Provides tools for performing consensus clustering on multiple
imputed datasets. The package supports a range of clustering algorithms
across imputations, including hierarchical methods (e.g., Ward, single,
complete, average) and partition-based approaches such as k-means,
k-medoids (PAM), fuzzy clustering, model-based clustering ('mclust'),
and methods for mixed or categorical data (k-modes and k-prototypes).
A co-assignment matrix is constructed to quantify agreement between
partitions, and consensus solutions are derived via hierarchical
clustering applied to the resulting dissimilarity matrix. Additional
functions are provided for validation and visualization of clustering
results, facilitating robust analysis in the presence of missing data.
Consensus clustering framework is based on Monti et al. (2003)