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KFPCA  

Kendall Functional Principal Component Analysis
View on CRAN: Click here


Download and install KFPCA package within the R console
Install from CRAN:
install.packages("KFPCA")

Install from Github:
library("remotes")
install_github("cran/KFPCA")

Install by package version:
library("remotes")
install_version("KFPCA", "2.0")



Attach the package and use:
library("KFPCA")
Maintained by
Rou Zhong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-11
Latest Update: 2022-02-04
Description:
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) . Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) .
How to cite:
Rou Zhong (2021). KFPCA: Kendall Functional Principal Component Analysis. R package version 2.0, https://cran.r-project.org/web/packages/KFPCA. Accessed 16 Jul. 2026.
Previous versions and publish date:
1.0 (2021-02-11 16:30), (2026-07-09 08:07)
Other packages that cited KFPCA R package
View KFPCA citation profile
Other R packages that KFPCA depends, imports, suggests or enhances
Complete documentation for KFPCA
Functions, R codes and Examples using the KFPCA R package
Some associated functions: CD4 . FPCscoreLSE . GenDataKL . GetGCVbw1D . GetGCVbw2D . KFPCA . KFPCA_reg . MeanEst . SparsePlot . kernfun . predict.KFPCA . 
Some associated R codes: CD4.R . FPCscoreLSE.R . GCVbw1D.R . GCVbw2D.R . GenDataKL.R . GetGCVbw1D.R . GetGCVbw2D.R . GetMatList.R . KFPCA.R . KFPCA_reg.R . MeanEst.R . MeanObsGrid.R . SparsePlot.R . basicfun.R . kernfun.R . predict.KFPCA.R .  Full KFPCA package functions and examples
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