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: | 0.1.5 |
| Depends: | R (≥ 3.5.0) |
| Imports: | kml, Hmisc, ggplot2, utils, reshape2, longitudinalData, stats, signal |
| Suggests: | knitr, rmarkdown, flextable, kableExtra, clusterCrit |
| Published: | 2020-01-10 |
| Author: | Monsuru Adepeju [cre, aut], Samuel Langton [aut], Jon Bannister [aut] |
| Maintainer: | Monsuru Adepeju <monsuur2010 at yahoo.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | akmedoids results |
| Reference manual: | akmedoids.pdf |
| Vignettes: |
A guide to measuring long-term inequality in the exposure to crime at micro-area levels using 'Akmedoids' package |
| Package source: | akmedoids_0.1.5.tar.gz |
| Windows binaries: | r-devel: akmedoids_0.1.5.zip, r-release: akmedoids_0.1.5.zip, r-oldrel: akmedoids_0.1.5.zip |
| macOS binaries: | r-release: akmedoids_0.1.5.tgz, r-oldrel: akmedoids_0.1.5.tgz |
| Old sources: | akmedoids archive |
Please use the canonical form https://CRAN.R-project.org/package=akmedoids to link to this page.