permutes: Permutation Tests for Time Series Data

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>.

Version: 2.2
Depends: R (≥ 2.10)
Imports: plyr
Suggests: buildmer (≥ 1.7.1), car, doParallel, dplyr, ggplot2, lmPerm, lme4, permuco, knitr, rmarkdown, viridis
Published: 2021-10-13
Author: Cesko C. Voeten [aut, cre]
Maintainer: Cesko C. Voeten <cvoeten at gmail.com>
BugReports: https://github.com/cvoeten/permutes/issues
License: FreeBSD
NeedsCompilation: no
CRAN checks: permutes results

Documentation:

Reference manual: permutes.pdf
Vignettes: Analyzing time series data using 'clusterperm.lmer'
Analyzing time series data using 'permu.test'

Downloads:

Package source: permutes_2.2.tar.gz
Windows binaries: r-devel: permutes_2.2.zip, r-release: permutes_2.2.zip, r-oldrel: permutes_2.2.zip
macOS binaries: r-release (arm64): permutes_2.1.1.tgz, r-release (x86_64): permutes_2.2.tgz, r-oldrel: permutes_2.2.tgz
Old sources: permutes archive

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