Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). 
 (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, 
 HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, 
 RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, 
 JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, 
 MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, 
 JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, 
 DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, 
 CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>,
 HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, 
 TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, 
 BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, 
 WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).

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>.

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.