<p>Eye tracking has become an increasingly important tool in cognitive and developmental research, providing insights into processes that are difficult to measure otherwise. The majority of eye-tracking studies rely on accurate identification of fixations and saccades in raw data using event classification algorithms (sometimes called fixation filters). Subsequently, it is common to analyze whether fixations or saccades fall into specific areas of interest (AOI). The choice of algorithms can significantly influence study outcomes, especially in special populations such as young children or individuals with neurodevelopmental conditions, where data quality is often compromised by factors such as signal loss, poor calibration, or movement artifacts. It is therefore crucial to examine how available fixation classification algorithms affect the data set at hand as part of the eye-tracking analysis. Here, we introduce the kollaR package, an open-source R library for performing the main steps of an eye-tracking analysis from event classification to AOI-based analyses and visualizations of individual or group-level data for publications. The kollaR package was specifically designed to facilitate the selection and comparison of different event classification algorithms through visualizations. In a validation analysis, we show that results from fixation classification in kollaR are consistent with those from other software implementations of the same algorithms. We demonstrate the use of kollaR with real data from typically developing individuals and individuals with neurodevelopmental conditions, and illustrate how potential threats to validity can be identified in both high- and low-quality data.</p>