BEAST wrapper — Bayesian estimation of abrupt change, seasonality, and trend

Wraps Rbeast::beast() (Zhao et al., 2019), a Bayesian model-averaging ensemble that estimates the number and location of trend changepoints together with their posterior occurrence probabilities. Locations whose posterior probability reaches prob_threshold are reported; the probability profile renders via ggcpt_posterior().

Usage

beast_wrapper(x, prob_threshold = 0.5, seed = NULL, ...)

Arguments

x: A numeric vector (treated as a non-seasonal series).
prob_threshold: Posterior probability cutoff in \((0, 1)\) above which a candidate trend changepoint is reported. Defaults to 0.5.
seed: Optional seed for the engine's MCMC sampler (passed to Rbeast::beast() as mcmc.seed).
...: Additional arguments passed to Rbeast::beast().

Value

A ggcpt object. The changepoints tibble carries posterior_prob, and the data tibble carries the posterior mean trend in its fitted column.

References

Zhao K, Wulder MA, Hu T, Bright R, Wu Q, Qin H, Li Y, Toman E, Mallick B, Zhang X, Brown M (2019). “Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm.” Remote Sensing of Environment, 232, 111181.

Examples

res <- beast_wrapper(c(rnorm(60), rnorm(60, 4)), seed = 2026)
res$changepoints
#> # A tibble: 1 × 3
#>      cp cp_value posterior_prob
#>   <int>    <dbl>          <dbl>
#> 1    60    -1.79              1