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Anytime-valid control charts with e-detectors.
edetect answers one question: how do I monitor a
process for a change and raise an alarm with a guaranteed false-alarm
rate, without assuming a parametric model for the process before or
after the change? It implements e-detectors (Shin,
Ramdas & Rinaldo, 2024): nonparametric, anytime-valid analogues of
the CUSUM and Shiryaev–Roberts charts, built from e-values for bounded,
sub-Gaussian, Bernoulli or Poisson observations, with a guaranteed
average run length (ARL) to a false alarm under every
distribution in the declared pre-change class.
The package follows the Phase I / Phase II workflow of shewhartr and the result-contract conventions of seqbench: every alarm (or its absence) comes with what was assumed, how the detector was built and what was observed.
# install.packages("pak")
pak::pak("castlaboratory/edetect")A bounded loss whose mean should stay at 0.5 rises after 60 observations.
library(edetect)
set.seed(1)
x <- c(runif(60, 0.3, 0.7), runif(60, 0.5, 0.9))
chart <- edetect_x(x, bounds = c(0, 1), center = 0.5, arl = 370, direction = "up")
#> Warning: Alarm at t = 71; 49 later observations were not consumed.
edetect_alarm(chart)
#> # A tibble: 1 × 6
#> alarm alarm_time evidence threshold change_estimate n_observed
#> <lgl> <int> <dbl> <dbl> <int> <int>
#> 1 TRUE 71 387. 370 58 71
autoplot(chart)The same engine drives the attribute charts and a regression chart:
edetect_p(defects, n = 100, center = 0.02, arl = 370) # proportion nonconforming
edetect_c(counts, phase1 = counts_in_control, arl = 370) # counts per unit
edetect_u(counts, n = exposure, center = 1.5, arl = 370) # counts per unit exposure
edetect_regression(y ~ x, phase1 = d1, phase2 = d2, arl = 370) # residuals of a Phase I modelLower-level functions give full control:
edetect_design() fixes the pre-change class, the direction,
the detector type (SR or CUSUM) and the mixture of post-change shifts;
edetect_init() and edetect_update() run it one
observation at a time; edetect_report(),
tidy(), glance() and autoplot()
read the result; edetect_arl() simulates run lengths and
detection delays of a design. The vignettes on the package site work
through the industrial datasets of shewhartr: tablet
weights, bottle filling, insurance claims, solder defects and an oven
with drift.
For a design with target arl, every pre-change
distribution in the declared class gives an expected time to a false
alarm of at least arl, and a probability of a false alarm
by any time t of at most t / arl (Ramdas,
2026). The classes are bounded (known bounds, nothing else),
sub-Gaussian (declared scale), Bernoulli (p chart;
dependence within a subgroup allowed given the conditional
probabilities) and Poisson (c and u charts; no overdispersion).
The price is detection delay, which the mixture of shifts and
edetect_arl() let you measure before monitoring.
edetect is not a general change-point library. For
classical charts calibrated by simulation see shewhartr and qcc; for ARL
computation of CUSUM/EWMA charts see spc; for nonparametric
sequential change-point models see cpm. No package
implementing e-detectors was found on CRAN or PyPI (checked 2026-09-25
and 2026-10-10).
Ramdas, A. (2026). Universality of e-detectors for ARL control. arXiv:2608.12660.
Shin, J., Ramdas, A. and Rinaldo, A. (2024). E-detectors: a nonparametric framework for sequential change detection. The New England Journal of Statistics in Data Science, 2(2), 229–260.
GPL (>= 3). © CAST Lab, Universidade Federal de Pernambuco.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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