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KFPCA (version 2.0)

Kendall Functional Principal Component Analysis

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) .

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Version

Install

install.packages('KFPCA')

Monthly Downloads

155

Version

2.0

License

GPL (>= 3)

Maintainer

Rou Zhong

Last Published

February 4th, 2022

Functions in KFPCA (2.0)

KFPCA_reg

Kendall Functional Principal Component Analysis (KFPCA) for dense and regular design
CD4

CD4 cell counts
GenDataKL

Generate functional/longitudinal data via KL expansion
SparsePlot

Sparse plot
MeanEst

Local linear estimates of mean function
GetGCVbw1D

Bandwidth selection through GCV for one-dimension cases
kernfun

Kernel Functions
GetGCVbw2D

Bandwidth selection through GCV for two-dimension cases
FPCscoreLSE

Least square estimates of functional principal component scores
KFPCA

Kendall Functional Principal Component Analysis (KFPCA) for sparse design
predict.KFPCA

Predict FPC scores