Wraps the fpop package for optimal changepoint detection via
functional pruning.
Arguments
- x
A numeric vector.
- penalty
Penalty value. Defaults to
2 * log(length(x))(BIC). This is an absolute penalty on the residual sum of squares, so it is only calibrated for noise of standard deviation 1: on wider data the default under-penalises badly and the segmentation shatters. Standardise the series, or scale the penalty by the noise variance (for example2 * log(length(x)) * stats::var(diff(x)) / 2). See the scale-sensitivity section ofcpt_detect. This default differs from the onecpt_detectapplies, which resolves its"MBIC"default to a stronger numeric value, so the two entry points need not agree unlesspenaltyis given.- ...
Additional arguments passed to
fpop::Fpop().
References
Maidstone R, Hocking T, Rigaill G, Fearnhead P (2017). “On Optimal Multiple Changepoint Algorithms for Large Data.” Statistics and Computing, 27(2), 519–533. doi:10.1007/s11222-016-9636-3 .
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(),
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(),
taylor_wrapper(),
tguh_wrapper(),
trend_wrapper(),
var_wrapper(),
wbs2_wrapper(),
wbs_wrapper(),
wbsts_wrapper()
Examples
set.seed(2026)
x <- c(rnorm(60), rnorm(60, 4))
fpop_wrapper(x)$changepoints
#> # A tibble: 1 × 2
#> cp cp_value
#> <int> <dbl>
#> 1 60 -0.999
# the default penalty assumes sd 1; scale it for wider noise
y <- 5 * x
fpop_wrapper(y, penalty = log(length(y)) * var(diff(y)))$changepoints
#> # A tibble: 1 × 2
#> cp cp_value
#> <int> <dbl>
#> 1 60 -5.00