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.