Hierarchical Clustering with Spatial Constraints
c(person("Marie", "Chavent", role = c("aut", "cre"),
email = "Marie.Chavent@u-bordeaux.fr"),
person("Vanessa", "Kuentz", role = "aut"),
person("Amaury", "Labenne", role = "aut"),
person("Jerome", "Saracco", role = "aut"))
Marie Chavent [aut, cre],
Vanessa Kuentz [aut],
Amaury Labenne [aut],
Jerome Saracco [aut]
Implements a Ward-like hierarchical clustering
algorithm including spatial/geographical constraints.

Implements a Ward-like hierarchical clustering
algorithm including soft spatial/geographical constraints.

K-means clustering with build-in missing data imputation
This clustering algorithm deals with missing data via weights that are imposed on missings and succesively increased. See the vignette for details.
ClusterR, copula, dplyr, magrittr, rlang
psych, ggplot2, knitr, rmarkdown, testthat (>= 2.1.0), tidyr,
Hmisc, tictoc, spelling, corrplot, covr

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

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

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.
ClusterR, copula, dplyr, magrittr, tidyr, ggplot2, rlang,
knitr
ggExtra, rmarkdown, testthat (>= 2.1.0), Hmisc, tictoc,
spelling, corrplot, covr

K-Means Clustering with Build-in Missing Data Imputation