Apply block-bootstrapping procedure to module input — .block

.block_bootstrap returns a block-bootstrapped replicate of the incidence. Incidence should be a vector of non-negative values

.block_bootstrap(
  incidence_input,
  round_incidence = TRUE,
  smoothing_method = "LOESS",
  ...
)

Arguments

incidence_input	Module input object. List with two elements: A numeric vector named `values`: the incidence recorded on consecutive time steps. An integer named `index_offset`: the offset, counted in number of time steps, by which the first value in `values` is shifted compared to a reference time step This parameter allows one to keep track of the date of the first value in `values` without needing to carry a `date` column around. A positive offset means `values` are delayed in the future compared to the reference values. A negative offset means the opposite.
round_incidence	boolean. If `TRUE`, the bootstrapped incidence is rounded to the nearest integer.
smoothing_method	string. Method used to smooth the original incidence data. Available options are: 'LOESS', implemented in `.smooth_LOESS`
...	Arguments passed on to `.block_bootstrap_overlap_func`, `smooth_incidence` `block_size` integer. Size of a bootstrapping block. `keep_weekdays_aligned` boolean. Set to `FALSE` if not daily incidence, or if no weekly noise pattern that would require to apply errors to the same day of the week as they were in the original data.

a module output object. bootstrapped incidence.

This function works by resampling blocks of differences (on the log-scale) between the original data and a smoothed version of the original data.