[R-pkgs] permuco: permutation tests and multiple comparisons.
We present you the permuco package, which has 2 main purposes: PERmutation tests and MUltiple COmparisons.
First, the package has functions for permutation tests for parameters in linear models with nuisance variables. Several permutation methods exist in the literature to reduce the effect of nuisance variables and the permuco package allows to use them for t statistics in regression model, F tests in type 3 ANOVA, and F tests in repeated measures ANOVA. The 2 functions aovperm() and lmperm() perform these tests and their usage is similar to the parametric counterpart aov() and lm().
Secondly, it uses multiple comparisons procedures with permutation tests to compare signals time by time. This problem is common when analysing electroencephalography (or fMRI) data where the response variables are EEG signals recorded for each experimental setting. In these experiments, the number of tests is equal to the number of time points of the signal which is typically in the thousands. The permuco package implements the state-of-the-art multiple comparisons procedures which uses permutations, like the cluster-mass test or the threshold-free cluster-enhancement. These procedures are powerful when the effects appear in clusters and they will control the family wise error rate (FWER) under the null hypothesis.
All these procedures are explained and exemplified in detail in a vignette.
We are waiting for your feedback in order to improve the next releases of permuco.