Geometrically-inspired multivariate changepoint wrapper (geomcp)

Wraps changepoint.geo::geomcp() (Grundy, Killick and Mihaylov, 2020): each multivariate observation is mapped to its distance from, and angle to, a reference point, and univariate PELT is run on the two mapped series. Distance changes capture shifts in magnitude, angle changes capture shifts in orientation/correlation structure.

Usage

geomcp_wrapper(
  x,
  penalty = "MBIC",
  mapping = c("both", "distance", "angle"),
  ...
)

Arguments

x

A numeric matrix or data frame with one row per time point.

penalty

Penalty for the univariate PELT runs (a changepoint-style character penalty). Defaults to "MBIC".

mapping

Which mapped series' changepoints to report: "both" (union, default), "distance", or "angle".

...

Additional arguments passed to changepoint.geo::geomcp().

Value

A ggcpt object whose changepoints tibble carries a mapping column ("distance" or "angle"; a location found in both is labelled "both").

References

Grundy T, Killick R, Mihaylov G (2020). “High-dimensional changepoint detection via a geometrically inspired mapping.” Statistics and Computing, 30, 1155–1166.

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(), 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 <- rbind(matrix(rnorm(100 * 4), 100), matrix(rnorm(100 * 4, 2), 100))
res <- geomcp_wrapper(X)
res$changepoints
#> # A tibble: 2 × 3
#>      cp cp_value mapping 
#>   <int>    <dbl> <chr>   
#> 1    99   -1.08  angle   
#> 2   100    0.369 distance