Wraps ChangePointTaylor::change_point_analyzer(): the
bootstrap-and-recursion procedure of Wayne Taylor that the quality-control
and Six Sigma community uses as its default. Each candidate is scored by
the bootstrap probability that a change occurred there, which gives a
confidence level per changepoint and a confidence interval for its
location — both carried onto the result.
Usage
taylor_wrapper(
x,
n_bootstraps = 1000,
min_candidate_conf = 0.5,
min_conf = 0.9,
conf_level = 0.95,
seed = NULL
)Arguments
- x
A numeric vector.
- n_bootstraps
Bootstrap samples per candidate. Defaults to
1000; the engine accepts 100 to 1,000,000.- min_candidate_conf
Minimum confidence for a candidate to be considered. Defaults to
0.5.- min_conf
Minimum confidence for a changepoint to be reported. Defaults to
0.9.- conf_level
Confidence level of the reported location intervals. Defaults to
0.95.- seed
Optional seed (the procedure is bootstrap-based).
Value
A ggcpt object with ci_lower/ci_upper (so
autoplot(show_ci = TRUE) works) and a confidence column.
References
Taylor WA (2000). Change-Point Analysis: A Powerful New Tool for Detecting Changes. Taylor Enterprises, Libertyville, Illinois.
See also
Other changepoint engines:
bcp_wrapper(),
beast_wrapper(),
bfast_wrapper(),
binsegrcpp_wrapper(),
bocpd_wrapper(),
cpm_wrapper(),
cpop_wrapper(),
cpt_wrapper(),
decafs_wrapper(),
ecp_wrapper(),
envcpt_wrapper(),
esac_wrapper(),
fabisearch_wrapper(),
fastcpd_wrapper(),
fcov_wrapper(),
fmean_wrapper(),
fpop_wrapper(),
geomcp_wrapper(),
hdcov_wrapper(),
hdreg_wrapper(),
idetect_wrapper(),
inspect_wrapper(),
kcp_wrapper(),
kwc_wrapper(),
mcp_wrapper(),
mosum_wrapper(),
network_wrapper(),
not_wrapper(),
npmojo_wrapper(),
nsp_wrapper(),
ocd_wrapper(),
pilliat_wrapper(),
segmented_wrapper(),
smuce_wrapper(),
sn_wrapper(),
strucchange_wrapper(),
tguh_wrapper(),
trend_wrapper(),
var_wrapper(),
wbs2_wrapper(),
wbs_wrapper(),
wbsts_wrapper()
Examples
set.seed(2026)
taylor_wrapper(c(rnorm(60), rnorm(60, 3)), n_bootstraps = 200, seed = 1)
#> ggcpt (changepoint detection result)
#> Method: taylor
#> Change in: mean
#> Changepoints found: 2
#> CP convention: left
#> Penalty: confidence = 0.9
#> Series length: 120
#>
#> Changepoints:
#> # A tibble: 2 × 5
#> cp cp_value ci_lower ci_upper confidence
#> <int> <dbl> <int> <int> <dbl>
#> 1 15 -2.55 6 50 1
#> 2 60 -0.999 59 61 1