A stateful sequential changepoint monitor

Creates a detector that consumes observations as they arrive and raises alarms, rather than segmenting a series that is already complete. Feed it with cpt_update(), read its alarm log with alarms(), and score it with cpt_delay() — because for an online method "did you find the location?" is the wrong question and "how long did you take, and how often do you false-alarm?" is the right one.

Usage

cpt_monitor(
  method = c("edetector", "cpm", "ocd"),
  baseline = NULL,
  alpha = 0.01,
  arl0 = 500,
  cpm_type = "Mann-Whitney",
  patience = 5000,
  deltas = c(0.5, 1, 2),
  reset = TRUE,
  relearn = 20,
  thresh = "MC",
  mc_reps = 100,
  ...
)

# S3 method for class 'ggcpt_monitor'
tidy(x, ...)

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

# S3 method for class 'ggcpt_monitor'
autoplot(object, plot_type = c("timeline", "statistic", "runlength"), ...)

Arguments

method

Which sequential detector:

"edetector": (default) a mixture Shiryaev–Roberts e-detector; see the section below.
"cpm": cpm's sequential change-point model, tuned by ARL0.
"ocd": ocd's high-dimensional online detector. Multivariate only – it tracks a projection of the whole vector and needs at least two coordinates, so it refuses a single series rather than falling back to a univariate statistic.

baseline

A numeric vector (or, for "ocd", a matrix with rows as time points) of pre-change training data used to estimate the in-control mean and scale. Required for "edetector" and "ocd"; optional for "cpm", which has its own start-up period.

alpha

Target false-alarm probability for "edetector": the average run length under the null is at least 1 / alpha, by optional stopping on the martingale \(M_t - t\) (see Details). Defaults to 0.01.

arl0

Target in-control average run length for "cpm". Defaults to 500.

cpm_type

Statistic for "cpm". Defaults to "Mann-Whitney".

patience

Target patience (average run length) for "ocd". Defaults to 5000.

deltas

Shift sizes, in baseline standard deviations, mixed over by "edetector". Defaults to c(0.5, 1, 2), each taken in both directions. The mixture is a uniform average, so adding shifts costs power at the ones already there rather than inflating the false-alarm rate; mix over the range you think the change could fall in, not over everything.

reset

After an alarm, restart the detector (TRUE, the default) or leave it running? Restarting is what makes a monitor report repeated changes rather than latching on the first one.

relearn

How many observations after an alarm are used to re-learn the in-control baseline, during which no further alarm can fire. Defaults to 20. This matters more than it looks: a real change is persistent, so a detector that restarts against the stale pre-change baseline alarms again on the very next observation and keeps alarming for the rest of the series — the monitor reports one change as hundreds, and cpt_delay() then counts them all as false alarms. Set relearn = 0 to switch the behaviour off and see every threshold crossing.

thresh

Threshold rule for "ocd": "MC" (default) calibrates by Monte Carlo against patience, or supply a numeric vector of three thresholds. The Monte Carlo calibration is the expensive part of building an "ocd" monitor — a minute or more at the default patience — so pass thresholds directly when you already have them, or lower mc_reps while exploring.

mc_reps

Monte Carlo repetitions for the "ocd" threshold.

...

Additional arguments passed to the engine's constructor.

x

A ggcpt_monitor object (for print()).

object

A ggcpt_monitor object (for autoplot()).

plot_type

"timeline" (the monitored series with the alarms marked), "statistic" (the running detection statistic against its threshold) or "runlength" (the gaps between alarms, which estimate the run length). For a multivariate monitor the timeline draws the first coordinate — the alarms are shared, so the rules are right whichever coordinate is shown, but the line is one of several.

Value

A ggcpt_monitor object.

The e-detector, and why it is implemented rather than wrapped

Shin, Ramdas and Rinaldo (2023) give a nonparametric sequential framework with non-asymptotic control of the average run length, and it has no R implementation. The construction used here is the mixture Shiryaev–Roberts e-detector for a sub-Gaussian shift. For each candidate shift \(\delta\) the increment is the likelihood ratio \(e_t^{(\delta)} = \exp(\delta (X_t - \mu_0)/\sigma^2 - \delta^2/(2\sigma^2))\), which has unit mean under the null; the running statistic is \(R_t^{(\delta)} = (1 + R_{t-1}^{(\delta)}) e_t^{(\delta)}\); the shifts are combined by averaging, \(M_t = K^{-1} \sum_\delta R_t^{(\delta)}\); and an alarm is raised the first time \(M_t \ge 1/\alpha\).

The averaging is not a detail. Under the null \(M_t - t\) is a mean-zero martingale, so optional stopping at the alarm time \(\tau\) gives \(E_\infty[\tau] = E_\infty[M_\tau] \ge 1/\alpha\): a finite-sample lower bound on the in-control average run length, with no asymptotics and no calibration run. A convex combination of e-detectors is an e-detector; a maximum of them is not, and taking one silently multiplies the false-alarm rate by roughly the number of shifts mixed over. Every other detector in this package wraps published, separately maintained code; this one does not, and is labelled as such wherever it appears.

References

Shin J, Ramdas A, Rinaldo A (2023). “E-detectors: a nonparametric framework for sequential change detection.” The New England Journal of Statistics in Data Science, 1(2), 229–260. doi:10.51387/23-NEJSDS49 .

Examples

set.seed(2026)
mon <- cpt_monitor("edetector", baseline = rnorm(100))
mon <- cpt_update(mon, rnorm(50))          # still in control
mon <- cpt_update(mon, rnorm(50, 3))       # a change arrives
alarms(mon)
#> # A tibble: 1 × 3
#>    time statistic threshold
#>   <int>     <dbl>     <dbl>
#> 1    51      134.       100