EnvCpt wrapper — changepoints versus trends versus autocorrelation

Wraps EnvCpt::envcpt() (Beaulieu and Killick, 2018), which fits up to twelve competing models — constant mean or linear trend, each with or without changepoints, and with white-noise, AR(1) or AR(2) errors — and lets an information criterion decide whether the series really contains changepoints or merely trend/autocorrelation ("memory"). The changepoints of the winning model (if any) are returned, and the winning model's name is recorded, guarding against the classic false positive of running a mean-shift detector on autocorrelated data.

Usage

envcpt_wrapper(
  x,
  models = c("mean", "meancpt", "meanar1", "meanar2", "meanar1cpt", "meanar2cpt",
    "trend", "trendcpt", "trendar1", "trendar2", "trendar1cpt", "trendar2cpt"),
  criterion = c("AIC", "BIC"),
  minseglen = 5,
  ...
)

Arguments

x

A numeric vector.

models

Character vector of models to fit; see EnvCpt::envcpt(). Defaults to all twelve.

criterion

Model selection criterion: "AIC" (default) or "BIC".

minseglen

Minimum segment length. Defaults to 5.

...

Additional arguments passed to EnvCpt::envcpt().

Value

A ggcpt object. $fit holds the full envcpt output; the selected model name is stored in the penalty descriptor and printed by glance() via penalty_type.

References

Beaulieu C, Killick R (2018). “Distinguishing trends and shifts from memory in climate data.” Journal of Climate, 31(23), 9519–9543.

Examples

set.seed(2026)
res <- envcpt_wrapper(c(rnorm(100), rnorm(100, 3)))
res$changepoints
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1    98   -0.490