tutorials
Most of the experimental data I work with are time series data, such as EEG data, gaze data, or pupil dilation. For these analysis I prefer Generalized Additive Mixed Modeling (GAMM) as implemented in the R package mgcv (Wood, 2006; 2011), which is a nonlinear regression method.
tutorials
 Overview of useful commands in R. For beginning R users. (Under development)
 Using R Markdown. Quick, basic tutorial on how to use R Markdown for generating analysis reports. Overview GAMM.
 Short overview of a GAMM analysis of time series data.
 Generating animations. Examples on how to include animations in R Markdown reports.
R packages
itsadug
We've combined several functions that facilitate the statistical analysis with GAMMs in the package itsadug
, which is available on CRAN. The package includes functions for model comparisons, visualization of nonlinear interactions, and inspection of the residuals.
Version 2.3 of itsadug
is available on CRAN. Below the code for installing the newest version of itsadug (version 2.3) directly.
citation:
Jacolien van Rij, Martijn Wieling, R. Harald Baayen, & Hedderik van Rijn (2017). itsadug: Interpreting Time Series and Autocorrelated Data Using GAMMs. R package version 2.3.
how to install:

If you are interested to try the package, you can use the package installer in R, or run the following code in R to install the package:
install.packages("itsadug")
 Alternatively, the newest version of the package can be downloaded or directly installed from my website. Run the following code in R:
# first install the package plotfunctions (necessary for itsadug):
install.packages("http://www.jacolienvanrij.com/itsadug/package/plotfunctions_1.3.tar.gz", repos=NULL, type="source")
# then install itsadug in the same way:
install.packages("http://www.jacolienvanrij.com/itsadug/package/itsadug_2.3.tar.gz", repos=NULL, type="source")
plotfunctions
The package plotfunctions
implements various functions that facilitate visualization of data and analysis results. The functions in the package are relatively simple and are intended to facilitate incremental building of graphs using the standard R plot functions (e.g., plot, lines, points
) An overview of most functions can be found in the vignette (or see vignette("plotfunctions")
).