Allows one to assess the stability of individual objects, clusters 
    and whole clustering solutions based on repeated runs of the K-means and K-medoids 
    partitioning algorithms.

Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) <doi:10.1093/oxfordjournals.aob.a083391>.

The reliability of clusters is estimated using random projections. 
    A set of stability measures is provided to assess the reliability of the clusters 
    discovered by a generic clustering algorithm. 
    The stability measures are taylored to high dimensional data (e.g. DNA microarray data) 
    (Valentini, G (2005), <doi:10.1093/bioinformatics/bti817>.

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.

Provides tools for Bayesian estimation and inference for modelling clusterwise multivariate regression models for numeric, count, binary, ordinal and count outcomes observed repeatedly on the same units and where possible relations among outcomes are captured through a joint distribution of random effects. The clusters are defined through cluster-specific parameters, which the analyst can choose, e.g., with respect to the regression coefficients. In particular, the model specification for each regression model via the formula is specific to the outcome and consists of four parts: (1) fixed - regression coefficients common to all clusters, (2) group - group-specific regression coefficients, (3) random - random effects specific for each unit, (3) offset - name of an offset variable (if needed). Estimation is performed using MCMC sampling combining Gibbs and Metropolis-Hastings steps. Post-processing tools allow to assess convergence and address label switching and provide visual diagnostics. Units may be classified based on sampled allocation indicators or by exploiting the posterior distribution of the classification probabilities. For more details see Vavra et al. (2024) <doi:10.1007/s11222-023-10304-5>.