% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cclustr-package.R
\docType{package}
\name{cclustr-package}
\alias{cclustr}
\alias{cclustr-package}
\title{cclustr: Consensus Clustering Methods for Multiple Imputed Data}
\description{
The \pkg{cclustr} package provides a comprehensive framework for performing
clustering analysis on datasets treated with multiple imputation. It
addresses the uncertainty inherent in missing data by finding a stable
consensus partition across multiple imputed realizations.
}
\details{
Standard statistical workflows for imputed data often focus on supervised
learning or univariate statistics. \pkg{cclustr} extends this to
unsupervised learning by implementing a co-assignment matrix approach.
}
\section{Main Workflow}{

To achieve a robust consensus solution, follow these steps:
\enumerate{
\item \strong{Standardization:} Use \code{\link{as_mild_list}} to format
imputed data (e.g., from \pkg{mice}) into a consistent structure.
\item \strong{Clustering:} Apply \code{\link{cluster_imputations}} to run
algorithms (K-means, PAM, K-prototypes, etc.) across all imputations.
\item \strong{Consensus:} Generate the final partition using
\code{\link{consensus_clustering}} based on co-assignment probabilities.
\item \strong{Selection:} Evaluate model quality and optimal cluster
counts with \code{\link{choose_best_clustering}}.
}
}

\section{Supported Data Types}{

\itemize{
\item \strong{Numeric:} Hierarchical clustering, K-means, Fuzzy C-means, and Mclust.
\item \strong{Categorical:} K-modes.
\item \strong{Mixed:} PAM with Gower distance or K-prototypes.
}
}

\references{
Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003).
Consensus clustering: A resampling-based method for class discovery
and visualization of gene expression microarray data.
\emph{Machine Learning}, \strong{52}(1-2), 91-118.
\doi{10.1023/A:1023949509487}

Senbabaoglu, Y., Michailidis, G., & Li, J. Z. (2014).
Critical limitations of consensus clustering in class discovery.
\emph{Scientific Reports}, \strong{4}, 6207.
\doi{10.1038/srep06207}

Pihur, V., Datta, S., & Datta, S. (2007).
Weighted rank aggregation of cluster validation measures.
\emph{Bioinformatics}, \strong{23}(13), 1607-1615.
\doi{10.1093/bioinformatics/btm158}

Rubin, D. B. (1987).
\emph{Multiple Imputation for Nonresponse in Surveys}. Wiley.

van Buuren, S., & Groothuis-Oudshoorn, K. (2011).
mice: Multivariate Imputation by Chained Equations in R.
\emph{Journal of Statistical Software}, \strong{45}(3), 1-67.
\doi{10.18637/jss.v045.i03}

Hubert, L., & Arabie, P. (1985).
Comparing partitions.
\emph{Journal of Classification}, \strong{2}, 193-218.
\doi{10.1007/BF01908075}

Rousseeuw, P. J. (1987).
Silhouettes: A graphical aid to the interpretation and validation
of cluster analysis.
\emph{Journal of Computational and Applied Mathematics}, \strong{20}, 53-65.
\doi{10.1016/0377-0427(87)90125-7}

Calinski, T., & Harabasz, J. (1974).
A dendrite method for cluster analysis.
\emph{Communications in Statistics}, \strong{3}, 1-27.
\doi{10.1080/03610927408827101}

Davies, D. L., & Bouldin, D. W. (1979).
A cluster separation measure.
\emph{IEEE Transactions on Pattern Analysis and Machine Intelligence},
\strong{PAMI-1}, 224-227.
\doi{10.1109/TPAMI.1979.4766909}

Dunn, J. C. (1974).
Well-separated clusters and optimal fuzzy partitions.
\emph{Journal of Cybernetics}, \strong{4}, 95-104.
\doi{10.1080/01969727408546059}
}
\seealso{
Useful links:
\itemize{
\item \url{https://github.com/andrews06ml/cclustr}
\item Report bugs at \url{https://github.com/andrews06ml/cclustr/issues}
}
}
\author{
\strong{Maintainer}: Andres Montenegro Lemus \email{andresfemole@gmail.com}

Authors:
\itemize{
\item Anhuar Duran Mendoza \email{asteyduran15@gmail.com}
\item Mario Pacheco Lopez \email{mariopachecolopez@gmail.com}
}
}
\keyword{internal}
