Imports plain-text ASC data files from EyeLink eye trackers 
    into (relatively) tidy data frames for analysis and visualization.

Import gaze data from  edf files generated by 
  the SR Research <https://www.sr-research.com/> EyeLink eye tracker. Gaze data,
  both recorded events and samples, is imported per trial. The package allows to extract
  events of interest, such as saccades, blinks, etc. as well as
  recorded variables and custom events (areas of interest, triggers) into separate tables.
  The package requires EDF API library that can be obtained at <https://www.sr-research.com/support/>.

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.

Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) <doi:10.1038/s41467-020-17851-9>; Kret & Sjak-Shie (2018) <doi:10.3758/s13428-018-1075-y>; Strauch (2024) <doi:10.1016/j.tins.2024.06.002>]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) <doi:10.1038/sdata.2016.18>]. 'eyeris' provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) <doi:10.1038/s41592-018-0235-4>; Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44>]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology.