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.

A dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects.
    Johnson WE & Li C (2007)  <doi:10.1093/biostatistics/kxj037> "Adjusting batch effects in microarray expression data using empirical Bayes methods."
    Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnes M, Phillips ML, Trivedi MH, Weissman MM, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.11.024> "Harmonization of cortical thickness measurements across scanners and sites."
    Fortin J-P, Parker D, Tun<e7> B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.08.047> "Harmonization of multi-site diffusion tensor imaging data."