kollaR-package: Filtering, Visualization, and Analysis of Eye Tracking Data

In kollaR: Filtering, Visualization and Analysis of Eye Tracking Data

Filtering, Visualization, and Analysis of Eye Tracking Data

Description

Functions for analyzing eye-tracking data, including fixation filtering/event detection (I-VT, I-DT, and two-means clustering), visualizations, and area of interest (AOI) based analyses. See separate documentation for each function. Make sure it works with your data. The principles underlying I-VT and I-DT filters are described in Salvucci & Goldberg (2000,\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1145/355017.355028")}). Two-means clustering is described in Hessels et al. (2017, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-016-0822-1")}).

Details

Overview of functions: Pre-processing (smoothing, interpolation, downsampling):'process_gaze', 'downsample_gaze'

Fixation and Saccade Detection (Fixation Filters)**: 'ivt_filter', 'idt_filter', 'cluster2m'

Visualization of Output from Fixation Filter and Preprocessing Algorithms: 'filt_plot_temporal', 'filt_plot_2d', 'plot_velocity_profiles', 'plot_sample_velocity', 'plot_filter_results'

Visualization of Gaze Data: 'static_plot', 'animated_fixation_plot'

AOI Based Analyses: 'draw_aoi', 'aoi_test'

Author(s)

Johan Lundin Kleberg [e-mail]

kollaR documentation built on April 13, 2025, 5:11 p.m.