Description Author(s) References
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.
Thomas Maierhofer Florian Pfisterer
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.
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