KFPCA: Kendall Functional Principal Component Analysis

Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.48550/arXiv.2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.

Version: 2.0
Depends: R (≥ 2.10)
Imports: kader, utils, pracma, fdapace, fda, stats, graphics
Published: 2022-02-04
DOI: 10.32614/CRAN.package.KFPCA
Author: Rou Zhong [aut, cre], Jingxiao Zhang [aut]
Maintainer: Rou Zhong <zhong_rou at 163.com>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: KFPCA results


Reference manual: KFPCA.pdf


Package source: KFPCA_2.0.tar.gz
Windows binaries: r-devel: KFPCA_2.0.zip, r-release: KFPCA_2.0.zip, r-oldrel: KFPCA_2.0.zip
macOS binaries: r-release (arm64): KFPCA_2.0.tgz, r-oldrel (arm64): KFPCA_2.0.tgz, r-release (x86_64): KFPCA_2.0.tgz, r-oldrel (x86_64): KFPCA_2.0.tgz
Old sources: KFPCA archive


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