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fdaconcur (version 0.1.3)

Concurrent Regression and History Index Models for Functional Data

Description

Provides an implementation of concurrent or varying coefficient regression methods for functional data. The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data. References: Yao, F., Müller, H.G., Wang, J.L. (2005) . Sentürk, D., Müller, H.G. (2010) .

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install.packages('fdaconcur')

Monthly Downloads

209

Version

0.1.3

License

BSD_3_clause + file LICENSE

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Maintainer

Su I Iao

Last Published

July 20th, 2024

Functions in fdaconcur (0.1.3)

historyIndexSparse

Functional History Index Model
GetCI_Dense

Bootstrap pointwise confidence intervals for the coefficient functions in functional concurrent regression for densely observed data.
ptFCReg

Functional concurrent regression using pointwise multiple linear regression.
smPtFCRegCoef

Smooth the concurrent effects functions in a ptFCReg object using local linear regression. The local linear regression is implemented using the function Lwls1D.
historyIndexDense

Functional History Index Model
fdaconcur

fdaconcur: Concurrent Regression and History Index Models for Functional Data
fitted_ptFCReg

Fitted functional responses from a ptFCReg object.
ConcurReg

Functional Concurrent Regression using 2D smoothing
GetCI_Sparse

Bootstrap pointwise confidence intervals for the coefficient functions in functional concurrent regression for sparsely observed data.
ConcReg_Lag

Functional Concurrent Regression with Lag Model