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 visualisation 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.
| Version: |
1.3.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
kml, stats, utils, dplyr, signal, Hmisc, grDevices, ggplot2, clusterCrit |
| Suggests: |
knitr, rmarkdown, digest, gdtools, kableExtra |
| Published: |
2021-04-13 |
| Author: |
Monsuru Adepeju [cre, aut],
Samuel Langton [aut],
Jon Bannister [aut] |
| Maintainer: |
Monsuru Adepeju <monsuur2010 at yahoo.com> |
| BugReports: |
https://github.com/MAnalytics/akmedoids/issues |
| License: |
GPL-3 |
| URL: |
https://cran.r-project.org/package=akmedoids |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README NEWS |
| CRAN checks: |
akmedoids results |