Description: Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>)  and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, Free Sorting and JAR experiments are available.

Title: Clustering of Observations with Geographical Constraints
Author: Amaury Labenne, Marie Chavent, Vanessa Kuentz-Simonet and Jerome Saracco
Description: Functions which allow to integrate geographical constraints in Ward hierarchical clustering. Geographical maps of typologies obtained can be displayed with the use of shapefiles.
Depends: R(>= 2.10.0), rCarto, FactoMineR, plyr,
Packaged: 2015-06-23 12:41:59 UTC; Amaury.Labenne

Title: Hierarchical Clustering with Spatial Constraints
Authors@R: c(person("Marie", "Chavent", role = c("aut", "cre"),
person("Vanessa", "Kuentz", role = "aut"),
person("Amaury", "Labenne", role = "aut"),
person("Jerome", "Saracco", role = "aut"))
Description: Implements a Ward-like hierarchical clustering

algorithm including soft spatial/geographical constraints.

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