person("Jeremy", "Rohmer", email = "J.Rohmer@brgm.fr", role = "aut"),
person("Yves", "Deville", email = "deville.yves@alpestat.com", role = "ctb"),
person("Deborah", "Idier", email = "D.Idier@brgm.fr", role = "ctb"))
with emphasis on treatment of functional inputs that are regularly sampled. This package
Imports: methods, foreach, knitr, scales, microbenchmark, doFuture,

Title: Model-based clustering in group-specific functional subspaces
Description: The package provides the funHDDC algorithm (Bouveyron & Jacques, 2011) which allows to cluster functional data by modeling each group within a specific functional subspace.

Title: Univariate and Multivariate Model-Based Clustering in
Author: A Schmutz, J. Jacques & C. Bouveyron
Description: The funHDDC algorithm (Bouveyron and Jacques, 2001, Schmutz et al., 2017) allows to cluster functional univariate or multivariate data by modeling each group within a specific functional subspace.

Description: The funHDDC algorithm (Schmutz et al., 2017) allows to cluster functional univariate or multivariate data by modeling each group within a specific functional subspace.

Description: The funHDDC algorithm (Schmutz et al., 2018) allows to cluster functional univariate or multivariate data by modeling each group within a specific functional subspace.

Description: The funHDDC algorithm  allows to cluster functional univariate (Bouveyron and Jacques, 2011, <doi:10.1007/s11634-011-0095-6>) or multivariate data (Schmutz et al., 2018) by modeling each group within a specific functional subspace.

Title: Model-Based Co-Clustering of Functional Data
Author: Charles Bouveyron and Julien Jacques
Depends: fda, parallel, funFEM, R (>= 3.1.0)
Description: The funLBM algorithm allows to simultaneously cluster the rows and the columns of a data matrix where each entry of the matrix is a function or a time series.

Author: Charles Bouveyron, Julien Jacques and Amandine Schmutz
Depends: fda, parallel, funFEM,abind,ggplot2, R (>= 3.4.0)

Depends: fda, parallel, funFEM, abind, ggplot2, R (>= 3.4.0)