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cpt_detect() grows from 13 to 31 wired methods. Eighteen
new wrappers, all of whose engines live on CRAN and enter
Suggests behind requireNamespace() guards:
smuce_wrapper() — SMUCE/HSMUCE multiscale inference
(stepR), the first engines to populate
ci_lower/ci_upper confidence-interval
columns.cpop_wrapper() — exact change-in-slope detection
(cpop); cpt_detect(change_in = "slope") now
routes here or to NOT’s linear contrast instead of erroring.bcp_wrapper(), bocpd_wrapper(),
beast_wrapper() — the Bayesian pillar (bcp,
ocp, Rbeast), with posterior_prob
columns and the posterior mean carried as a fitted signal.cpm_wrapper() — sequential distribution-free detection
(cpm), with a detection_time column.kcp_wrapper() — kernel change point analysis on running
statistics (kcpRS; mean, variance, autocorrelation,
correlation).npmojo_wrapper() — nonparametric MOSUM under serial
dependence (CptNonPar).decafs_wrapper() — abrupt changes amid drift and AR(1)
noise (DeCAFS).sn_wrapper() — self-normalised segmentation
(SNSeg; mean, variance, acf, bivariate correlation).inspect_wrapper(), ocd_wrapper(),
geomcp_wrapper() — high-dimensional and multivariate
detection (InspectChangepoint, ocd,
changepoint.geo).strucchange_wrapper() — Bai-Perron structural breaks
with break-date confidence intervals (strucchange); accepts
a bare series or a regression formula.segmented_wrapper() — broken-line regression with kink
confidence intervals (segmented).envcpt_wrapper() — changepoints vs. trends
vs. autocorrelation model selection (EnvCpt).fastcpd_wrapper() — the modern fastcpd engine
(fastcpd), covering mean/variance/meanvariance plus
AR/ARMA/GARCH model changepoints.cpt_crops() computes the full CROPS penalty path
and returns a ggcpt_path object with print(),
tidy(), and
autoplot(type = c("elbow", "path", "segmentations")).cpt_batch() runs one detector over many series
(matrix, data frame, or list) with optional future
parallelism; returns a ggcpt_batch tibble with
tidy() and a faceted autoplot().cpt_stability() bootstrap stability diagnostic:
segment-preserving resampling with a detection-frequency profile and
autoplot().ggcpt_posterior() (posterior
mean + per-location changepoint probability) and
ggcpt_runlength() (the BOCPD run-length posterior
heatmap).ggcpt_interactive() renders any result as a
plotly widget.cpt_cite() returns the verified methodological
reference(s) behind a result or method name.autoplot.ggcpt() gains show_ci (draws
changepoint-location confidence intervals from
ci_lower/ci_upper) and show_fit
(overlays the engine’s fitted signal), and renders multivariate results
as faceted small-multiples.geom_cpt_ci() migrated off the deprecated
ggplot2::geom_errorbarh() to
geom_errorbar(orientation = "y").autoplot()/ggcptplot() now warn instead of
being silently discarded, and plotting an empty ggcpt
errors cleanly instead of producing infinite axis limits.cpt_detect(penalty = <number>) works for the
changepoint-package methods (pelt, binseg,
segneigh, amoc): a numeric penalty is now
translated to the engine’s
penalty = "Manual", pen.value = <number> instead of
erroring with “Unknown Penalty” (#2).binseg / segneigh no longer crash on short
series that pass validation; the maximum number of segments
Q is clamped to a length-safe value (#3).augment() uses the engine’s fitted signal when the
result carries one, instead of always the per-segment mean (#4).augment() keeps all coordinates for a multivariate
result instead of dropping everything but the first (#5).segments table’s start /
n columns are integer, matching the documented schema
(#6).signal_mix() gains a minimum-n guard and
filters its changepoint indices, so true_changepoints no
longer contains 0, n, or duplicates for small
n (#7).autoplot() honours the index argument for
multivariate results (#8).cpt_batch() / ggcpt_compare() no longer
crash with “factor level duplicated” when two series share a name, or
the methods vector repeats (#9).wbs returns an empty result instead of erroring when a
manual threshold admits no changepoints (#10).idetect returns an empty result on short series instead
of erroring with “wrong sign in ‘by’ argument” (#11).ecp_wrapper() no longer fabricates changepoints on
no-change data (the positional boundary strip reversed
c(1, n+1)), and no longer drops genuine changepoints in
e.agglo’s wrap-around case (C1).wbs_wrapper() now returns the sSIC model selection it
documents; a manual threshold is recorded as the penalty actually used
(C2).cpt_detect() now error on
multi-column input instead of silently flattening it column-major
(C3).idetect_wrapper() returns an empty result on no-change
data instead of erroring (C4).tguh_wrapper() pins breakfast’s model selection to
“ic”: no more spurious changepoint on constant data, no crash on short
series, and the scalar-0 “no changepoints” sentinel is handled
(C5).glance() is always one row: fpop’s per-position cost
vector no longer explodes the tibble, and $ partial
matching no longer grabs unrelated fit elements (C6).mosum_wrapper() records the numeric threshold as
penalty$value (was the string “critical.value”) and
implements its documented multiscale argument via
mosum::multiscale.localPrune() (C7, C8).cpt_detect() forwards change_in to NOT via
contrast mapping and the result reports what actually ran (C9);
penalty = "None" resolves to 0 for numeric-penalty engines
(C10).cpt_penalty("sSIC") implements the strengthened SIC
k * log(n)^alpha (was 0.5 * k * log(n), weaker
than BIC) (C11).ggcpt_eval() uses the same one-to-one
matching as cpt_metrics() and its “Miss” legend entry
renders (C15).ggcpt_compare() keeps a facet panel for every method,
including those that found nothing, and no longer errors when no method
finds anything (C16).stat_changepoint() sorts by the x
aesthetic before detecting (results were previously row-order dependent)
and declares dropped_aes so building the plot is
warning-free (C17).signal_blocks() generates the true Donoho-Johnstone
blocks signal (cumulative jumps, not absolute levels) (C18); simulated
t-noise is rescaled so its standard deviation matches sd
(C19); all signal generators validate their minimum lengths (C20).cpt_wrapper(cp_method = "SegNeigh") falls back to the
SIC penalty the engine supports instead of always erroring under the
default; np results report
change_in = "distribution"; meanvar results
stay "meanvar" in the user’s vocabulary;
ggecpplot() handles multivariate input without
crashing.smuce_wrapper(),
cpop_wrapper(), bcp_wrapper(),
bocpd_wrapper(), beast_wrapper(),
cpm_wrapper(), decafs_wrapper(),
strucchange_wrapper(), segmented_wrapper() and
envcpt_wrapper() turned a 120x2 matrix into a 240-point
series. The new cpt_crops() and
cpt_stability() entry points guard the same way (R16).segneigh no longer errors with “subscript out of
bounds” on short series. The Q clamp added for #3 missed
the engine’s real constraint: Segment Neighbourhood requires
Q >= 3 regardless of length, so the clamped
Q of 1 or 2 failed for every n < 8.
Q is now clamped into the engine’s valid window
(3 <= Q <= n - 2 for a mean change,
floor(n / 2) + 1 when a variance is estimated per segment),
and a series too short to admit any valid Q gets an
actionable message naming the constraint instead of the engine’s
internal error (R17).index no
longer crashes mv_data_wide() with “Column name
index must not be duplicated”; it is made unique against
the position column, so ecp, inspect,
geomcp, ocd, npmojo,
kcp and fastcpd all accept such data
(R18).NA changepoint indices from an engine are dropped
rather than propagating into build_segments() as an “NA/NaN
argument” error, and any engine-supplied extra columns
(ci_lower, posterior_prob, …) stay row-aligned
through the drop (R19).cpt_penalty()’s "MBIC" no longer
misattributes its formula to Zhang and Siegmund (2007), whose modified
BIC penalty depends on the segment lengths and cannot be written as a
function of n and k alone. The computed value
is unchanged; the documentation now states what it is (BIC plus a
combinatorial placement term) and how it differs.cpt_detect()
no longer errors with “formal argument … matched by multiple actual
arguments”. The dispatcher derives some arguments from
change_in and was passing them alongside the caller’s, so
the documented ... passthrough was broken for
not’s contrast, cpm’s
cpm_type, kcp’s running_stat,
sn’s parameter, fastcpd’s
family and hsmuce’s family. A
value supplied by the caller now wins over the derived one (R20).smuce_wrapper(family = "poisson") — current
stepR accepts no such family, so it always errored — and
cpm_wrapper(cpm_type = "GLRAdjusted"), which
cpm::processStream() rejects by printing an error
and returning no changepoints, making it silently report “no changes”
for any input. cpm_type = "FET" is retained and documented
as needing 0/1 data plus a lambda value.ocd_wrapper() no longer advertises univariate input:
ocd’s detector cannot be constructed for a single
coordinate (it fails with “subscript out of bounds”), so a bare vector
now gets a message naming the requirement instead of the engine’s
internal error (R22).sn, kcp,
npmojo, inspect) or, for
segmented, a spurious kink recovered from a singular fit
(R23).inspect, npmojo and kcp
standardise each coordinate, so one flat column (a dead sensor channel,
say) made their statistics undefined and the whole call failed with
“missing value where TRUE/FALSE needed” even when the other coordinates
carried an obvious change. Flat coordinates are now dropped with a
warning naming them, detection proceeds on the rest, reported locations
stay in the original row space, and the dropped coordinates are still
kept for plotting (R24).kcp and sn explain themselves on series
too short for their windows, instead of surfacing “wrong sign in ‘by’
argument” and “only 0’s may be mixed with negative subscripts”
(R25).print() and summary() no longer render
penalties at full double precision or with a placeholder value:
Penalty: Manual = 17.8459510605346 is now
Manual = 17.846, and a penalty that carries no numeric
value prints as MBIC rather than
MBIC = NA.The whole exported surface was exercised with degenerate, contract-violating and self-generated input. Items are listed with the ones that change an answer or end a session first.
hsmuce no longer aborts the R session. When a series
carries essentially no noise at the per-segment scale,
stepR’s heterogeneous variance estimator does not raise an
R error but terminates the session, so nothing downstream can
catch it and the user loses their work. It is reachable straight from
cpt_detect(x, method = "hsmuce"). Two regimes were measured
as fatal: a globally flat series such as
rep(4, 300) + rnorm(300, 0, 2e-7), and — more dangerous,
because it looks entirely ordinary — a clean step whose segments are
numerically constant,
c(rep(0, 150), rep(5, 150)) + rnorm(300, 0, 1e-9), which is
what cpt_simulate(sd = 0) produces once any rounding is
added. Both are refused when the point-to-point variation lies more than
about seven orders of magnitude below the data’s own scale, with a
message naming family = "gauss", which handles the whole
range. An exactly noiseless series is safe upstream and still works
(R53).idetect no longer invents changepoints on a constant
series. IDetect::ID() is erratic on flat input — its
statistics go to 0/0, and what it returns depends on the value and the
length: rep(3, 200) came back with 126
changepoints at 1, 3, 4, 6, 7, …, while rep(0, 100) errors
and rep(-2.5, 60) returns a sentinel 0. Every other search
wrapper reports none, and the 0.4.0 audit fixed exactly this class of
bug for segmented, sn, kcp,
npmojo and inspect — idetect was
missed. It now short-circuits to the empty result, decided by exact
equality so a series with tiny but genuine variation still reaches the
engine (R50).cpt_stability() reports the quantity it documents.
freq is described as “the proportion of replicates
detecting a changepoint within margin of that index”, but
the loop incremented once per changepoint, so a replicate whose
detections had overlapping ±margin windows was counted
twice at the shared indices; pmin(hits / B, 1) then hid the
overflow by clipping it. The effect was to inflate exactly the number
the function exists to report — in a measured example an index that only
half the replicates covered was shown as 1.00, “re-detected every time”.
Each replicate now contributes at most one to any index, so
freq is a genuine proportion and needs no clipping
(R38).glance() always returns the single row it documents.
new_ggcpt() defaulted method and
change_in to character(0), so
tibble() recycled every other column down to zero rows — an
empty summary for any hand-built result, including the one the README
demonstrates. Those defaults are now NA_character_, and
glance() coerces the metadata fields to length one whatever
the object carries (R37).changepoint engines (pelt,
binseg, segneigh, amoc,
np) keep their upstream cpt object in
$fit. It was NULL, although $fit
is documented as “the raw upstream object” and every other engine stored
one — which also left the inherits(fit, "cpt") branch of
glance() unreachable, and with it a sign error and a wrong
element index that had never run. glance()$total_cost now
reports the unpenalised −2 log L for those engines where
changepoint exposes it on that scale, and stays
NA where it does not, rather than mixing two scales in one
column; ?glance.ggcpt spells out which cases are which
(R35).ggcpt_interactive() works on multivariate results. The
faceted small-multiple that autoplot() builds for them used
a facet column named variable, which is also the name
plotly::ggplotly() gives a column of its own when it melts
the built plot, so every multivariate result failed with “Names must be
unique”. The column is now coordinate; the facet strips are
unchanged (R36).add_column() reject the wide frame with “must have unique
names as of tibble 3.0.0”; the R18 fix had only deduplicated a
coordinate named index against the position column, not the
coordinates against each other. All coordinate names are now made unique
in one pass (R34).envcpt no longer prints its engine’s internal failures
as though the call had failed. EnvCpt fits up to twelve
models with try(), and a non-silent try()
writes its error straight to stderr, so on a degenerate series
envcpt_wrapper() printed six lines beginning “Error in
arima(…): non-stationary AR part from CSS” and then returned a perfectly
good result. Those failures are expected — the criterion ignores the
models that did not fit — so the message stream is diverted for the
duration of the call. Genuine warnings are deferred past the diversion
and still reach the user, and a call that really does fail still errors
(R52).cpt_simulate(change_in = "meanvar") works without
params. It was the one change type with no parameter
default, so the call died with “replacement has length zero” instead of
simulating anything (R32).cpt_simulate() warns about recycled parameters for
every change type, not only "mean". Supplying fewer
parameters than there are segments reuses the last one, so the trailing
entries of changepoints were recorded in
true_changepoints with no actual change behind them —
silently wrong ground truth for "var",
"meanvar" and "slope" (R32).cpm ships thresholds only for a fixed set of average run
lengths; for any other arl0 its
processStream() prints “Error: No thresholds
available for selected ARL0” and returns an empty result instead of
raising a condition, so tryCatch() never saw it and the
wrapper reported zero changepoints on a series with an obvious one — the
same trap the earlier audit found for
cpm_type = "GLRAdjusted", on a different argument. And
kcp_wrapper() with nperm below 2 either
reported nothing (0 or negative) or died inside the engine with an
unreadable row.names error (1). Both are refused now, with
the supported arl0 values named in the message (R61).conf_level no longer hangs
strucchange_wrapper(). stats::confint() on a
breakpoints fit at level = 2 never returns, and the
tryCatch() already around that call cannot rescue a call
that does not terminate — so the session simply locked up.
conf_level is now required to lie strictly between 0 and 1
in both strucchange_wrapper() and
segmented_wrapper(). In the same sweep:
bocpd_wrapper(hazard) and cpop_wrapper(sd)
must be positive, and wbs_wrapper(n_intervals) at least 1 —
all previously accepted meaningless values (R60).cpt_simulate() refuses parameters that made it emit
NaN. It is where ground truth for every benchmark comes
from, so a silent series of NaN is the worst thing it can
produce — and sd = -1, sd = NA, and
|rho| >= 1 under the AR(1) model each did exactly that,
with no error and no warning (sqrt(1 - rho^2) is not a
number outside the stationary range). Non-positive n is
refused too. rho is checked only for
noise = "ar1", so a stray value the chosen model ignores is
still accepted (R59).show_segments, show_ci,
show_fit, show_line, show_points
and mosum_wrapper(multiscale) are all documented as
“Logical” but were read with isTRUE(), which treats
everything that is not TRUE as FALSE. So
show_segments = 1, = "yes",
= "TRUE" or = NA quietly drew nothing, and
show_line = 1 quietly removed the line the user
was asking to keep — three layers down to one.
show_points = NULL keeps its documented meaning of deciding
from the series length (R58).stepR refuses an
alpha outside (0, 1), SNSeg an unlisted
confidence — but ggchangepoint’s were taken on trust, and
out-of-range values returned answers instead of errors:
cpt_metrics(margin = -3) scored a perfect
segmentation as precision 0 and recall 0;
cpt_stability(B = 0) produced a stability profile of
NaN; cpt_metrics(n = -10) a covering metric of
−1; bcp_wrapper and beast_wrapper with
prob_threshold = 0 reported 239 changepoints in a 240-point
series; kcp_wrapper(alpha = 2) and
cpt_crops(pen_min = -5) ran regardless. All are refused
now, with the legitimate boundaries (margin = 0,
B = 1, n = 1, prob_threshold = 1)
still accepted (R57).mosum_wrapper()’s automatic bandwidth is never 1.
min(n / 10, 100) rounds to 1 for every
n < 20, and a one-observation window leaves the engine’s
studentised statistic undefined, so it warned “NaNs produced” and
returned spurious changepoints rather than failing. The automatic
bandwidth is floored at 2, and a series too short for any window gets an
actionable message (R29).npmojo_wrapper()’s default bandwidth is capped at
n / 2, the largest the engine accepts. The documented
max(20, 0.1 * n) exceeded that for every series shorter
than 40, so the default always failed with “Bandwidth is too large for
the length of time series”. Series of 40 or more observations are
unchanged (R30).cpt_wrapper(change_in = "np") refuses
cp_method values other than "PELT" up front.
changepoint.np::cpt.np() implements PELT only, so
"BinSeg" and "SegNeigh" used to die on the
internal Q clamp with “unused argument (Q = 5)” and
"AMOC" surfaced the engine’s “Invalid Method” (R27).autoplot(), ggcptplot() and
ggecpplot() reject an index whose length does
not match the series, naming the argument at fault, instead of surfacing
dplyr’s recycling error (“x must be size 200 or 1, not
10”), which never mentions index (R26).cpt_penalty() enforces the argument ranges it documents
(R28): alpha > 1 for "sSIC" (at or below 1
it is weaker than BIC, so no longer a strengthened SIC);
n >= 3 for the log-based penalties (log(n)
is 0 at n = 1 and log(log(n)) is negative
below n = 3, so the “penalty” rewarded extra changepoints);
and 0 <= k <= n for "MBIC", whose
log C(n, k) term is -Inf beyond that.
"AIC", which does not involve n, is
exempt.cpt_batch() names the series that failed. It exists for
panels of hundreds of series, but an error in any one of them surfaced
only as the underlying complaint — “x must have at least 3
observations” — leaving the user to bisect the list to find which. The
message is now prefixed with the series name and its position,
e.g. Series `short` (2 of 3): (R49).cpt_detect() records the
cpt_detect() call in $call. It previously held
the internal helper each branch happened to use — e.g.
wrap_cpt_to_ggcpt(x = data_vec, change_in = ci, ...), an
unexported function named with the dispatcher’s local symbols, which a
reader can neither recognise nor re-run. Wrappers called directly still
record themselves (R33).cpt_cite() on a result with no method name says so,
instead of surfacing tibble’s “Can’t subset rows with
refs$method == method” (R37).ggcpt_eval() no longer warns “No shared levels found …”
when there is nothing to draw: a run with no predictions and no ground
truth is a perfect score, not a broken plot (R31).ggcpt_compare() pads its changepoint rules by a fixed
amount on a flat series, as ggcptplot() already did; a zero
data range would otherwise collapse them to invisible zero-height
segments.glance() no longer carries an unreachable branch. It
tested inherits(fit, "cptrange"), but the
changepoint class is cpt.range — with a dot —
so the branch could never fire, and its body used $ on an
S4 object, which would have errored had it ever been reached. Removed;
the BinSeg/SegNeigh case is handled explicitly alongside the other
engines whose cost is on a different scale (R46).?new_ggcpt and ?ecp_wrapper explain why
$fit is NULL for "ecp" and only
for "ecp": ecp::e.agglo() returns a
cluster-progression matrix that is quadratic in the series length, so
retaining it by default would make the result object explode on a long
series — 207 kB of fit for a 1.3 kB series at n = 160 alone (R46).cpt_metrics() compared every truth segment against every
prediction segment, so scoring a segmentation with many changepoints
crawled — 7.5 seconds for 3000 of them. Because both partitions tile the
series and their breakpoints are sorted, only the overlapping prediction
segments can win, and two findInterval() lookups locate
them; the same case now takes 0.42 seconds. The numbers are unchanged:
verified identical on 4010 cases (4000 random plus adversarial
partitions) and pinned in the tests against an independent set-based
statement of the definition (R42)..onLoad() is gone. It re-registered
print, plot, summary,
tidy, glance, augment and
autoplot at load time — writing into base‘s
and generics’ S3 method tables — even though NAMESPACE
already declares every one of them, and it wrapped the lot in
suppressWarnings(), so a genuine registration failure would
have been invisible. It was a leftover from before
@exportS3Method base::generic was adopted in 0.3.0.
Verified redundant before removing: all eleven methods still dispatch
with and without the package attached, and every declared generic/class
pair still resolves through getS3method() (R41).?ocd_wrapper says how long it takes. Nearly all of
ocd’s cost is Monte Carlo threshold calibration, which
happens before a single observation is read: measured at
mc_reps = 5, construction is about 3 s at p = 3, 9 s at p =
10 and 55 s at p = 50, and four times that at mc_reps = 20
— so the default mc_reps = 100 extrapolates to roughly a
quarter of an hour at p = 50. The help now gives those numbers, notes
that monitoring the observations afterwards is comparatively free, and
points at thresh, which takes the three thresholds directly
and skips calibration entirely. That escape hatch had no test; it has
one now (R56).stats is declared in Imports.cpt_metrics() and
ggcpt_eval(), its segments feeding
geom_cpt_segment(), the object itself feeding
cpt_cite(). The chain was run for all 31 methods — it
completes for every one, and 24 of them recover both planted
changepoints with precision, recall, F1 and covering all exactly 1. The
exceptions are all correct by construction: amoc finds at
most one changepoint, cpop and segmented are
slope engines being shown a step, ocd is online and reports
declaration times, and geomcp unions its distance and angle
mappings. A six-method version spanning the structural variety is now in
the suite (R55).cpt_simulate() and every
canonical signal was run through all 31 methods to confirm none of them
can produce input that terminates the session. Three configurations do
land in the degenerate band and are now refused by hsmuce
rather than crashing it: sd = 0 and sd = 1e-9
for a change in mean, and — the one the audit turned up —
change_in = "slope" with sd = 0, whose
consecutive differences are a constant slope, so its point-to-point
variation is floating-point residue of about 1e-14 rather than zero.
Nothing else crashes on any of them, and the realistic settings and all
five canonical signals are unaffected (R54).change_in translations are tested.
cpt_detect() derives an engine-specific argument from
change_in for not, cpm,
kcp, sn and fastcpd; the suite
covered overriding those through ... but never the
derivation, so a wrong translation would have silently run the wrong
analysis. Each is now checked against the equivalent explicit call
(R51).cpt_metrics()’s one-to-one matching is verified to be a
genuine maximum matching, which is what ?cpt_metrics claims
and what precision and recall are derived from — if the greedy scan ever
fell short, both would be silently understated. Checked against an exact
maximum bipartite matching on 300 random configurations plus seven
clustered and interleaved patterns chosen to break a greedy rule: it
never falls short (R48).ggcpt_posterior() on a beast_wrapper() result
(the help says it handles both bcp and BEAST, but only the bcp branch of
the profile extractor was ever run), and every guard on
ggcpt_posterior()/ggcpt_runlength() —
non-ggcpt input, a result with no posterior, and a
prob_floor that leaves nothing to draw (R47).ggcpt_compare_table() and
cpt_metrics_annotated() — alongside a set of documented
modes and arguments that nothing exercised:
ecp_wrapper(algorithm = "agglo"),
sn_wrapper(parameter = "bivcor"),
cpt_simulate(noise = "ar1" | "rw"),
signal_mix(), autoplot(show_segments = TRUE),
the “no changepoints detected” print paths, and the
sd/breaks/model_param/lambda/threshold/G
arguments of the cpop, strucchange, DeCAFS, inspect and mosum wrappers.
All of them worked; none of them was guarded against a future refactor
(R45).expect_error(fpop_wrapper(X), "univariate") and the fpop
half of the scale-sensitivity note — so on a machine with no fpop they
met “Package ‘fpop’ is required” instead of the message under test,
which is an ERROR rather than a skip on CRAN’s noSuggests flavour. The
earlier _R_CHECK_DEPENDS_ONLY_ run had missed both because
the fallback library it used still exposed part of Suggests; the suite
is now verified against a library holding the Imports and nothing else.
Both assertions are guarded and the pelt half of each stayed unguarded,
so the cases that need no Suggests still run. A static sweep of every
test_that() block for a Suggests package used without a
matching guard found no others (R62).ggcpt_compare() and ggcpt_compare_table()
refuse a multi-column x instead of flattening it. Both run
univariate detectors but took as.numeric(x) on trust, so a
160x2 matrix was unrolled column after column and the join between the
columns read as a level shift: the table came back with changepoints at
80 and 160, and 160 is the seam, not a feature of either
series. Every wrapper already refused wide input through the same check;
these two entry points were the only ones that did not. Non-numeric
input now names the argument as well, rather than failing inside
as.numeric() with “cannot coerce type ‘object’ to vector of
type ‘double’”. The message points at cpt_batch(), which is
what runs a detector over a panel (R63).ggcpt_compare() hands future.apply a
documented future.seed value. It passed seed
straight through, and seed defaults to NULL,
which is not among the logical/integer/list values
future_lapply() documents — so every parallel comparison
run without an explicit seed was outside that contract. It now sends
TRUE in that case, asking for parallel-safe L’Ecuyer
streams, which is what cpt_batch() already did. Sequential
runs are unaffected. Found by exercising the parallel branch of both
functions for the first time: it is documented in three vignettes and
both help pages, and no test had ever set a non-sequential
future::plan(). The branch is otherwise correct — same
changepoints as the sequential path, series names preserved,
... forwarded, and the “which series failed” error still
named (R64).?strucchange_wrapper says how large its result is.
Measuring object.size() for every engine on one series
turned up a single outlier: a strucchange result is
quadratic in the series length, because breakpoints() keeps
RSS.triang, the triangular table of segment residual sums
of squares that lets it return the optimal segmentation for any number
of breaks without refitting. On a 3.2 kB series it comes to 1.7 MB at n
= 200, 5.9 MB at n = 400 and 22.6 MB at n = 800 — about four times
larger per doubling — and the table’s share of that grows from 85% to
95% over the same range. One fit is nothing; a few hundred from
cpt_batch() are, so the help now says to keep
$changepoints rather than the whole list of results.
Nothing changed in the object: this is the same size-versus-usefulness
trade-off already documented for ecp in the opposite
direction, and it was simply unstated. Every other engine is ordinary —
the median result across the other thirty is under ten times the size of
the series it was given (R65).cpt_methods() lists gfpop,
robust, focus and sbs with
status = "planned", but
cpt_detect(x, method = "gfpop") went to
match.arg(), whose message enumerates the thirty-one wired
methods — so it did not contain the name the user had just read out of
the table. The table said the name existed and the dispatcher said it
did not. It now reports what the method is waiting on and which package
it will be built on; an outright unknown name still gets the ordinary
list. In the same pass, sbs’s entry was out of date: it
said “when on CRAN”, but hdbinseg returned to CRAN as 1.0.3
in September 2025, so the only thing standing between sbs
and a user is the wrapper. gfpop was removed from CRAN and
robseg and FOCuS have never been on it, so
those three still read “when on CRAN” (R66).?cpt_detect gains a scale-sensitivity section, and the
README and the introduction vignette repeat it: pelt,
binseg, segneigh and fpop weigh
the penalty against a raw segment cost when detecting a change
in mean, because changepoint’s Normal cost fixes the noise
standard deviation at 1 and fpop’s lambda
penalises the residual sum of squares directly. Neither rescales the
data, so wider noise makes the penalty negligible and the segmentation
shatters — on one true changepoint with a five-sigma jump,
pelt returns 1 changepoint at sigma = 1, 29 at sigma = 3
and 138 at sigma = 10. The note gives the three remedies (standardise
the series, scale the penalty by the noise variance, or use
change_in = "meanvar") and records that every other engine
estimates or cancels the noise scale itself; both halves of it are
pinned by a test (R39). ?cpt_wrapper,
?fpop_wrapper and ?cpt_penalty point at it.
Behaviour is unchanged; the trap was simply undocumented, and the
package’s own examples all use unit-variance data, so nothing exposed
it.?cpt_detect, ?fpop_wrapper,
?cpop_wrapper and ?decafs_wrapper now record
that the dispatcher and those wrappers do not share a default penalty.
cpt_detect() resolves its "MBIC" default to a
numeric value that is stronger than the wrappers’ own
2 * log(n) — 19.9 against 11.8 at n = 360 — so
cpt_detect(x, method = "decafs") reports 3 changepoints
where decafs_wrapper(x) reports 5 on the same series. Both
defaults were documented individually; that they differ was not. Passing
penalty explicitly makes the two entry points agree
(R40).?npmojo_wrapper records that the engine calibrates its
detection threshold by bootstrap, so the value stored in the penalty
descriptor varies between runs unless set.seed() is called
first (or a manual threshold is passed through ...).?cpt_detect warns that a misspelt engine argument can
pass unnoticed. wbs, not, Rbeast,
strucchange, segmented and
fastcpd all end their own signature in ..., so
an unrecognised name forwarded through cpt_detect()’s
... is discarded upstream and the engine quietly uses its
default. Intercepting it here would risk rejecting arguments those
engines legitimately forward deeper, so the behaviour is unchanged and
documented instead.geom_cpt_segment() example that
could not run (it was given xintercept, but the geom needs
x/xend/y/yend);
DeCAFS and EnvCpt filed under multivariate
methods when both are univariate; is_ggcpt() demonstrated
on the input series rather than the result; a claim that only three
engine packages are required; and a method-family count that disagreed
between the package help, the README and the vignettes (all now six —
the feature-tour vignette was the last straggler and still said
five).ocd_wrapper() test uses mc_reps = 10
rather than 50. Those repetitions only calibrate the detection
threshold, and the change the test plants is far too large for the
calibration to matter — 10 reps give the same declaration as 50 and take
7 seconds instead of 36, cutting the whole test suite from 74 to 42
seconds with the assertions unchanged.ocd_wrapper() example runs in 3.6 seconds instead
of 20. It was by far the slowest example in the package —
ocd’s Monte Carlo threshold calibration scales with both
the number of coordinates and mc_reps — and a smaller,
cleaner problem (100x3 with mc_reps = 5) demonstrates the
wrapper better anyway: it reports one declaration just after the true
change, where the old example also produced a spurious second one.cpt_cite("tguh") but 2022 (50(5), 2721-2761)
in the vignette bibliography — the same paper with two sets of
coordinates; the bibliography is corrected to match, and its key renamed
accordingly. ?ecp_wrapper cited the arXiv preprint of the
ecp software paper while both vignettes cited its published form, so
inst/REFERENCES.bib now carries the Journal of Statistical
Software version (62(7), 1-25). A new test cross-validates all three
sources: shared BibTeX keys must describe the same publication, every
\insertRef key must resolve in
inst/REFERENCES.bib, and every @key cited in a
vignette must resolve in the vignette bibliography (R44).?stat_changepoint says which geoms actually work with
it. The stat emits one xintercept per changepoint and drops
x/y, so "vline" (the default) and
"rug" fit while "point" errors; the help
previously read as though any geom would do.?geom_cpt_ci no longer claims an x
aesthetic is required. The layer is a horizontal error bar, so it needs
y, xmin and xmax; x
is accepted but unnecessary, and neither of the package’s own call sites
(autoplot(show_ci = TRUE) and the feature-tour vignette)
supplies it, so the help contradicted the package’s own usage
(R43).?augment.ggcpt now says what the columns mean for a
multivariate result: every coordinate is returned and
seg_id/is_changepoint apply to the whole row,
but .fitted and .resid describe the first
coordinate only — the same one $segments$param_estimate
summarises.?cpt_penalty now
records the one silent substitution the dispatcher makes:
changepoint does not implement MBIC for Segment
Neighbourhood, so cpt_detect(method = "segneigh") falls
back to "SIC" on the default penalty and its result is
therefore not directly penalty-comparable with a PELT one.gfpop engine (never wrapped) has been
removed from it.vignette("ggchangepoint"))
walking the full exported surface, including the per-engine wrappers,
theme_ggcpt(), and annotate_segments().?ggchangepoint) was rewritten
to describe the unified ggcpt framework and the current
13-method engine list (it previously still claimed “only three
changepoint packages”).cpt_methods() introspection helper returning a
tibble of every known method, its engine, availability status, and
whether the engine is installed.ggcpt class:
summary(), as_tibble(),
as.data.frame(), format(), and
plot().cpt_penalty() gained a documented per-engine
penalty-semantics section.cpt_detect() no longer advertises 13 methods that
errored at runtime; match.arg() now enumerates only the
wired methods (B7).cpt_detect() validates method ×
change_in combinations and errors with a clear message
instead of silently mislabelling the result (B3).signal_blocks() now produces the correct Blocks signal;
the segment levels previously collapsed to a single step because the
assignment loop ran in reverse (B1).cpt_metrics() uses one-to-one matching, so
recall and f1 can no longer exceed 1 (B2), and
no longer warns on empty pred/truth (B6).ecp_wrapper() returns a correct per-coordinate
cp_value for matrix and data.frame input instead of a
column-major flattened scalar (B4); cpt_detect() no longer
flattens multivariate input before passing it to ecp.stat_changepoint() maps detected indices back to the
x aesthetic so rules land at the correct location on
non-1:n axes (B5).glance.ggcpt() now reports a measured
runtime and populates total_cost from the
underlying fit when available (B8).augment.ggcpt() renames data columns
position-independently, so it no longer breaks when the data carries
more than two columns (B11).cpt_simulate() @return now documents the
seg_id column it actually returns (B9), and the dead
show_segments parameter was removed from the internal plot
helper (B10).ggcpt S3 result class with tidy(),
glance(), augment(), and
autoplot() methodscpt_detect() unified dispatcher for changepoint
methodsgeom_changepoint(),
geom_cpt_segment(), geom_cpt_ci(),
stat_changepoint()ggcpt_compare() and
ggcpt_compare_table() for method comparisoncpt_metrics(),
cpt_metrics_annotated(), ggcpt_eval()cpt_simulate()/rcpt() and
canonical test signalscpt_penalty() helpertheme_ggcpt() and annotate_segments()
for plot customisationecp_wrapper() no-change bug fixed: spurious boundary
changepoints and NA no longer emittedsize → linewidth migration:
cptline_linewidth replaces deprecated
cptline_sizematch.arg() input validation added to all wrappers"_PACKAGE" sentinelchange_in = "np" alias added (keeps
"cpt_np" for backward compatibility)show_points
auto-off above 500 obsindex parameter for time-series axestestthat test suite with coverage for all new and
hardened functionscpt_wrapper(),
ecp_wrapper(), ggcptplot(),
ggecpplot().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.