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`eyeris`: Flexible, Extensible, & Reproducible Pupillometry Preprocessing

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Install

install.packages('eyeris')

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575

Version

2.1.1

License

MIT + file LICENSE

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Repository

https://github.com/shawntz/eyeris/

Homepage

https://shawnschwartz.com/eyeris/

Maintainer

Shawn Schwartz

Last Published

July 26th, 2025

Functions in eyeris (2.1.1)

Check if column exists in dataframe

NA-pad blink events / missing data

check_time_monotonic

Check time series monotonicity

Internal function to remove blink artifacts from pupil data

Check if object is of class eyeris

check_epoch_msg_values

Check epoch message values against available events

Detrend the pupil time series

Clean string by removing non-alphanumeric characters

export_confounds_to_csv

Export confounds data to CSV files

Remove pupil samples that are physiologically unlikely

convert_nested_dt

Convert nested data.table objects to tibbles

Downsample pupil time series with anti-aliasing filtering

Count epochs and validate data is epoched

detransient_pupil

Internal function to remove transient artifacts from pupil data

compute_baseline

Compute baseline correction for epoch data

draw_random_epochs

Draw random epochs for plotting

Main epoching and baselining logic

check_start_end_timestamps

Check start and end timestamps are balanced

check_baseline_mean

Check if baseline mean is zero

Create a counter progress bar

check_epoch_input

Check epoch input for plotting

check_baseline_inputs

Check baseline input arguments

evaluate_pipeline_step_params

Evaluate pipeline step parameters

Draw vertical lines at NA positions

Epoch (and baseline) pupil data based on custom event message structure

eyelink_asc_binocular_demo_dataset

Access example EyeLink .asc binocular mock dataset file provided by the eyeris package.

epoch_start_end_msg

Epoch using a start and an end message (explicit timestamps)

check_pupil_cols

Check if pupil_raw column exists

check_epoch_manual_input_data

Check epoch manual input data structure

epoch_and_baseline_block

Block-by-block epoch and baseline handler

Obtain timestamps from events data

get_confounds_for_step

Calculate confounds for a single pupil data step

extract_baseline_epochs

Extract baseline epochs from timeseries data

Internal function to detrend pupil data

eyelink_asc_demo_dataset

Access example EyeLink .asc demo dataset file provided by the eyeris package.

Run eyeris commands with automatic logging of R console's stdout and stderr

Manually epoch using provided start/end dataframes of timestamps

epoch_only_start_msg

Epoch based on a single event message (without explicit limits)

downsample_pupil

Internal function to downsample pupil data

Create eyeris report

get_block_numbers

Extract block numbers from eyeris object or character vector

format_call_stack

Format call stack information for display

Internal function to lowpass filter pupil data

sanitize_event_tag

Sanitize event tag string into canonical epoch label

Internal function to run bidsify on a single eye

merge_events_with_timeseries

Process event messages and merge with timeseries

The opinionated "glass box" eyeris pipeline

Filter epoch names from eyeris object

print_lightbox_img_html

Print lightbox image HTML

Create markdown table from dataframe

Create interactive epoch gallery report

find_baseline_structure

Find baseline structure name for a given epoch

Handle errors with custom error classes

eyeris: Flexible, Extensible, & Reproducible Pupillometry Preprocessing

make_baseline_label

Create baseline label for epoch data

epoch_start_msg_and_limits

Epoch using a start message with fixed limits around it

interpolate_pupil

Interpolate missing pupil data using linear interpolation

is_binocular_object

Check if object is a binocular eyeris object

Create a progress bar for tracking operations

Print plots in markdown format

glassbox_internal

Internal glassbox function for processing individual eyes

Prompt user for continuation

eyeris_color_palette

Default color palette for eyeris plotting functions

Interpolate missing pupil samples

Index metadata from dataframe

Tag blinks in pupil data

plot_pupil_distribution

Plot pupil distribution histogram

plot_detrend_overlay

Internal helper to plot detrending overlay

make_epoch_label

Generate epoch label from events and data

make_bids_fname

Make a BIDS-compatible filename

make_prog_summary_plot

Create progressive preprocessing summary plot

Plot pre-processed pupil data from eyeris

plot_binocular_correlation

Plot binocular correlation between left and right eye data

make_md_table_multiline

Create multiline markdown table from dataframe

Load and parse SR Research EyeLink .asc files

Lowpass filtering of time series data

Plot with seed handling for glassbox pipeline

Tick a progress bar

tag_gaze_coords

Tag gaze coordinates as on/off screen

Slice epoch from raw timeseries data

save_progressive_summary_plots

Save progressive summary plots for each block

should_plot_binoc_cors

Check if binocular correlations should be plotted

save_detrend_plots

Save detrend plots for each block

Z-score pupil timeseries data

plot_gaze_heatmap

Create gaze heatmap of eye coordinates

slice_epochs_no_limits

Slice epochs with no explicit limits

process_epoch_and_baselines

Epoch and baseline processor

process_eyeris_data

Process eyeris data and create eyeris object

Calculate pupil speed using finite differences

summarize_confounds

Extract confounding variables calculated separately for each pupil data file

Internal function to z-score pupil data

slice_epochs_with_limits

Slice epochs with explicit limits

normalize_gaze_coords

Normalize gaze coordinates to screen-relative units

Render R Markdown report

pipeline_handler

Build a generic operation (extension) for the eyeris pipeline

parse_eyelink_info

Parse EyeLink version and model information

parse_call_stack

Parse call stack information

Robust plotting function with error handling

check_and_create_dir

Check and create directory if it doesn't exist

Save out pupil timeseries data in a BIDS-like structure

Bin pupil time series by averaging within time bins

check_baseline_epoch_counts

Check baseline and epoch counts match

Display formatted alert messages

Check if input argument is provided

calc_euclidean_dist

Calculate Euclidean distance between points

add_unique_event_identifiers

Add unique event identifiers to handle duplicate event messages

Bin pupil data into specified time bins

check_epoch_manual_input_dfs

Check epoch manual input dataframe format

add_unique_identifiers_to_df

Add unique identifiers to a single events data frame

calculate_epoched_confounds

Calculate confounds for epoched data

Check limits in wildcard mode