Package: eyetools 0.10.0

eyetools: Analyse Eye Data

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>.

Authors:Tom Beesley [aut, cre], Matthew Ivory [aut]

eyetools_0.10.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
eyetools/json (API)

# Install 'eyetools' in R:

install.packages('eyetools', repos = c('https://tombeesley.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tombeesley/eyetools/issues

Pkgdown/docs site:https://tombeesley.github.io

Datasets:

HCL - Example dataset from that contains binocular eye data from two participants from a simple contingency learning task (the data are from Beesley, Nguyen, Pearson, & Le Pelley, 2015). In this task there are two stimuli that appear simultaneously on each trial (to the left and right of the screen). Participants look at these cues and then make a decision by selecting an "outcome response" button. Data was recorded with a Tobii TX300 sampling at 300Hz. Participant sat approximately 60 cm from the screen. The screen resolution was 1920 x 1080.
HCL_AOIs - Example AOIs for use with HCL
HCL_behavioural - Example dataset of behavioural data to complement dataset HCL.

On CRAN:

areas-of-interest attention-visualization cognitive-science dwell-time-algorithm eye-tracker eye-tracking eyetracking ggplot2 psychology psychology-experiments saccades tobii tobii-eye-tracker visualization

5.64 score 9 stars 24 scripts 447 downloads 22 exports 40 dependencies

Last updated from:812a2dc7fb. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	186
source / vignettes	OK	243
linux-release-x86_64	OK	167
macos-release-arm64	OK	98
macos-oldrel-arm64	OK	102
windows-devel	OK	140
windows-release	OK	132
windows-oldrel	OK	133
wasm-release	OK	128

Exports:AOI_seq AOI_time AOI_time_binned ASC_to_df combine_eyes compare_algorithms conditional_transform create_AOI_df dist_to_visual_angle fixation_dispersion fixation_VTI hdf5_get_event hdf5_to_df interpolate plot_AOI_growth plot_heatmap plot_seq plot_spatial saccade_VTI smoother tracker_properties_get tracker_properties_set

Dependencies:abind base64enc bit bit64 cli cpp11 curl farver ggforce ggplot2 ggrepel glue gridExtra gtable hdf5r isoband jsonlite labeling lattice lifecycle magick magrittr MASS pbapply png polyclip R6 RColorBrewer Rcpp rlang S7 scales systemfonts tidyselect tweenr vctrs viridis viridisLite withr zoo

An introduction to eyetools

Rendered fromeyetools.Rmdusingknitr::rmarkdown

Last update: 2026-07-17
Started: 2024-09-26

Help page	Topics
Sequence analysis of area of interest entries	AOI_seq
Analysis of time spent in areas of interest	AOI_time
Binned time analysis of area of interest entries	AOI_time_binned
Read Eyelink ASC Sample Data	ASC_to_df
Combine binocular data into single X/Y coordinate pairs	combine_eyes
A battery of metrics and plots to compare the two algorithms (dispersion and VTI)	compare_algorithms
conditional_transform	conditional_transform
Create a blank data frame for populating with AOIs	create_AOI_df
Compute visual angle from distance metrics	dist_to_visual_angle
Fixation detection using a dispersion method	fixation_dispersion
Fixation detection using a velocity threshold identification method	fixation_VTI
Example dataset from that contains binocular eye data from two participants from a simple contingency learning task (the data are from Beesley, Nguyen, Pearson, & Le Pelley, 2015). In this task there are two stimuli that appear simultaneously on each trial (to the left and right of the screen). Participants look at these cues and then make a decision by selecting an "outcome response" button. Data was recorded with a Tobii TX300 sampling at 300Hz. Participant sat approximately 60 cm from the screen. The screen resolution was 1920 x 1080.	HCL
Example AOIs for use with HCL	HCL_AOIs
Example dataset of behavioural data to complement dataset HCL.	HCL_behavioural
Get messages stored in TOBII-generated HDF5 files	hdf5_get_event
Convert TOBII-generated HDF5 files to dataframe	hdf5_to_df
Interpolation of missing data (NAs)	interpolate
Plots absolute or proportional time spent in AOIs over time	plot_AOI_growth
Plot heatmap of raw data	plot_heatmap
Plot of raw data over time	plot_seq
Plot raw data and fixations	plot_spatial
Velocity threshold identification of saccades	saccade_VTI
Smoothing of raw data	smoother
Get Current Eyetracker Properties	tracker_properties_get
Set the properties of the eye-tracker that was used in recording the data	tracker_properties_set

Package: eyetools 0.10.0

eyetools: Analyse Eye Data

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)