akmedoids: Anchored Kmedoids for Longitudinal Data Clustering

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 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: 0.1.2
Depends: R (≥ 3.5.0)
Imports: kml, Hmisc, ggplot2, utils, reshape2, longitudinalData
Suggests: knitr, rmarkdown, flextable, kableExtra
Published: 2019-04-19
Author: Monsuru Adepeju [cre, aut], Samuel Langton [aut], Jon Bannister [aut]
Maintainer: Monsuru Adepeju <monsuur2010 at yahoo.com>
License: GPL-2
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_0.1.2.tar.gz
Windows binaries: r-devel: akmedoids_0.1.2.zip, r-release: akmedoids_0.1.2.zip, r-oldrel: akmedoids_0.1.2.zip
OS X binaries: r-release: akmedoids_0.1.2.tgz, r-oldrel: akmedoids_0.1.2.tgz
Old sources: akmedoids archive


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