Package: kollaR 1.0.4

Johan Lundin Kleberg

kollaR: Filtering, Visualization and Analysis of Eye Tracking Data

Functions for analysing eye tracking data, including event detection (I-VT, I-DT and two means clustering), visualizations and area of interest (AOI) based analyses. See separate documentation for each function. The principles underlying I-VT and I-DT filters 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}).

Authors:Johan Lundin Kleberg [aut, cre]

kollaR_1.0.4.tar.gz
kollaR_1.0.4.tar.gz(r-4.5-noble)kollaR_1.0.4.tar.gz(r-4.4-noble)
kollaR_1.0.4.tgz(r-4.4-emscripten)kollaR_1.0.4.tgz(r-4.3-emscripten)
kollaR.pdf |kollaR.html✨
kollaR/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 251 downloads 17 exports 118 dependencies

Last updated 19 days agofrom:f8eb29da52. Checks:2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 20 2025
R-4.5-linuxOKFeb 20 2025

Exports:animated_fixation_plotaoi_testcluster2mdownsample_gazedraw_aoisfilt_plot_2dfilt_plot_temporalfind.transition.weightsidt_filterinterpolate_with_marginivt_filtermerge_adjacent_fixationsplot_filter_resultsplot_sample_velocityplot_velocity_profilesprocess_gazestatic_plot

Dependencies:abindaskpassbackportsbase64encbootbroombslibcachemcarcarDataclicolorspacecommonmarkcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDerivdigestdoBydplyrevaluatefansifarverfastmapfontawesomeFormulafsgenericsggforceggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehighrhtmltoolshtmlwidgetshttpuvhttrisobandjpegjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelme4magickmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslpatchworkpbkrtestpillarpkgconfigplotlypolyclippolynompromisespurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangrmarkdownrstatixsassscalesshinysourcetoolsSparseMstringistringrsurvivalsyssystemfontstibbletidyrtidyselecttinytextweenrutf8vctrsviridisLitewithrxfunxtableyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Filtering, Visualization, and Analysis of Eye Tracking DatakollaR
Create GIF animation of fixations on a stimulus imagesanimated_fixation_plot
Test whether a gaze coordinates are within or outside a rectangular or elliptical AOI. The aois df must contain the variables x0, x1, y0 and y1. x0 is the minimum x value, y0 the minimum y value. x1 the maximum x value. y1 the maximum y value and type where rect means that the AOI is a rectangle and circle that the AOI is a circle or ellipse If a column called name is present, the output for each AOI will be labelled accordingly. Otherwise, the output will be labelled according to the order of the AOI in the data frame. The df 'gaze' must contain the variables onset, duration, x, and y. Latency will be defined as the value in onset of the first detected gaze coordinate in the AOI Make sure that the timestamps are correct! The function can be used with gaze data either fixations, saccades, or single samples. Note that the output variables are not equally relevant for all types of gaze data. For example, both total duration and latency are relevant in many analyses focusing on fixations, but total duration may be less relevant in analyses of saccades.aoi_test
Fixation detection by two-means clusteringcluster2m
Downsample gazedownsample_gaze
Draw one or more areas of interest, AOIs, on a stimulus image and save to the R prompt. The input is the path to a 2D image. Supported file formats: JPEG, BMP, PNG. The function returns a data frame with all saved AOIs. By default, AOIs are drawn in a coordinate system where y is 0 in the lower extreme of the image, e.g., an ascending y axis. Tobii eye trackers use a coordinate system with a descending y-axis, e.g., x and y are 0 in the upper left corner of the image. Make sure that your AOIS match the coordinate system of your eye tracker output. By setting the parameter reverse.y.axis to TRUE, the saved AOIs will be reformatted to fit a coordinate system with a descending y-axis. All AOIS have the variables x0, x1, y0 and y1. x0 is the minimum x value, y0 the minimum y value. x1 the maximum x value. y1 the maximum y valuedraw_aois
Plot fixation filtered vs. raw or unfiltered gaze coordinates in 2D space.filt_plot_2d
Plot fixation filtered vs. raw gaze coordinatesfilt_plot_temporal
Find transition weights for each sample in a gaze matrix.find.transition.weights
Dispersion-based fixation detection algorithm '(I-DT)'idt_filter
Interpolate over gaps (subsequent NAs) in vector.interpolate_with_margin
I-VT algorithm for fixation and saccade detectionivt_filter
Merge adjacent fixationsmerge_adjacent_fixations
Plot validity measures from one or more fixation detection algorithmsplot_filter_results
Plot the sample-to-sample velocity of eye tracking data.plot_sample_velocity
Create ggplot of saccade velocity profilesplot_velocity_profiles
Interpolation and smoothing of gaze-vectorprocess_gaze
Fixation-filtered sample-by-sample example datasample.data.filtered
Fixations from 1 individualsample.data.fixation1
Fixations from 7 individualssample.data.fixations
Pre-processed sample-by-sample example datasample.data.processed
Saccades from 3 individualssample.data.saccades
Unprocessed sample-by-sample example datasample.data.unprocessed
Plot fixations in 2D space overlaied on a stimulus imagestatic_plot