Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters.

An interactive platform for clustering analysis and teaching based on the 'shiny' web application framework.
    Supports multiple popular clustering algorithms including k-means, hierarchical clustering,
    DBSCAN (Density-Based Spatial Clustering of Applications with Noise), PAM (Partitioning Around Medoids),
    GMM (Gaussian Mixture Model), and spectral clustering. Users can upload datasets or use built-in ones,
    visualize clustering results using dimensionality reduction methods such as Principal Component Analysis (PCA)
    and t-distributed Stochastic Neighbor Embedding (t-SNE), evaluate clustering quality via silhouette plots,
    and explore method-specific visualizations and guides. For details on implemented methods, see:
    Reynolds (2009, ISBN:9781598296975) for GMM;
    Luxburg (2007) <doi:10.1007/s11222-007-9033-z> for spectral clustering.

Implements a Ward-like hierarchical clustering
    algorithm including soft spatial/geographical constraints.

This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.

Clustering methods, which (if asked) can provide step-by-step
    explanations of the algorithms used, as described in Ezugwu et. al., (2022)
    <doi:10.1016/j.engappai.2022.104743>; and datasets to test them on, which
    highlight the strengths and weaknesses of each technique, as presented in
    the clustering section of 'scikit-learn' (Pedregosa et al., 2011)
    <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>.