Description: This clustering algorithm deals with missing data via weights that are imposed on missings and successively increased. See the vignette for details.

Suggests: ggExtra, ggplot2, knitr, rmarkdown, testthat (>= 2.1.0),
tidyr, Hmisc, tictoc, spelling, corrplot, covr

Description: 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.
Imports: ClusterR, copula, dplyr, magrittr, tidyr, ggplot2, rlang,
Suggests: ggExtra, rmarkdown, testthat (>= 2.1.0), Hmisc, tictoc,

Title: K-Means Clustering with Build-in Missing Data Imputation

Title: Variable Selection in Clustering by Mixture Models for Discrete
Description: An implementation of a variable selection procedure in clustering by mixture of multinomial models for discrete data. Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.
Collate: "RcppExports.R" "ClustMMDD.R" "modelKS.R" "zzz.R"

Description: An implementation of a variable selection procedure in clustering by mixture of multinomial models for discrete data. Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.