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).
|Depends:||R (≥ 2.14.0), mgcv (≥ 1.8), plotfunctions (≥ 1.0)|
|Suggests:||knitr, xtable, sp, data.table|
|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)]|
|Citation:||itsadug citation info|
|CRAN checks:||itsadug results|
ACF: checking & handling autocorrelation
Visual inspection of GAMM models
Testing for significance
|Windows binaries:||r-devel: itsadug_2.2.zip, r-release: itsadug_2.2.zip, r-oldrel: itsadug_2.2.zip|
|OS X El Capitan binaries:||r-release: itsadug_2.2.tgz|
|OS X Mavericks binaries:||r-oldrel: itsadug_2.2.tgz|
|Old sources:||itsadug archive|
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