{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:53:20Z","timestamp":1784120000269,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JEMR"],"abstract":"<jats:p>Reproducible eye-movement research requires a documented path from device exports to structured, quality-checked, and reportable data objects. Gazepoint GP3 and Gazepoint Analysis provide accessible gaze, fixation, pupil, timing, media, and area-of-interest data, but their folder-based CSV exports are not automatically analysis-ready. Existing R tools support important stages of eye-tracking and pupillometry analysis, but they generally assume that data have already been organised into suitable structures; they do not provide a Gazepoint-aware workflow beginning with export-folder checks, all-gaze\/fixation pairing, sampling and tracking-quality diagnostics, and preservation of preprocessing decisions through reporting. This article presents gp3tools, an open-source R package (R version 4.6.1) that converts Gazepoint GP3\/Gazepoint Analysis exports into structured R objects, diagnostic summaries, preprocessing outputs, model-ready tables, interoperability objects, and reproducible reports. Rather than introducing a new statistical estimator, the package provides an export-aware workflow scaffold for import checking, quality control, pupil preprocessing, area-of-interest, fixation and transition summaries, model preparation, interoperability, and reporting. A synthetic Gazepoint-style demonstration dataset was used to evaluate workflow execution without exposing private participant data. The demonstration identified all expected file pairs and produced sample-level gaze\/pupil tables, fixation tables, sampling-quality summaries, area-of-interest summaries, review flags, and reporting outputs. A small, private real-export compatibility check further showed that the workflow could process one empirical Gazepoint folder without manual restructuring. These results support a bounded software-evaluation claim: gp3tools executes the tested Gazepoint-style workflow and returns expected diagnostic and reporting objects, but the evidence does not establish hardware accuracy, preprocessing accuracy, computational scalability, general robustness across all Gazepoint exports, or substantive psychological or perceptual effects.<\/jats:p>","DOI":"10.3390\/jemr19040076","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T08:04:31Z","timestamp":1784016271000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["gp3tools: An R Package for Reproducible Analysis and Reporting of Gazepoint GP3 Eye-Tracking Exports"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2444-9796","authenticated-orcid":false,"given":"Stefanos","family":"Balaskas","sequence":"first","affiliation":[{"name":"eGovernment & eCommerce Lab (Innovation & Entrepreneurship), Department of Business Administration, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Berkovsky, S., Taib, R., Koprinska, I., Wang, E., Zeng, Y., Li, J., and Kleitman, S. 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