Jackknife resampling and estimation in Julia
15 Stars
Updated Last
2 Years Ago
Started In
July 2016


Travis Coveralls

This package provides jackknife resampling and estimation functions for Julia.


None of the functions here are exported, so you'll have to call them with the prefix Jackknife. or explicitly import them.

Each function takes the following two arguments:

  • A point estimator, given as a Function. The function must return a scalar when passed a vector.

  • A real-valued vector of length > 1.


leaveoneout(estimator, x)

Compute a vector of point estimates based on systematic subsamples of x wherein each index is omitted one at a time. These are the "leave-one-out" estimates. The resulting vector will have length length(x) - 1.


variance(estimator, x)

The variance of the estimator computed using the jackknife technique.

bias(estimator, x)

The bias of the estimator computed using the jackknife technique.

estimate(estimator, x)

The bias-corrected jackknife estimate of the parameter.