This packages proposes a model-based clustering algorithm for multivariate functional data. The parametric mixture model, based on the assumption of normality of the principal components resulting from a multivariate functional PCA, is estimated by an EM-like algorithm. The main advantage of the proposed algorithm is its ability to take into account the dependence among curves.
|Author||Mohamed Soueidatt <email@example.com>, <firstname.lastname@example.org>*.|
|Date of publication||2014-01-15 12:17:19|
|Maintainer||Vincent KUBICKI <email@example.com>|
|License||GPL (>= 2)|
cppMultiData: The C++ code, of this package take to run from the data two...
cppUniData: The C++ code, of this package take to run from the data two...
funclust: funclust, clustering multivariate functional data
Funclustering-package: A package for functional data clustering.
harmsCut: Separates the matrices of the coefficients of harmonics
Input-class: Constructor of Input class
mfpca: Multivariate functional pca
mfpcaPlot: Plot multivariate functional pca
Output-class: Constructor of Output class
plotfd: plot a functional data object
plotOC: plot Original Curves