Eye-Tracking Data Analysis
Add an area-of-interest to your dataset, based on x-y coordinates and ...
Estimate confidence intervals for bootstrapped splines data
analyze_time_bins()
Bootstrap analysis of time-clusters.
Clean data by removing high-trackloss trials/subjects.
Describe dataset
eyetrackingR: A package for cleaning, analyzing, and visualizing eye-t...
Get information about the clusters in a cluster-analysis
Bootstrap resample splines for time-series data.
Convert raw data for use in eyetrackingR
Make onset-contingent data.
Summarize data into time-to-switch from initial AOI.
Make data for cluster analysis.
make_time_sequence_data()
Make a dataset collapsing over a time-window
Plot test-statistic for each time-bin in a time-series
Plot differences in bootstrapped-splines data
Plot bootstrapped-splines data
Visualize the results of a cluster analysis.
Plot some summarized data from eyetrackingR
Plot onset-contingent data
Plot mean switch-from-initial-AOI times.
Plot test-statistic for each time-bin in a time-series, highlight clus...
Plot time-sequence data
Plot a time-window dataset
Print Method for Cluster Analysis
Add the original class/attributes back onto result (usually of dplyr o...
Simulate an eyetrackingR dataset
Extract a subset of the dataset within a time-window in each trial.
Summary Method for Time-bin Analysis
Summary Method for Bootstrapped Splines Analysis
Summary Method for Cluster Analysis
Summary Method for Cluster Analysis
Analyze trackloss.
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>.