Summary of Latrend: a Framework For Clustering Longitudinal Data, by Niek Den Teuling et al.
latrend: A Framework for Clustering Longitudinal Data
by Niek Den Teuling, Steffen Pauws, Edwin van den Heuvel
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces an R package called “latrend” that simplifies the process of clustering longitudinal data to identify common trends among subjects over time. The package enables comparisons between various methods, including dtwclust, flexmix, kml, lcmm, mclust, mixAK, and mixtools, by providing a unified framework for implementing these methods with minimal coding. Researchers can use “latrend” as an interface to access the capabilities of these packages, facilitating rapid prototyping and method comparison. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how people’s behaviors change over time. Imagine you’re tracking how well patients are sticking to their therapy plans for sleep apnea. You want to group similar patients together based on their progress. The “latrend” package makes it easy to do this by combining different methods for analyzing longitudinal data. This means researchers can quickly try out different approaches and see which one works best. |
Keywords
* Artificial intelligence * Clustering * Tracking
