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Description: This repository accompanies the publication: 'A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series' (https://doi.org/to_be_published). The repository contains three main components: (1) research materials that document the open material, (2) data files that include raw and processed data files necessary to replicate all analyses, and (3) fully annotated analysis code, including the RMarkdown files of the methods and results sections as well as the Quarto files from the illustration. Abstract: Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of `dynamic measures' that describe meaningful developmental patterns for psychological data (e.g., instability, inertia, linear trend). Yet, commonly used clustering approaches are often not able to include these meaningful measures (e.g., due to model assumptions). We propose feature-based time series clustering as a flexible, transparent, and well-grounded approach that clusters participants based on the dynamic measures directly using common clustering algorithms. We introduce the approach and illustrate the utility of the method with real-world empirical data that highlight common ESM challenges of multivariate conceptualizations, structural missingness, and nonlinear trends. We use the data to showcase the main steps of input selection, feature extraction, feature reduction, feature clustering, and cluster evaluation. We also provide practical algorithm overviews and readily available code for data preparation, analysis, and interpretation.