`bootstrap()`

generate bootstrap estimations of an event.`jackknife()`

generate jackknife estimations of an event.

## Arguments

- object
- level
A length-one

`numeric`

vector giving the confidence level.- progress
A

`logical`

scalar: should a progress bar be displayed?- ...
Further arguments to be passed to internal methods.

- probs
A

`numeric`

vector of probabilities with values in \([0,1]\).- n
A non-negative

`integer`

specifying the number of bootstrap replications.

## Value

A `data.frame`

.

## Details

If `jackknife()`

is used, one type/fabric is removed at a
time and all statistics are recalculated. In this way, one can assess
whether certain type/fabric has a substantial influence on the date
estimate.
A three columns `data.frame`

is returned, giving the results of
the resampling procedure (jackknifing fabrics) for each assemblage (in rows)
with the following columns:

`mean`

The jackknife mean (event date).

`bias`

The jackknife estimate of bias.

`error`

The standard error of predicted means.

If `bootstrap()`

is used, a large number of new bootstrap assemblages is
created, with the same sample size, by resampling each of the original
assemblage with replacement. Then, examination of the bootstrap statistics
makes it possible to pinpoint assemblages that require further
investigation.

A five columns `data.frame`

is returned, giving the bootstrap
distribution statistics for each replicated assemblage (in rows)
with the following columns:

`min`

Minimum value.

`mean`

Mean value (event date).

`max`

Maximum value.

`Q5`

Sample quantile to 0.05 probability.

`Q95`

Sample quantile to 0.95 probability.

## See also

Other resampling methods:
`resample_mcd`