Enables the automation of actions across the pipeline, including 
  initial steps of transforming binocular data and gap repair to event-based 
  processing such as fixations, saccades, and entry/duration in Areas of 
  Interest (AOIs). It also offers visualisation of eye movement and AOI 
  entries. These tools take relatively raw (trial, time, x, and y form) data 
  and can be used to return fixations, saccades, and AOI entries and time spent
  in AOIs. As the tools rely on this basic data format, the functions can work 
  with data from any eye tracking device. Implements fixation and saccade 
  detection using methods proposed by Salvucci and Goldberg (2000) 
  <doi:10.1145/355017.355028>.

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.