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.

A complete rewrite and reimagining of 'bakR' (see 'Vock et al.' (2025) <doi:10.1371/journal.pcbi.1013179>).
  Designed to support a wide array of analyses of nucleotide recoding RNA-seq (NR-seq) datasets of any
	type, including TimeLapse-seq/SLAM-seq/TUC-seq, Start-TimeLapse-seq (STL-seq),
	TT-TimeLapse-seq (TT-TL-seq), and subcellular NR-seq. 'EZbakR' extends
	standard NR-seq standard NR-seq mutational modeling to support multi-label analyses
	(e.g., 4sU and 6sG dual labeling), and implements an improved hierarchical model to better
	account for transcript-to-transcript variance in metabolic label incorporation.
	'EZbakR' also generalized dynamical systems modeling of NR-seq data to support analyses
	of premature mRNA processing and flow between subcellular compartments. Finally,
	'EZbakR' implements flexible and well-powered comparative analyses of all
	estimated parameters via design matrix-specified generalized linear modeling.