Hierarchical clustering will soon be available as well. References:
Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean
alignment for curve clustering" <doi:10.1016/j.csda.2009.12.008>.

Title: Concurrent Regression and History Index Models for Functional
Description: Provides implementation of concurrent or varying coefficient regression methods for
functional data. The implementations are done for both dense and sparsely observed functional
data. Pointwise confidence bands can be constructed for each case. Further, the influence of
past predictor values is modeled by a smooth history index function,
while the effects on the response are described by smooth varying coefficient functions,
which are very useful in analyzing real data such as COVID data.
Suggests: MASS, Rcpp (>= 0.11.5), Matrix, pracma, numDeriv, testthat
Author: Satarupa Bhattacharjee [aut, cre],

Title: Functional Data Analysis for Density Functions by Transformation
Author: A. Petersen, P. Z. Hadjipantelis and H.G. Mueller
Description: An implementation of the methodology described in
Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional
data analysis of samples of density functions.  Densities are first
transformed to their corresponding log quantile densities, followed by
ordinary Functional Principal Components Analysis (FPCA).  Transformation
modes of variation yield improved interpretation of the variability in the
data as compared to FPCA on the densities themselves.  The standard
fraction of variance explained (FVE) criterion commonly used for functional
data is adapted to the transformation setting, also allowing for an
alternative quantification of variability for density data through the
Wasserstein metric of optimal transport.
Packaged: 2017-10-16 17:26:41 UTC; alexanderpetersen

Packaged: 2018-02-08 21:34:26 UTC; alexanderpetersen

Packaged: 2019-12-04 18:09:17 UTC; alexanderpetersen

Title: Clustering and alignment of a functional dataset
Author: Mirco Patriarca, Laura Sangalli, Piercesare Secchi, Simone Vantini, Valeria Vitelli.
Description: The package fdakma jointly performs clustering and alignment of a multidimensional or unidimensional functional dataset.
Packaged: 2013-07-30 07:55:15 UTC; mircopatriarca