Packaged: 2019-07-22 10:08:07 UTC; patrickenglish

Packaged: 2021-01-04 12:55:24 UTC; patrickenglish

Title: Interpreting Time Series and Autocorrelated Data Using GAMMs
Authors@R: c( person("Jacolien", "van Rij", email="vanrij.jacolien@gmail.com", role = c("aut", "cre")),person("Martijn","Wieling", role = "aut"), person("R. Harald","Baayen", role = "aut"), person("Hedderik", "van Rijn", role = "aut"))
Description: 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).
Maintainer: Jacolien van Rij <vanrij.jacolien@gmail.com>

Authors@R: c( person("Jacolien", "van Rij", email="vanrij.jacolien@gmail.com", role = c("aut", "cre")),
person("R. Harald","Baayen", role = "aut"),
person("Hedderik", "van Rijn", role = "ctb"))
Description: 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