Consensus Clustering Methods for Multiple Imputed Data // 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) doi:10.1023/A:1023949509487, rank aggregation methods follow Pihur et al. (2007) doi:10.1093/bioinformatics/btm158, and the PAC (Proportion of Ambiguous Clustering) metric is based on Senbabaoglu et al. (2014) doi:10.1038/srep06207.