Functions for Generating Restricted Permutations of Data
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.

c(person(given = "Gavin L.", family = "Simpson",
email = "ucfagls@gmail.com",
role = c("aut", "cph", "cre"),
comment = c(ORCID = "0000-0002-9084-8413")),
person(given = "R Core Team", role = "cph"),
person(given = "Douglas M.", family = "Bates", role = "ctb"),
person(given = "Jari", family = "Oksanen", role = "ctb"))
Gavin L. Simpson [aut, cph, cre]
(<https://orcid.org/0000-0002-9084-8413>),
R Core Team [cph],
Douglas M. Bates [ctb],
Jari Oksanen [ctb]

vegan (>= 2.0-0), testthat (>= 0.5), parallel, knitr,
rmarkdown, bookdown, sessioninfo

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.

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 effects begins and ends, and the results can be plotted in various
types of heatmap for reporting.
R (>= 2.10), plyr, lmPerm, ggplot2, viridis

doParallel, dplyr, tidyr, knitr, rmarkdown

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>.
buildmer (>= 1.7.1), car, doParallel, dplyr, ggplot2, lmPerm,
lme4, permuco, pbkrtest, tidyr, knitr, rmarkdown, viridis