itsadug: Interpreting Time Series and Autocorrelated Data Using GAMMs

GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).

Version: 2.2
Depends: R (≥ 2.14.0), mgcv (≥ 1.8), plotfunctions (≥ 1.0)
Suggests: knitr, xtable, sp, data.table
Published: 2016-06-13
Author: Jacolien van Rij [aut, cre], Martijn Wieling [aut], R. Harald Baayen [aut], Hedderik van Rijn [ctb]
Maintainer: Jacolien van Rij <vanrij.jacolien at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: itsadug citation info
Materials: NEWS
CRAN checks: itsadug results

Downloads:

Reference manual: itsadug.pdf
Vignettes: ACF: checking & handling autocorrelation
Visual inspection of GAMM models
Testing for significance
Package source: itsadug_2.2.tar.gz
Windows binaries: r-devel: itsadug_2.2.zip, r-release: itsadug_2.2.zip, r-oldrel: itsadug_2.2.zip
OS X Mavericks binaries: r-release: itsadug_2.2.tgz, r-oldrel: itsadug_2.2.tgz
Old sources: itsadug archive

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