Event Classification, Visualization and Analysis of Eye Tracking Data
Adjust the onset and offset of fixations to avoid misclassification of...
Adaptive velocity-based algorithm for saccade and fixation detection
Fixation detection by two-means clustering
Dispersion-based fixation detection algorithm (I-DT)
I-VT algorithm for fixation and saccade detection
Create GIF animation of fixations on a stimulus images
Test whether a gaze coordinates are within or outside a rectangular or...
Calculate sample-to-sample root mean square distance (RMS) of a data s...
Fixation detection by two-means clustering
Downsample gaze
Draw one or more areas of interest, AOIs, on a stimulus image and save...
Plot fixations vs. individual sample coordinates in 2D space. In the c...
Plot fixation filtered vs. raw gaze coordinates. This function will be...
Find transition weights for each sample in a gaze matrix.
Find subsequent periods in a vector with values below a threshold. Use...
Plot fixations vs. individual sample coordinates in 2D space.
Plot fixation classified vs. raw gaze coordinates
Plot fixation classified vs. raw gaze coordinate time series
Dispersion-based fixation detection algorithm (I-DT)
Interpolate over gaps (subsequent NAs) in vector.
I-VT algorithm for fixation and saccade detection
Fixation and Saccade Detection, Visualization, and Analysis of Eye Tra...
Merge adjacent fixations
Calculate the moving mean of a vector
Plot vdescriptives one or more fixation detection algorithms
Plot descriptives from one or more fixation detection algorithms
Plot the sample-to-sample velocity of eye tracking data.
Create ggplot of saccade velocity profiles
Interpolation and smoothing of gaze-vector
Interpolation and smoothing of gaze-vector. This function will be repl...
Sample-to-sample raw and fixation classified data from 1 individual
Fixations from 1 individual
Fixations from 7 individuals
Pre-processed sample-by-sample example data
Saccades from 3 individuals
Unprocessed sample-by-sample example data
Plot fixations in 2D space overlaied on a stimulus image
Data-driven identification of threshold parameters for adaptive veloct...
Summarize fixation statistics
Adjust the onset and offset of fixations to avoid misclassification of...
Functions for analysing eye tracking data, including event detection, visualizations and area of interest (AOI) based analyses. The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms. See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in Salvucci & Goldberg (2000,\doi{10.1145/355017.355028}). Two-means clustering is described in Hessels et al. (2017, \doi{10.3758/s13428-016-0822-1}). The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010,\doi{10.3758/BRM.42.1.188}). See a demonstration in the URL.