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

Documentation:

Reference manual:

KFPCA.pdf

Downloads:

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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