Description: An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). 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.

Author: Marie Chavent and Vanessa Kuentz and Benoit Liquet and Jerome
Description: Cluster analysis of a set of variables. Variables can be
quantitative, qualitative or a mixture of both.

Author: Marie Chavent and Vanessa Kuentz and Benoit Liquet and Jerome Saracco
Description: Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.

person("Benoit", "Liquet", role = "aut"),

Title: Clustering of variables around Latent Variables
Description: This package provides a set of function that allows us to cluster a set of variables
around Latent Variables. Each group is associated with a latent variable.
External variables measured on the same observations or/and additional information on the
Packaged: 2013-10-31 14:57:55 UTC; mingkun.chen

Description: This package provides a set of functions that allows us to cluster a set of variables
Packaged: 2013-12-12 14:49:41 UTC; mingkun.chen

Packaged: 2014-04-04 07:49:44 UTC; mingkun.chen

Description: This package provides a set of functions that allows us to cluster
a set of variables around Latent Variables. Each group is associated with a
latent variable. External variables measured on the same observations
or/and additional information on the variables can be taken into account.
Packaged: 2014-09-03 09:04:57 UTC; mingkun.chen