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>.">

permutes: Permutation Tests for Time Series Data (original) (raw)

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: 2.8
Depends: R (≥ 2.10)
Imports: plyr, stats, utils
Suggests: buildmer (≥ 2.3), car, doParallel, ggplot2, glmmTMB, knitr, lme4, lmPerm, permuco, rmarkdown, viridis
Published: 2023-09-28
DOI: 10.32614/CRAN.package.permutes
Author: Cesko C. Voeten [aut, cre]
Maintainer: Cesko C. Voeten
BugReports: https://gitlab.com/cvoeten/permutes/-/issues
License: FreeBSD
NeedsCompilation: no
CRAN checks: permutes results

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