registered methods are visibly user-supplied: cpt_registered_methods() lists just those, cpt_methods() marks them, print() says so on every result, and cpt_cite() returns the citation you gave or states plainly that none was given. as_ggcpt() does the same job for a bare set of changepoints — a published paper’s reported breaks, say — without registering anything.

Compare methods

ggcpt_compare() runs several detectors on the same series and facets the results, so agreement (and disagreement) is visible at a glance:

ggcpt_compare(x, methods = c("pelt", "binseg", "fpop", "wbs"))

For a numeric summary, use ggcpt_compare_table():

ggcpt_compare_table(x, methods = c("pelt", "binseg", "fpop", "wbs"))
#> # A tibble: 4 × 3
#>   method    cp cp_value
#>   <chr>  <int>    <dbl>
#> 1 pelt     100    0.467
#> 2 binseg   100    0.467
#> 3 fpop     100    0.467
#> 4 wbs      100    0.467

Batch detection and stability diagnostics

cpt_batch() runs one detector over many series (a matrix, data frame, or list) and returns a tidy tibble of results — honouring future::plan() for parallel execution. cpt_stability() bootstrap-resamples within fitted segments and reports how often each location is re-detected, a cheap confidence signal for engines with no native intervals:

X <- cbind(shifted = x, noise = rnorm(200))
batch <- cpt_batch(X, method = "pelt")
batch
#> ggcpt_batch (2 series, method: pelt)
#> 
#> # A tibble: 2 × 2
#>   series  n_changepoints
#>   <chr>            <int>
#> 1 shifted              1
#> 2 noise                0
autoplot(batch)

st <- cpt_stability(x, 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)

Multivariate and high-dimensional detection

The multivariate engines accept a matrix (rows are time points) directly through cpt_detect() and render as faceted small-multiples:

set.seed(1)
Xhd <- cbind(a = c(rnorm(80), rnorm(80, 3)),
             b = c(rnorm(80), rnorm(80, -2)),
             c = rnorm(160))
res_hd <- inspect_wrapper(Xhd)
tidy(res_hd)
#> # A tibble: 1 × 3
#>      cp cp_value strength
#>   <int>    <dbl>    <dbl>
#> 1    80   -0.590     21.9
autoplot(res_hd)

Univariate methods never silently flatten a matrix: hand one to pelt and you get an error naming the multivariate alternatives instead. ocd_wrapper() insists the other way — it projects across coordinates, so it needs a matrix with at least two columns.

Evaluation

When ground truth changepoints are known, compute accuracy metrics (precision/recall/F1 under one-to-one matching within a tolerance margin, the covering metric, Hausdorff distance, adjusted Rand index):

# 98 matches the true changepoint at 100; 150 is a false positive
cpt_metrics(pred = c(98, 150), truth = c(100), n = 200)
#> # A tibble: 1 × 12
#>       n n_pred n_truth precision recall    f1 covering hausdorff rand_index
#>   <int>  <int>   <int>     <dbl>  <dbl> <dbl>    <dbl>     <dbl>      <dbl>
#> 1   200      2       1       0.5      1 0.667     0.74        50      0.719
#> # ℹ 3 more variables: annotation_error <int>, mae_matched <dbl>,
#> #   rmse_matched <dbl>

When multiple annotation sets are available, use cpt_metrics_annotated(), and visualise agreement with ggcpt_eval():

cpt_metrics_annotated(c(100), list(c(100), c(101), c(99)), n = 200, margin = 5)
#> # A tibble: 1 × 7
#>       n n_annotators n_pred precision recall    f1 covering
#>   <dbl>        <int>  <int>     <dbl>  <dbl> <dbl>    <dbl>
#> 1   200            3      1         1      1     1    0.993

Data simulation

dat <- cpt_simulate(200, changepoints = c(100), change_in = "mean",
                    params = c(0, 10), sd = 1)
attributes(dat)$true_changepoints
#> [1] 100

rcpt() is an alias, for readers who prefer the r* random-generation naming. Built-in test signals include signal_blocks() (the Donoho–Johnstone blocks signal), signal_fms(), signal_mix(), signal_teeth() and signal_stairs(), each carrying its known changepoints in a true_changepoints attribute.

Penalty configuration

cpt_penalty() constructs penalty values for the methods that take a numeric penalty. Engines differ in how they read a penalty, and ?cpt_penalty documents each convention:

cpt_penalty("BIC", n = 200)
#> [1] 5.298317
cpt_penalty("AIC", n = 200)
#> [1] 2
cpt_penalty("Manual", value = 10)
#> [1] 10

One caveat worth knowing. pelt, binseg, segneigh and fpop compare the penalty against a raw segment cost when detecting a change in mean: changepoint’s Normal cost assumes noise of standard deviation 1, and fpop’s lambda penalises the residual sum of squares directly. On a series with wider noise the penalty is effectively negligible and the segmentation shatters — for one true changepoint with a five-sigma jump, pelt returns 1 changepoint at sigma = 1 but 29 at sigma = 3 and 138 at sigma = 10. Standardise the series (cpt_detect(scale(x)[, 1], method = "pelt")), pass a penalty on the data’s own scale, or use change_in = "meanvar", which estimates a variance per segment. Every other engine — SMUCE, WBS, NOT, MOSUM, CPOP, DeCAFS, the Bayesian and nonparametric families — estimates or cancels the noise scale itself and is unaffected. See ?cpt_detect for the full note.

Direct engine wrappers

For fine-grained control, each engine also has a dedicated wrapper that exposes its own arguments and returns a ggcpt object directly. The classic search and pruning engines:

fpop_wrapper(x, penalty = 2 * log(200))
#> ggcpt (changepoint detection result)
#>   Method:         fpop
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         Manual = 10.597 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
wbs_wrapper(x, n_intervals = 2000)
#> ggcpt (changepoint detection result)
#>   Method:         wbs
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         sSIC 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
wbs2_wrapper(x)
#> ggcpt (changepoint detection result)
#>   Method:         wbs2
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         SDLL 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
not_wrapper(x, contrast = "pcwsConstMean")
#> ggcpt (changepoint detection result)
#>   Method:         not
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         sSIC 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
mosum_wrapper(x)
#> ggcpt (changepoint detection result)
#>   Method:         mosum
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         threshold = 3.6342 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
idetect_wrapper(x)
#> ggcpt (changepoint detection result)
#>   Method:         idetect
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         threshold 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
tguh_wrapper(x)
#> ggcpt (changepoint detection result)
#>   Method:         tguh
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         sSIC 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467

And the 0.4.0 wave (each behind its Suggests engine): smuce_wrapper(), cpop_wrapper(), bcp_wrapper(), bocpd_wrapper(), beast_wrapper(), cpm_wrapper(), kcp_wrapper(), npmojo_wrapper(), decafs_wrapper(), sn_wrapper(), inspect_wrapper(), ocd_wrapper(), geomcp_wrapper(), strucchange_wrapper(), segmented_wrapper(), envcpt_wrapper(), and fastcpd_wrapper().

The 0.5.0 wave adds nsp_wrapper(), mcp_wrapper(), esac_wrapper(), pilliat_wrapper(), hdcov_wrapper(), network_wrapper(), var_wrapper(), fmean_wrapper(), fcov_wrapper(), kwc_wrapper(), fabisearch_wrapper(), wbsts_wrapper(), bfast_wrapper(), trend_wrapper(), taylor_wrapper(), hdreg_wrapper() and binsegrcpp_wrapper().

# change-in-slope: exact penalised broken-line estimation
y_slope <- cumsum(c(rep(0.4, 100), rep(-0.3, 100))) + rnorm(200)
res_slope <- cpop_wrapper(y_slope)
autoplot(res_slope, show_fit = TRUE)

Custom geoms, stats, and theming

The package provides composable ggplot2 layers for changepoint visualisation:

library(ggplot2)

# Use geom_changepoint as a standalone layer
cp_tbl <- tidy(cpt_detect(x, method = "pelt", change_in = "mean"))
ggplot(data.frame(index = seq_along(x), value = x), aes(index, value)) +
  geom_line() +
  geom_changepoint(data = cp_tbl, aes(xintercept = cp), color = "red") +
  theme_ggcpt()

# Use stat_changepoint to compute and draw changepoints in one step
ggplot(data.frame(index = seq_along(x), value = x), aes(index, value)) +
  geom_line() +
  stat_changepoint(method = "pelt", color = "red")

# Shade alternating segments between changepoints
ggplot(data.frame(index = seq_along(x), value = x), aes(index, value)) +
  geom_line() +
  annotate_segments(cp = cp_tbl$cp, n = length(x))

# Draw each segment's estimated level with geom_cpt_segment
segs <- cpt_detect(x, method = "pelt", change_in = "mean")$segments
ggplot(data.frame(index = seq_along(x), value = x), aes(index, value)) +
  geom_line() +
  geom_cpt_segment(data = segs,
                   aes(x = start, xend = end,
                       y = param_estimate, yend = param_estimate),
                   color = "blue", linewidth = 1)

# Draw confidence intervals with geom_cpt_ci (when the engine provides them)
cp_ci <- tidy(smuce_wrapper(x))
ggplot(data.frame(index = seq_along(x), value = x), aes(index, value)) +
  geom_line() +
  geom_cpt_ci(data = cp_ci,
              aes(x = cp, xmin = ci_lower, xmax = ci_upper, y = min(x) - 1))

# Shade an interval that provably contains a change with geom_cpt_region,
# and mark known external events with geom_cpt_event
ggplot(data.frame(index = seq_along(x), value = x), aes(index, value)) +
  geom_cpt_region(aes(xmin = start, xmax = end),
                  data = cpt_regions(cpt_detect(x, method = "nsp"))) +
  geom_line() +
  geom_cpt_event(aes(xintercept = when, label = what),
                 data = data.frame(when = 100, what = "policy change"))

Colour is never the only channel carrying meaning. scale_colour_cpt() (and its scale_color_cpt() alias), scale_fill_cpt() and scale_linetype_cpt() are the Okabe-Ito palette used throughout, safe under deuteranopia, protanopia and tritanopia; scale_colour_cpt_label() is the matching discrete scale for the label-error vocabulary. Every autoplot() also carries generated alt text, so a rendered figure reaches a screen reader without the author writing anything.

Interactive exploration and citations

Any result — or any ggplot built from one — renders as an interactive widget with ggcpt_interactive() (requires plotly). cpt_cite() returns the methodological reference behind a result, so an analysis 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.

ggcpt_interactive(res)   # hover for values; requires plotly

Class constructors

Advanced users can construct ggcpt objects directly or test for the class:

new_ggcpt(
  changepoints = tibble::tibble(cp = 100L, cp_value = 5.0),
  data = tibble::tibble(index = 1:200, value = rnorm(200)),
  method = "manual"
)
is_ggcpt(res)

The original 0.1.0 API

cpt_wrapper(), ecp_wrapper(), ggcptplot() and ggecpplot() all continue to work unchanged. ecp_wrapper() and ggecpplot() reach the ecp engine directly, including genuine multivariate input:

ecp_wrapper(x, algorithm = "divisive")
ggecpplot(x, algorithm = "divisive")

cpt_wrapper(x)
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
ggcptplot(x)

Additional S3 methods

The ggcpt class also provides:

res <- cpt_detect(x, method = "pelt", change_in = "mean")
summary(res)          # human-readable digest
#> ggcpt Summary
#>   Method:                   pelt 
#>   Change in:                mean 
#>   Changepoints found:       1 
#>   CP convention:            left 
#>   Series length:            200 
#>   Penalty:                  MBIC 
#>   Runtime (seconds):        0.007 
#> 
#> Segments:
#> # A tibble: 2 × 5
#>   seg_id start   end     n param_estimate
#>    <int> <int> <int> <int>          <dbl>
#> 1      1     1   100   100          0.139
#> 2      2   101   200   100          9.80 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
as_tibble(res)        # tibble of changepoints
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
as.data.frame(res)    # data frame of changepoints
#>    cp cp_value
#> 1 100 0.467023
format(res)           # one-line summary string
#> [1] "ggcpt [pelt] 1 changepoint(s) on 200 observations"
plot(res)             # base-graphics fallback (delegates to autoplot)

Learn more

The full reference index and rendered vignettes live at https://pursuitofdatascience.github.io/ggchangepoint/. From R:

• vignette("ggchangepoint", package = "ggchangepoint") — feature tour
• vignette("introduction", package = "ggchangepoint") — the framework, in research-paper form
• vignette("comparison", package = "ggchangepoint") — method comparison and evaluation