Clusters longitudinal trajectories over time (can be unequally 
    spaced, unequal length time series and/or partially overlapping series) on
    a common time axis. Performs k-means clustering on a single continuous 
    variable measured over time, where each mean is defined by a thin plate 
    spline fit to all points in a cluster. Distance is MSE across trajectory 
    points to cluster spline. Provides graphs of derived cluster splines, 
    silhouette plots, and Adjusted Rand Index evaluations of the number
    of clusters. Scales well to large data with multicore parallelism available
    to speed computation.

Monitor and trace changes in clustering solutions of accumulating datasets 
    at successive time points. The clusters can adopt External and Internal transition at 
    succeeding time points. The External transitions comprise of Survived, Merged, Split, 
    Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes 
    in location and cohesion of the survived clusters. The package uses MONIC framework developed by 
    Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006)<doi:10.1145/1150402.1150491> .

A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; <DOI:10.18637/jss.v091.i10>). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; <DOI:10.1002/wics.1456>), which combine PCA for mixed-type data with K-means.