Permutation Tests for Nonparametric Statistics Using R

Permutation tests for nonparametric detection

In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman-Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the ...

Nonparametric Permutation Tests for Functional Neuroimaging

Permutation tests for linear models in R

An R package which uses permutation tests to obtain p-values for linear models. Standard R linear model functions have been modified to produce p-values obtained from permutation tests instead of from normal theory. These values are advantageous when the degrees of freedom for error is small or non-existent, as is the case with saturated experimental designs, or when the data is drawn from a no...

Nonparametric Survival Analysis: Cox-Mantel tests and Permutation tests

Nonparametric permutation tests for functional neuroimaging: a primer with examples.

Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts ...