# Analyse Eye Data

## Documentation for package ‘eyetools’ version 0.9.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

AOI_seq | Sequence analysis of area of interest entries
AOI_time | Analysis of time spent in areas of interest
AOI_time_binned | Binned time analysis of area of interest entries
combine_eyes | Combine binocular data into single X/Y coordinate pairs
compare_algorithms | A battery of metrics and plots to compare the two algorithms (dispersion and VTI)
conditional_transform | conditional_transform
create_AOI_df | Create a blank data frame for populating with AOIs
dist_to_visual_angle | Compute visual angle from distance metrics
fixation_dispersion | Fixation detection using a dispersion method
fixation_VTI | Fixation detection using a velocity threshold identification method

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.

HCL_AOIs | Example AOIs for use with HCL
HCL_behavioural | Example dataset of behavioural data to complement dataset HCL.
hdf5_get_event | Get messgaes stored in TOBII-generated HDF5 files
hdf5_to_df | Convert TOBII-generated HDF5 files to dataframe
interpolate | Interpolation of missing data (NAs)
plot_AOI_growth | Plots absolute or proportional time spent in AOIs over time
plot_heatmap | Plot heatmap of raw data
plot_seq | Plot of raw data over time
plot_spatial | Plot raw data and fixations
saccade_VTI | Velocity threshold identification of saccades
smoother | Smoothing of raw data