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.

Clustering methods, which (if asked) can provide step-by-step
    explanations of the algorithms used, as described in Ezugwu et. al., (2022)
    <doi:10.1016/j.engappai.2022.104743>; and datasets to test them on, which
    highlight the strengths and weaknesses of each technique, as presented in
    the clustering section of 'scikit-learn' (Pedregosa et al., 2011)
    <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>.

Multiple comparison techniques are typically applied following an F
    test from an ANOVA to decide which means are significantly different from
    one another. As an alternative to traditional methods, cluster analysis
    can be performed to group the means of different treatments into
    non-overlapping clusters. Treatments in different groups are considered
    statistically different. Several approaches have been proposed, with varying
    clustering methods and cut-off criteria. This package implements
    cluster-based multiple comparisons tests and also provides a visual
    representation in the form of a dendrogram. Di Rienzo, J. A., Guzman, A. W., 
    & Casanoves, F. (2002) <jstor.org/stable/1400690>. Bautista, M. G., Smith, D. 
    W., & Steiner, R. L. (1997) <doi:10.2307/1400402>.

Model-based clustering of mixed data (i.e. data which consist of
    continuous, binary, ordinal or nominal variables) using a parsimonious
    mixture of latent Gaussian variable models.