Title: Adaptive Kmeans algorithm based on threshold
Description: Adaptive K-means algorithm with various threshold
settings.  It support two distance metric at the first version:
Euclidean distance, Cosine distance (1 - cosine similarity)

Description: Adaptive K-means algorithm with various threshold settings.
In version 1.1, it contains one more threshold condition.

Title: Anchored Kmedoids for Longitudinal Data Clustering
Author: Monsuru Adepeju [cre, aut], Samuel Langton [aut], Jon Bannister [aut]
Description: 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.
Imports: kml, Hmisc, ggplot2, utils, reshape2, longitudinalData

Suggests: knitr, rmarkdown, flextable, kableExtra

Description: 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.
Imports: kml, Hmisc, ggplot2, utils, reshape2, longitudinalData, stats,
Suggests: knitr, rmarkdown, flextable, kableExtra, clusterCrit