classiFunc: The classiFunc package

In maierhofert/classiFunc: Classification of Functional Data

Description

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.

Author(s)

Thomas Maierhofer Florian Pfisterer

References

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.

maierhofert/classiFunc documentation built on May 21, 2019, 11:06 a.m.