adjust_fixation_timing
                        Adjust the onset and offset of fixations to
                        avoid misclassification of saccade samples as
                        belonging to fixations
algorithm_adaptive      Adaptive velocity-based algorithm for saccade
                        and fixation detection
algorithm_i2mc          Fixation detection by two-means clustering
algorithm_idt           Dispersion-based fixation detection algorithm
                        '(I-DT)'
algorithm_ivt           I-VT algorithm for fixation and saccade
                        detection
animated_fixation_plot
                        Create GIF animation of fixations on a stimulus
                        images
aoi_test                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.
calculate_rms           Calculate sample-to-sample root mean square
                        distance (RMS) of a data segment
cluster2m               Fixation detection by two-means clustering
downsample_gaze         Downsample gaze
draw_aois               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
                        value
filt_plot_2d            Plot fixations vs. individual sample
                        coordinates in 2D space. In the current
                        release, filt_plot_2d is a wrapper around
                        fixation_plot_2d which accepts the same
                        arguments.
filt_plot_temporal      Plot fixation filtered vs. raw gaze
                        coordinates. This function will be replaced by
                        fixation_plot_temporal in future releases. It
                        is currently a wrapper around
                        fixation_plot_temporal accepting the same
                        arguments.
find.transition.weights
                        Find transition weights for each sample in a
                        gaze matrix.
find.valid.periods      Find subsequent periods in a vector with values
                        below a threshold. Used internally by the
                        function suggest_threshold
fixation_plot_2d        Plot fixations vs. individual sample
                        coordinates in 2D space.
fixation_plot_temporal
                        Plot fixation classified vs. raw gaze
                        coordinates
fixation_plot_ts        Plot fixation classified vs. raw gaze
                        coordinate time series
idt_filter              Dispersion-based fixation detection algorithm
                        '(I-DT)'
interpolate_with_margin
                        Interpolate over gaps (subsequent NAs) in
                        vector.
ivt_filter              I-VT algorithm for fixation and saccade
                        detection
kollaR                  Fixation and Saccade Detection, Visualization,
                        and Analysis of Eye Tracking Data
merge_adjacent_fixations
                        Merge adjacent fixations
movmean.filter          Calculate the moving mean of a vector
plot_algorithm_results
                        Plot vdescriptives one or more fixation
                        detection algorithms
plot_filter_results     Plot descriptives from one or more fixation
                        detection algorithms
plot_sample_velocity    Plot the sample-to-sample velocity of eye
                        tracking data.
plot_velocity_profiles
                        Create ggplot of saccade velocity profiles
preprocess_gaze         Interpolation and smoothing of gaze-vector
process_gaze            Interpolation and smoothing of gaze-vector.
                        This function will be replaced by
                        preprocess_gaze in future versions.
                        process_gaze is a wrapper around preprocess
                        gaze (the two functions produce the same
                        result)
sample.data.classified
                        Sample-to-sample raw and fixation classified
                        data from 1 individual
sample.data.fixation1   Fixations from 1 individual
sample.data.fixations   Fixations from 7 individuals
sample.data.processed   Pre-processed sample-by-sample example data
sample.data.saccades    Saccades from 3 individuals
sample.data.unprocessed
                        Unprocessed sample-by-sample example data
static_plot             Plot fixations in 2D space on a stimulus image
                        (with visible x and y axis labels and ticks)
static_plot_minimal     Plot fixations in 2D space on a stimulus image
                        (minimalistic plot)
suggest_threshold       Data-driven identification of threshold
                        parameters for adaptive veloctity-based saccade
                        detection.
summarize_fixation_metrics
                        Summarize fixation statistics
trim_fixations          Adjust the onset and offset of fixations to
                        avoid misclassification of saccade samples as
                        belonging to fixations
