[R-pkgs] {CIPerm}: Computationally-Efficient Confidence Intervals for Mean Shift from Permutation Methods

Jerzy Wieczorek j@w|eczo @end|ng |rom co|by@edu
Wed Jun 22 03:16:43 CEST 2022

Dear R users,

Emily Tupaj and I recently released a new package on CRAN:

{CIPerm}: Computationally-Efficient Confidence Intervals
for Mean Shift from Permutation Methods

It is straightforward to carry out a permutation or randomization test of
H_0: mu_A - mu_B = 0
but the "naive" approach to getting a confidence interval for the mean
shift parameter
(mu_A - mu_B)
is more computationally-intensive. In this "naive" approach we would test
H_0: mu_A - mu_B = delta
for many values of delta, and let our CI be the delta values where we
did not reject H_0. This requires running a new permutation test for
each candidate value of delta.

Our contribution:
Instead of the naive approach, our R package implements an exact
method derived by Minh Nguyen (2009) to calculate such confidence
interval endpoints from a single set of permutations. Thus, it is no
more computationally expensive than a single standard permutation

Please see our package's README and `nguyen` vignette for examples and
a brief overview of the method:

Our `naive` vignette contains timing tests to compare the
computational speeds of Nguyen's method against the naive approach.
For large datasets, Nguyen's method is substantially faster:

For details on the methodology, please see:
Nguyen, M.D. (2009). "Nonparametric Inference using Randomization and
Permutation Reference Distribution and their Monte-Carlo
Approximation" [unpublished MS thesis; Mara Tableman, advisor],
Portland State University. Dissertations and Theses. Paper 5927.

Feedback is welcome by email or at:

Best wishes,
Jerzy Wieczorek

Jerzy Wieczorek
Assistant Professor
Department of Statistics
Colby College
5841 Mayflower Hill
Waterville, ME 04901
jawieczo using colby.edu

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