Convex Clustering methods, including K-means algorithm,
  On-line Update algorithm (Hard Competitive Learning) and Neural Gas
  algorithm (Soft Competitive Learning), and calculation of several
  indexes for finding the number of clusters in a data set.

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>.

Classification method described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes.

Functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.