Classification of Functional Data

Efficient implementation of k-nearest neighbor estimation and kernel estimation for functional data classification.

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The classiFunc 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.

Using classiFunc

For installation instructions, see below. A hands on introduction to can be found in the vignette. Details on specific functions are in the reference manual.

Issues & Feature Requests

For issues, bugs, feature requests etc. please use the Github Issues. Input is always welcome.


You can install the current classiFunc version from CRAN with:


or the latest patched version from Github with:

# install.packages("devtools")


Reference manual

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0.1.1 by Thomas Maierhofer, a year ago

Browse source code at

Authors: Thomas Maierhofer [aut, cre] , Karen Fuchs [ctb] , Florian Pfisterer [aut]

Documentation:   PDF Manual  

Task views: Functional Data Analysis

GPL-3 license

Imports BBmisc, checkmate, dtw, fda, fda.usc, fdasrvf, proxy, rucrdtw, stats, zoo

Suggests testthat, knitr, rmarkdown, parallelMap

See at CRAN