Package: eyetrackingR 0.2.2

eyetrackingR: Eye-Tracking Data Analysis

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>.

Authors:Samuel Forbes [aut, cre], Jacob Dink [aut], Brock Ferguson [aut]

eyetrackingR_0.2.2.tar.gz
eyetrackingR_0.2.2.zip(r-4.7-any)eyetrackingR_0.2.2.zip(r-4.6-any)eyetrackingR_0.2.2.zip(r-4.5-any)
eyetrackingR_0.2.2.tgz(r-4.6-any)eyetrackingR_0.2.2.tgz(r-4.5-any)
eyetrackingR_0.2.2.tar.gz(r-4.7-any)eyetrackingR_0.2.2.tar.gz(r-4.6-any)
eyetrackingR_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
eyetrackingR/json (API)

# Install 'eyetrackingR' in R:
install.packages('eyetrackingR', repos = c('https://samhforbes.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/samhforbes/eyetrackingr/issues

Datasets:

On CRAN:

Conda:

7.95 score 26 stars 65 scripts 547 downloads 7 mentions 18 exports 45 dependencies

Last updated from:b6ba4aef82. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE205
source / vignettesOK311
linux-releaseNOTE184
macos-releaseNOTE255
macos-oldrelNOTE249
windows-develNOTE156
windows-releaseNOTE173
windows-oldrelNOTE144
wasm-releaseOK110

Exports:add_aoianalyze_boot_splinesanalyze_time_binsanalyze_time_clustersclean_by_tracklossdescribe_dataget_time_clustersmake_boot_splines_datamake_eyetrackingr_datamake_onset_datamake_switch_datamake_time_cluster_datamake_time_sequence_datamake_time_window_datareclasssimulate_eyetrackingr_datasubset_by_windowtrackloss_analysis

Dependencies:backportsbroombroom.mixedclicodacodetoolscpp11digestdplyrfarverforcatsfurrrfuturegenericsggplot2globalsgluegtableisobandlabelinglatticelazyevallifecyclelistenvmagrittrnlmeparallellypillarpkgconfigpurrrR6RColorBrewerrlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrzoo

Performing a window analysis using eyetrackingR
Overview of this vignette | Prerequisites | Sanity checks | Performing a simple paired t-test | Using mixed-effects models | Interpreting the estimates | Adding additional predictors | Additional information | A word of caution about window analyses | References
Rendered fromwindow_analysis_vignette.Rmdusingknitr::rmarkdown

Last update: 2025-06-16
Started: 2015-10-07

Estimating time windows of divergence
Overview of this vignette | Prerequisites | Testing Time-Bins Independently | Bootstrapped smoothed divergence analysis | Bootstrapped cluster-based permutation analysis | References
Rendered fromdivergence_vignette.Rmdusingknitr::rmarkdown

Last update: 2025-06-03
Started: 2015-10-07

Performing a growth curve analysis using eyetrackingR
Overview of this vignette | Prerequisites | Visualize the timecourse | Growth curve analysis | But when did the conditions diverge? | References
Rendered fromgrowth_curve_analysis_vignette.Rmdusingknitr::rmarkdown

Last update: 2023-09-14
Started: 2015-10-07

Performing an onset-contingent analysis using eyetrackingR
Overview of this vignette | Prerequisites | Adding new columns | Visualize switches from an onset AOI to the target AOI | Analyze onset-contingent switch times | References
Rendered fromonset_contingent_analysis_vignette.Rmdusingknitr::rmarkdown

Last update: 2023-09-14
Started: 2015-10-07

Preparing your data for use with eyetrackingR
Overview of this vignette | Your Data | Data Preparation | Load dataset and dependencies, set data options for eyetrackingR. | Dealing with Non-AOI Looks | Cleaning Up Messy Data: | Adding an Area-of-Interest | Subsetting into the Time-Window of Interest Across Trials | Dealing with trackloss | How much data are we left with? | Assess mean trackloss for each participant | Summarize samples contributed per trial | Assess number of trials contributed by each participant | Summarize number of trials contributed | Create additional columns needed for analysis
Rendered frompreparing_your_data_vignette.Rmdusingknitr::rmarkdown

Last update: 2023-09-14
Started: 2015-10-07

Readme and manuals

Help Manual

Help pageTopics
Add an area-of-interest to your dataset, based on x-y coordinates and the AOI rectangle.add_aoi
Estimate confidence intervals for bootstrapped splines dataanalyze_boot_splines analyze_boot_splines.boot_splines_data
analyze_time_bins()analyze_time_bins analyze_time_bins.time_sequence_data
Bootstrap analysis of time-clusters.analyze_time_clusters analyze_time_clusters.time_cluster_data
Clean data by removing high-trackloss trials/subjects.clean_by_trackloss
Describe datasetdescribe_data
eyetrackingR: A package for cleaning, analyzing, and visualizing eye-tracking datasetseyetrackingR-package eyetrackingR
Get information about the clusters in a cluster-analysisget_time_clusters get_time_clusters.cluster_analysis get_time_clusters.time_cluster_data
Bootstrap resample splines for time-series data.make_boot_splines_data make_boot_splines_data.time_sequence_data
Convert raw data for use in eyetrackingRmake_eyetrackingr_data
Make onset-contingent data.make_onset_data
Summarize data into time-to-switch from initial AOI.make_switch_data make_switch_data.onset_data
Make data for cluster analysis.make_time_cluster_data make_time_cluster_data.time_sequence_data
make_time_sequence_data()make_time_sequence_data
Make a dataset collapsing over a time-windowmake_time_window_data
Plot test-statistic for each time-bin in a time-seriesplot.bin_analysis
Plot differences in bootstrapped-splines dataplot.boot_splines_analysis
Plot bootstrapped-splines dataplot.boot_splines_data
Visualize the results of a cluster analysis.plot.cluster_analysis
Plot some summarized data from eyetrackingRplot.eyetrackingR_data_summary
Plot onset-contingent dataplot.onset_data
Plot mean switch-from-initial-AOI times.plot.switch_data
Plot test-statistic for each time-bin in a time-series, highlight clusters. Plot time_cluster_data, highlights clusters of above-threshold time-bins.plot.time_cluster_data
Plot time-sequence dataplot.time_sequence_data
Plot a time-window datasetplot.time_window_data
Print Method for Cluster Analysisprint.cluster_analysis
Add the original class/attributes back onto result (usually of dplyr operation)reclass reclass.eyetrackingR_df
Simulate an eyetrackingR datasetsimulate_eyetrackingr_data
Extract a subset of the dataset within a time-window in each trial.subset_by_window
Summary Method for Time-bin Analysissummary.bin_analysis
Summary Method for Bootstrapped Splines Analysissummary.boot_splines_analysis
Summary Method for Cluster Analysissummary.cluster_analysis
Summary Method for Cluster Analysissummary.time_cluster_data
Analyze trackloss.trackloss_analysis
Data collected in an infant eyetracking studyword_recognition