This package implements methods for functional data
classification. The main functions of this package are
classiKnn, a k nearest neighbor estimator for functional data,
and classiKernel, a kernel estimator
for functional data. The package uses efficiently implemented semimetrics to
create the distance matrix of the functional observations in the function
computeDistMat.
Currently supported distance measures are all methods implemented in
dist
and all semimetrics suggested in Fuchs et al. (2015).
Additionally, all (semi-)metrics can be used on a derivative of arbitrary
order of the functional observations.
This is a new package, please report all bugs and issues at
https://github.com/maierhofert/classiFunc.
Fuchs, K., J. Gertheiss, and G. Tutz (2015): Nearest neighbor ensembles for functional data with interpretable feature selection. Chemometrics and Intelligent Laboratory Systems 146, 186 - 197.