FADPclust: Functional Data Clustering Using Adaptive Density Peak Detection

An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).

Version: 1.1.1
Depends: R (≥ 3.5.0)
Imports: MFPCA, cluster, fpc, fda, fda.usc, funData, stats, graphics
Published: 2022-11-07
Author: Rui Ren [aut, cre], Kuangnan Fang [aut], Qingzhao Zhang [aut], Xiaofeng Wang [aut]
Maintainer: Rui Ren <xmurr at stu.xmu.edu.cn>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: FADPclust results

Documentation:

Reference manual: FADPclust.pdf

Downloads:

Package source: FADPclust_1.1.1.tar.gz
Windows binaries: r-devel: FADPclust_1.1.1.zip, r-release: FADPclust_1.1.1.zip, r-oldrel: FADPclust_1.1.1.zip
macOS binaries: r-release (arm64): FADPclust_1.1.1.tgz, r-oldrel (arm64): FADPclust_1.1.1.tgz, r-release (x86_64): FADPclust_1.1.1.tgz
Old sources: FADPclust archive

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