Package: itsadug
Version: 2.4
Date: 2020-04-30
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 = "ctb"))
Author: Jacolien van Rij [aut, cre],
  Martijn Wieling [aut],
  R. Harald Baayen [aut],
  Hedderik van Rijn [ctb]
Maintainer: Jacolien van Rij <vanrij.jacolien@gmail.com>
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).
License: GPL (>= 2)
LazyData: true
Depends: R (>= 4.0), mgcv (>= 1.8), plotfunctions (>= 1.4)
VignetteBuilder: knitr
Suggests: knitr, xtable, sp, data.table
RoxygenNote: 7.1.0
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2020-04-28 13:33:46 UTC; jacolien
Repository: CRAN
Date/Publication: 2020-04-29 10:40:07 UTC
Built: R 4.0.5; ; 2022-04-21 06:54:49 UTC; windows
