Advances a novel adaptation of longitudinal k-means clustering technique (Genolini et al. (2015) <doi:10.18637/jss.v065.i04>) for grouping trajectories based on the similarities of their long-term trends and determines the optimal solution based on either the average silhouette width (Rousseeuw P. J. 1987) or the Calinski-Harabatz criterion (Calinski and Harabatz (1974) <doi:10.1080/03610927408827101>). Includes functions to extract descriptive statistics and generate a visualization of the resulting groups, drawing methods from the 'ggplot2' library (Wickham H. (2016) <doi:10.1007/978-3-319-24277-4>). The package also includes a number of other useful functions for exploring and manipulating longitudinal data prior to the clustering process.
Package details |
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Author | Monsuru Adepeju [cre, aut], Samuel Langton [aut], Jon Bannister [aut] |
Maintainer | Monsuru Adepeju <monsuur2010@yahoo.com> |
License | GPL-3 |
Version | 1.2.6 |
URL | https://cran.r-project.org/package=akmedoids |
Package repository | View on GitHub |
Installation |
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