latrend: A Framework for Clustering Longitudinal Data
This provides a tool for researchers working with longitudinal data to streamline method comparisons and prototyping, but it is incremental as it builds on existing packages.
The authors tackled the problem of comparing and applying longitudinal clustering methods by introducing the R package 'latrend' as a unified framework, which reduces coding effort and enables method comparisons, demonstrated on a synthetic dataset of patient therapy adherence patterns.
Clustering of longitudinal data is used to explore common trends among subjects over time for a numeric measurement of interest. Various R packages have been introduced throughout the years for identifying clusters of longitudinal patterns, summarizing the variability in trajectories between subject in terms of one or more trends. We introduce the R package "latrend" as a framework for the unified application of methods for longitudinal clustering, enabling comparisons between methods with minimal coding. The package also serves as an interface to commonly used packages for clustering longitudinal data, including "dtwclust", "flexmix", "kml", "lcmm", "mclust", "mixAK", and "mixtools". This enables researchers to easily compare different approaches, implementations, and method specifications. Furthermore, researchers can build upon the standard tools provided by the framework to quickly implement new cluster methods, enabling rapid prototyping. We demonstrate the functionality and application of the latrend package on a synthetic dataset based on the therapy adherence patterns of patients with sleep apnea.