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All functions

GeomFlatViolin
geom_flat_violin_HELPER2
baseline_data()
Baseline pupil data to the average pupil size within a window
calculate_mean_pupil_size()
Calculate a mean size across two pupils over time
calculate_missing_data()
Calculate the missing data amount
clean_missing_data()
Clean missing data above an acceptable threshold
create_difference_data()
Create a difference data frame when dealing with a condition column with 2 levels
create_functional_data()
Makes a functional data with splines from a Pupil_difference_data dataframe.
create_time_windows()
Make PupillometryR dataframe into multiple time windows for easy analysis
create_window_data()
Make PupillometryR dataframe into a single collapsed window for easy analysis
detect_blinks_by_column()
detect blinks by a pre-existing labelled blink column that comes from the eyetracker
detect_blinks_by_size()
detect blinks by a change in pupil size
detect_blinks_by_velocity()
detect blinks by a change in velocity
downsample_time_data()
Downsample frequency to reduce number of samples and data size
filter_data()
Run a filter on the data to smooth it out.
geom_flat_violin()
ggplot Flat Violin
gfv_helper1 %||%
geom_flat_violin_HELPER1
interpolate_data()
Interpolate across the gaps in data
make_pupillometryr_data()
Prepare data for pre-processing in PupillometryR
mean2()
Helper function mean2
plot(<Pupil_difference_data>)
Pre-prepared plots of PupillometryR data
plot(<Pupil_test_data>)
Pre-prepared plots of PupillometryR data
plot(<Pupil_window_data>)
Pre-prepared plots of PupillometryR data
plot(<PupillometryR>)
Pre-prepared plots of PupillometryR data
pupil_data
Data collected in a pupillometry study by Sylvain Sirois
regress_data()
Regress one pupil against another for extra smoothing
replace_missing_data()
replaces missing observations if you have some degree of incomplete observations
run_functional_t_test()
Run a functional t-test on a dataframe previously fitted with b-splines.
subset_data()
Subset data to provide start and finish time windows