Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).

Conduct permutation One-Way or Two-Way Analysis of Variance in R. Use different permutation types for two-way designs.

Manipulates invertible functions from a finite set to
             itself.  Can transform from word form to cycle form and
             back.  To cite the package in publications please use
             Hankin (2020) "Introducing the permutations R package",
             SoftwareX, volume 11 <doi:10.1016/j.softx.2020.100453>.

A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.

Helps you determine the analysis window to use when analyzing densely-sampled
    time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007)
    <doi:10.1016/j.jneumeth.2007.03.024>. These permutation tests can help identify the timepoints
    where significance of an effect begins and ends, and the results can be plotted in various
    types of heatmap for reporting. Mixed-effects models are supported using an implementation of
    the approach by Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.