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Clusters trajectories (unequally spaced and unequal length time series) on a common time axis. Clustering proceeds by an EM algorithm that iterates switching between fitting a thin plate spline (TPS) to combined responses within each cluster (M-step) and reassigning cluster membership based on the nearest fitted TPS (E-step). Initial cluster assignments are random or distant trajectories. The fitting is done with the mgcv package function bam, which scales well to very large data sets. Additional parallelism available via multicore on unix and mac platforms.
See the vignettes for detailed use examples.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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