Functions to prepare and analyse eye tracking data of reading
  exercises. The functions allow some basic data preparations and code fixations 
  as first and second pass. First passes can be further devided into forward and 
  reading. The package further allows for aggregating fixation times per AOI or 
  per AOI and per type of pass (first forward, first rereading, second). These 
  methods are based on Hyönä, Lorch, and Rinck (2003) <doi:10.1016/B978-044451020-4/50018-9> 
  and Hyönä, and Lorch (2004) <doi:10.1016/j.learninstruc.2004.01.001>. It is 
  also possible to convert between metric length and visual degrees.

Addresses tasks along the pipeline from raw
    data to analysis and visualization for eye-tracking data. Offers several
    popular types of analyses, including linear and growth curve time analyses,
    onset-contingent reaction time analyses, as well as several non-parametric
    bootstrapping approaches. For references to the approach see Mirman, 
    Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and
    Barr (2008) <doi:10.1016/j.jml.2007.09.002>.

A set of functions for organising and analysing datasets from
    experiments run using 'Eyelink' eye-trackers. Organising functions help
    to clean and prepare eye-tracking datasets for analysis, and mark up
    key events such as display changes and responses made by participants.
    Analysing functions help to create means for a wide range of standard
    measures (such as 'mean fixation durations'), which can then be fed into
    the appropriate statistical analyses and graphing packages as necessary.

Facilitates easy analysis of factorial experiments, including
    purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss
    designs, and mixed within-and-between-Ss designs. The functions in this package
    aim to provide simple, intuitive and consistent specification of data analysis
    and visualization. Visualization functions also include design visualization for
    pre-analysis data auditing, and correlation matrix visualization. Finally, this
    package includes functions for non-parametric analysis, including permutation
    tests and bootstrap resampling. The bootstrap function obtains predictions
    either by cell means or by more advanced/powerful mixed effects models, yielding
    predictions and confidence intervals that may be easily visualized at any level
    of the experiment's design.