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

Univariate and Multivariate Model-Based Clustering in
Group-Specific Functional Subspaces
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.

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

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

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.

Model-Based Co-Clustering of Functional Data
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.

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

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

Learning Data Cleaning, Visual Analysis and Model Performance
Learn data cleaning, visual data analysis and model performance assessment (KS, AUC and ROC), package core is in the vignette documentation explaining last topics as a tutorial.
ROCR, ggplot2, gridExtra, pander, plyr, reshape2, scales

Title: Learn Data Science Through the "Data Science Live Book"
Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book <http://livebook.datascienceheroes.com/> are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
ROCR, ggplot2, gridExtra, pander, reshape2, scales, dplyr,
lazyeval, utils